The EDHEC European ETF Survey 2009 - EDHEC-Risk Institute · 2018. 7. 30. · 6 An EDHEC Risk and...

The EDHEC European ETF Survey 2009

May 2009

An EDHEC Risk and Asset Management Research Centre Publication

with the support of

Table of Contents

Foreword .......................................................................................................................................3

Executive Summary ...................................................................................................................5

1. Introduction ...............................................................................................................15

2. Background ........................................................................................................................... 19 2.1. Overview of Exchange-Traded Funds ........................................................................................................20 2.2. Asset Allocation with ETFs ............................................................................................................................34

3. Methodology and Data ..................................................................................................... 57 3.1. Methodology ......................................................................................................................................................58 3.2. Data .......................................................................................................................................................................58

4. Results .................................................................................................................................... 61 4.1. Exchange-Traded Funds in Practice ............................................................................................................62 4.2. The Pros and Cons of ETFs, Futures, Total Return Swaps, and Index Funds ...................................66 4.3. The Role of ETFs in the Asset Allocation Process ....................................................................................73 4.4. Trends: Use of and Satisfaction with ETFs over Time ............................................................................77 5. Conclusion ............................................................................................................................ 81

References.................................................................................................................................. 85

About the EDHEC Risk and Asset Management Research Centre ............................ 90

EDHEC Position Papers and Publications ........................................................................... 94

About CASAM ETF ....................................................................................................................100

Printed in France, May 2009. Copyright EDHEC 2009.The opinions expressed in this survey are those of the authors and do not necessarily reflect those of EDHEC Business School and CASAM ETF.

3An EDHEC Risk and Asset Management Research Centre Publication

The EDHEC European ETF Survey 2009 - May 2009

This survey is the first piece of work done as part of the Crédit Agricole Structured Asset Management (CASAM) "Core-Satellite and ETF Investment" research chair. The objective of this research chair at the EDHEC Risk and Asset Management Research Centre is to provide research insights into ETFs (exchange-traded funds) and the ways they are used in core-satellite asset management.

In the past decade, ETFs have stood out for their fast growth. In addition, they are one of the few recent financial innovations that seem to be weathering the current crisis without undue pain; they are even in demand. For the EDHEC Risk and Asset Management Research Centre, it is only natural to devote significant resources to research into ETFs; after all, both indexing and dynamic asset allocation are a main focus of our asset management research. ETFs, then, are at the heart of our concerns.

The current survey is divided into two parts. The first part is an overview of the ETF market and of the mechanisms behind ETFs. It then goes on to show how advanced techniques involving dynamic allocation strategies can be carried out with ETFs. In particular, this part shows how ETFs can be used to implement the core-satellite approach to investment.

The second part presents the results of our in-depth pan-European survey of the current use of ETFs. In general, the results suggest that European investors make wide use of ETFs and consider them superior to other indexing vehicles. However, we also find that investors seem to be somewhat wary of ETFs in such illiquid asset classes as hedge funds, real estate and corporate bonds. And it seems that ETFs are predominantly used for broad market exposure over long time horizons, a use that is at odds with the plentiful opportunities they provide for strategies that are dynamic and/or apply to specific market segments.

We would particularly like to thank our partners at CASAM for their support of our research. We would also like to express our appreciation to Daniel Mantilla and Lionel Martellini for helpful discussions and comments, as well as the publishing team for the production of this survey.

Foreword

Noël AmencProfessor of FinanceDirector of the EDHEC Risk and Asset Management Research Centre

4 An EDHEC Risk and Asset Management Research Centre Publication


About the authors

Felix Goltz is head of applied research at the EDHEC Risk and Asset Management Research Centre. He does research in empirical finance and asset allocation, with a focus on alternative investments and indexing strategies. His work has appeared in various international academic and practitioner journals and handbooks. He obtained a PhD in finance from the University of Nice Sophia-Antipolis after studying economics and business administration at the University of Bayreuth and EDHEC Business School.

Noël Amenc is professor of finance and director of research and development at EDHEC Business School, where he heads the EDHEC Risk and Asset Management Research Centre. He has a masters in economics and a PhD in finance and has conducted active research in the fields of quantitative equity management, portfolio performance analysis, and active asset allocation, resulting in numerous academic and practitioner articles and books. He is a member of the editorial board of the Journal of Portfolio Management, associate editor of the Journal of Alternative Investments and a member of the scientific advisory council of the AMF (French financial regulatory authority).

Adina Grigoriu is head of asset allocation at AM International Consulting, where she advises asset managers on constructing their hedge fund of fund portfolios as well as dynamic core-satellite portfolios. She has an actuarial degree and extensive experience in different finance fields, including quantitative modeling. She started her career as a derivatives trader. She then joined a multinational asset management company where she held several positions, ranging from product manager to fund manager and head of ALM.

David Schröder is a senior research engineer at the EDHEC Risk and Asset Management Research Centre. David obtained his PhD in economics from the University of Bonn. During his doctoral studies, he was also affiliated with the Centre de Recherche en Economie et Statistique (CREST) in Paris. His research focuses on empirical asset pricing, the predictive power of equity analysts’ forecasts, and decision making under ambiguity. He is a member of the Econometric Society and has presented at many international economic and finance conferences.

Executive Summary

5An EDHEC Risk and Asset Management Research Centre Publ icat ion



Exchange-traded funds (ETFs) are perhaps one of the greatest financial innovations of the last decade. ETFs are investment vehicles that usually track an index. Unlike the units of conventional index funds, ETF units trade on stock exchanges at market-determined prices, thereby combining the positive aspects of mutual funds and common stocks. Although the first European ETF was made available only in 2000, assets under management of ETFs amounted to around EUR 100 billion by the end of 2008. In less than ten years, ETFs became a serious alternative to other financial products, such as futures, that provide exposure to financial indices in a single trade. And the ETF market is still growing: the aim of the first ETFs was to replicate the performance of broad equity market indices, but newer products venture into more exotic markets and asset classes, such as emerging markets and alternative investments. Remarkably, the financial crisis has not stunted this growth: ETF providers launched new ETFs in the first months of 2009.

As the market is maturing, it is essential for ETF investors to be informed of the views and practices of their peers. This survey intends to contribute to this understanding by analysing the current and potential uses of ETFs, as well as European investors’ perceptions of ETFs. The EDHEC European ETF Survey 2009 was taken with the aid of an online questionnaire and email sent to European asset management professionals. For a read on the possibly diverging views and opinions across the industry, this survey targeted institutional investors as well as asset management firms and private wealth managers.

In total, we received 360 responses from European ETF users, providing us with a

survey that is largely representative of European ETF investors. The aim is to provide insight into the current uses and views of ETF products. As ETFs are also a very convenient means of improving asset allocation, this document also illustrates new ways of using ETFs for asset allocation. In particular, we show how ETFs may be used in the context of the dynamic core-satellite investment approach, a new risk-budgeting technique. ETFs are a natural for core-satellite investments as they are easily traded in dynamic strategies and provide exposure to a wide range of asset classes and subclasses. This study shows how investors can benefit from the potential outperformance of a satellite portfolio all while keeping risk in check. Risk control can be implemented in a customised fashion, since the approach allows various definitions of investors’ risk budgets. We will now present some of the possible applications of ETFs for dynamic risk budgeting. After having shown these possible uses of ETFs, we will provide an overview of the current uses of ETFs in the industry.

New Risk-Budgeting Techniques: Dynamic Asset Allocation Based on ETFs

The Core-Satellite ApproachWe provide various illustrations of how ETFs can be used in the dynamic core-satellite approach. The core-satellite approach allows investors to separate their portfolio management into the construction of a strategic benchmark and the generation of outperformance of that benchmark. The benchmark is constructed in the core and outperformance is sought in the satellite. Core portfolios may be made up of an allocation to broad market indices or indices

Executive Summary



for specific segments. Satellite portfolios may try to generate outperformance by deviating from the core portfolio, either with exposure to other asset classes or segments or with exposure to actively managed funds. Indexing vehicles such as ETFs may thus be used in both the core and the satellite.

The separation of portfolio management into benchmark construction and outperformance generation allows investors to structure their portfolio management tasks in a coherent manner. Moreover, it allows straightforward tracking error management. Since the core portfolio is by definition free of any tracking error with respect to the benchmark, the satellite portfolio is the only source of tracking error. The tracking error of the overall portfolio is thus determined by the weights attributed to the core and the satellite. By fixing the weights of a satellite with a tracking error of 20%, for example, at 10%, 20%, or 30%, the tracking error of the overall portfolio is 2%, 4%, or 6%.

However, such static allocation, in which the proportions invested in the core and the satellite are constant, does not distinguish between “bad” (underperformance of the benchmark) and “good” tracking error (outperformance of the benchmark). The full potential of core-satellite portfolio management is reached only when the allocation to the core and to the satellite is adjusted dynamically. The dynamic core-satellite approach of Amenc et al. (2004) is a method for such asymmetric management of tracking error; the idea is to benefit from outperformance of the satellite while limiting the risk of downside tracking error.

Exhibit 1: Allocation to core and satellite allows control of tracking error risk

Core SatelliteDefines the investor’s long-term choices in terms of risk/return profile through

• exposure to standard commercial indices• improved allocation to sub-segments (such as sectors, styles, and so on) or asset classes• improved allocation to constituents of commercial indices (new forms of indexing)

Static core-satellite approach Symmetric management of tracking error by fixing allocation to

the core and satellite

Dynamic core-satellite approach Asymmetric management of tracking error by using a strategy to limit the underperformance of the core while benefiting from the

upside potential of the satellite

Seeks to outperform the core while inducing tracking error through

• exposure to additional risk premia • abnormal returns (alpha) obtained by an active strategy

Dynamic Core-Satellite Portfolio ManagementThe dynamic core-satellite approach is a new concept that builds on the principle of constant proportion portfolio insurance (CPPI). This procedure enables asymmetric return profiles through systematic dynamic allocation rules. Dynamic core-satellite management extends this approach to asymmetric tracking error management. Investors will thus be certain that underperformance of the benchmark will be capped. Dynamic shifts in allocation are made not to a risky and a risk-free asset in the absolute sense but to the satellite portfolio and the core portfolio. The core is of course free of tracking error risk; the satellite is not.

Exhibit 2: This table compares the traditional CPPI and the relative CPPI approaches

Traditional CPPI Relative CPPI Approach

Risky asset Satellite portfolio

Risk-free asset Core portfolio

The advantage of the dynamic core-satellite approach is that it allows an investor to truncate the relative return distribution

Executive Summary



so as to shift the probability weights from severe relative underperformance to more potential for outperformance.

In the dynamic core-satellite approach, a floor determines the investor’s risk budget. If the difference between the floor and the total portfolio value increases, i.e., if the cushion becomes larger, the investor’s risk budget increases and more assets are allocated to the riskier satellite. By contrast, if the cushion becomes smaller—that is, if there is less room in the risk budget—the share of investment in the satellite falls.

In standard dynamic core-satellite investment, this floor is a constant fraction of a given benchmark value; it thereby protects the portfolio from severe relative losses. Depending on the investment purpose, however, different floors might be used to exploit more fully the benefits of core-satellite management. Indeed, core-satellite management can accommodate more complex or even multiple floors.

The most commonly used alternative floors are the capital guarantee floor, the maximum drawdown floor, and the trailing performance floor. The capital guarantee floor, usually used in CPPI, attempts to preserve the initial invested wealth. Maximum drawdown floors are designed to prevent the total portfolio value from falling more than a specified fraction of the highest asset value it has ever attained. So this particular floor is suitable for absolute return funds. Finally, the trailing performance floor is meant to prevent a portfolio from posting negative performance over a trailing horizon (twelve months, for example).

Besides imposing different floors, dynamic core-satellite management can also incorporate so-called investment goals. Instead of imposing a lower limit for the total portfolio value, an investment goal (or cap) restricts the upside potential of the portfolio. Although at first glance they may seem counterintuitive, goal-directed strategies reflect the possible failure of an investor to gain additional utility once some level of wealth is reached. In other words, once this wealth is reached the hope of realising more gains becomes the fear of losing the accumulated wealth. By forgoing this possible extra wealth, investors benefit from a decrease in the cost of downside protection. These investment goals may be constant or they may be a deterministic or stochastic function of time.

We use several examples to illustrate the benefits of the dynamic core-satellite investment strategy. In several of these examples, we take dynamic allocation to a defensive bond core (government bond ETF) and a risky equity satellite (Euro Stoxx 50 ETF). We provide an overview of this set of examples in the next two sections.

Dynamic Core-Satellite Portfolio Management with ETFs: Absolute Return FundsIn the first example, we look at the management of a portfolio with an absolute return objective. Investors interested in absolute return strategies expect market conditions not to have a great negative impact on portfolio performance; they expect drawdown not to be substantial. So in our example we rely on a dynamic core-satellite investment with a maximum drawdown floor. Moreover, we impose a restriction that the twelve-month trailing performance of the portfolio shall not be

Executive Summary



negative and define an investment goal to limit the risky upside potential of the investment. The core consists of a broad government bond ETF, the satellite of a broad equity ETF. Furthermore, to limit overall risk the maximum allocation to the satellite is restricted to 50%.1

Exhibit 3 shows the cumulative returns of this strategy, as well as those of the core and the satellite. In addition, to highlight the built-in protection of this investment strategy, the floor is displayed as well.

Exhibit 3: Absolute return fund: cumulative returns of the core, the satellite and the dynamic core-satellite portfolio

60

80

100

120

140

160

180

200

Core Satellite DCS Floor

12-1

998

12-1

999

12-2

000

12-2

001

12-2

002

12-2

003

12-2

004

12-2

005

12-2

006

12-2

007

12-2

008

We can draw a number of conclusions from this figure. The dynamics of the core portfolio confirm the conservative character of the core investment. However, we also see that the performance of the bond core was quite flat for extended periods, such as from 1998 to 2000, or from 2004 to 2006. By contrast, the returns of the equity ETF in the satellite portfolio were negative over the entire period. More importantly, the fluctuations of the large-cap equity ETF in the satellite are tremendous, with a sharp increase in value before 2000 and the steep falls from 2000 to 2002 and in 2008. The dynamic core-satellite arrangement combines the advantages of each of its ingredients: the smooth performance of the bond core and the upside potential of the equity satellite. As a result, performance is smooth over the entire period, and cumulative returns at the

end of the period are actually higher than those of both the core and the satellite. The graph also shows the dynamics of the floor. It is instructive to look at the portfolio performance during the stock market downturns in 2000 and 2008. In fact, the dynamic core-satellite portfolio is little affected. As the portfolio value approaches the floor, the allocation shifts to the core. This shift is also shown in the following graph, which shows the weight of the satellite.

Exhibit 4: Absolute return fund: changes in the allocation to the satellite

12-1

998

12-1

999

12-2

000

12-2

001

12-2

002

12-2

003

12-2

004

12-2

005

12-2

006

12-2

007

12-2

008

Satellite allocation

0%5%

10%15%20%25%30%35%40%45%50%

Allo

catio

n to

the

sate

llite

Controlling the Risk of Tactical Bets with Dynamic Core-Satellite Portfolios of ETFsThe previous example shows that dynamic risk budgeting can provide sound absolute return management and that it is easily implemented with ETFs for traditional asset classes (equity ETFs and bond ETFs). What makes it remarkable is the absence of reliance on forecasts. The systematic allocation based on past values of the bond core and the equity satellite portfolio means that the investor takes on no forecasting risk.

Of course, investment houses may have access to proprietary forecasts that they may wish to use to shift the allocation from the core to the satellite or from the satellite to the core. We test the ability of such a strategy to obtain absolute return properties similar to those of the dynamic

Executive Summary

1 - The bond ETF for the core consists of medium-term government bonds with a maturity of three to five years. The equity satellite is made up of blue-chip European stocks (Euro Stoxx 50). Please refer to the main document (see section 2.2.2) for further explanation of the exact implementation of the example.



core-satellite approach described above. To do so, we run simulations in which we assume that a tactical manager has positive forecasting skill; that is, that he correctly forecasts monthly satellite outperformance seven times out of twelve (his hit ratio, in other words, is at least 7/12). We assume hit ratios from 7/12 to 11/12. Our scenarios also assume that the manager will allocate 0% of the portfolio to the satellite if he thinks the satellite will underperform the core the following month and 50% if he thinks that it will outperform.

The resulting scenarios show that though they obviously have attractive average returns such strategies suffer from severe drawdown. Even with extremely high and clearly unrealistic hit ratios of 11/12 maximum drawdown is considerably higher than in the absolute return portfolio based on the dynamic core-satellite approach we described above.

It is natural to wonder whether it is possible to combine the return potential of forecasting and downside risk management that would mitigate the high maximum drawdown. We thus integrate the active manager’s forecasting ability into the dynamic core-satellite investment concept. Since the main objective is to reduce the drawdown statistics that result from the errors of skilled forecasters, we define the floor so as to impose a maximum drawdown of 10%. If the manager expects the satellite to outperform the core, the multiplier is set to m=5. But if he expects the satellite to underperform, the multiplier is set to m=0. In such situations, the portfolio is fully protected from the expected negative performance of the satellite.

Exhibit 5 shows the percentage by which combining the DCS (dynamic core satellite)framework and forecasts reduces the maximum drawdown found in standard tactical allocation (forecasts alone).

Exhibit 5: Reduction of the maximum drawdown when forecast-based tactical asset allocation is made part of the dynamic-core satellite framework.

Hit ratio of prediction of outperformance of the satellite

7/12 8/12 9/12 10/12 11/12

Risk reduction through DCS (reduction in magnitude of avg. max drawdown) compared to the standard tactical allocation approach

40% 30% 20% 12% 4%

The table clearly shows that risk control leads to significant benefits, especially when the hit ratio is comparatively low. In short, dynamic risk budgeting makes it possible to limit the severe drawdown resulting from forecast errors in the standard tactical allocation strategy. The same forecasting ability was used for both strategies.

The two examples summarised here show that the dynamic core-satellite approach provides a coherent framework with which to structure portfolios that allow investors to gain exposure to the upside potential of certain asset classes and to keep risk, either absolute or relative to a benchmark, under control. It is kept under control by rebalancing the positions in keeping with the investor’s dynamic risk budget. Since ETFs are easily traded, they are a natural medium for such strategies. In addition, the wide range of indices tracked by ETFs makes it possible to further customise the core and the satellite. These examples, then, are some of the possible applications of ETFs. We turn now to the survey responses.

Executive Summary



Current Use of Exchange-Traded Funds by European Investment ProfessionalsIn general, the survey shows that ETFs are very popular and widely used by European investors and asset managers. However, the current use of these products stops short of harnessing their full potential. We summarise the main results of this analysis in the following key conclusions.

1. ETFs Experiencing Strong Ongoing Growth The survey highlights the ongoing growth in the use of ETFs for all asset classes. When the use of ETF and ETF-like products over time is analysed, the tremendous growth of this financial product becomes apparent. Exhibit 6 shows that ETFs for all asset classes gained popularity over the past three years. ETF use in the equity universe has increased from 45% to 95%. ETFs in government bond investments have seen the biggest increase: 80% of respondents reported using them, up from 40% the year before.

Exhibit 6: Use of ETFs and ETF-like products over time

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2006 2008 2009

Equities

Government bonds

Corporate bonds

Commodities

Real estate

Hedge funds

2. ETFs are Used Predominantly for Equity InvestingETFs are still most heavily used for equity investing. Indeed, as exhibit 7 shows, they make up more than a third of the average respondent’s equity investments (and 22% of commodity investments).

The dominance of equity ETFs can be attributed in part to the satisfaction with equity ETFs: 94% of all equity ETF investors report that they are satisfied with their choice of investment.

Exhibit 7: For each asset class, indicate the percentage of total investment accounted for by ETFs or ETF-like products

36%

17%

22%

12%

12%

5%

Commodities

Real estate

Hedge funds

Corporate bonds

Government bonds

Equities

0 5 10 15 20 25 30 35 40

3. Dominance of Broad Market Indices and Static StrategiesExhibit 8 shows how frequently survey respondents use ETFs for various purposes. It is clear that ETFs for broad market coverage are more frequently used than ETFs for specific sub-segments. Likewise, long-term buy-and-hold investment is more frequent than short-term (tactical) investment. These results are surprising; after all, two of the clear advantages of ETFs are that they offer investment media for tracking particular sub-segments of the market and they are a liquid and easily traded instrument.

Exhibit 8: How often do you use ETFs for the following purposes?

RarelyFrequently

Specific sub-segment exposure (sector, style)

Management of cash flows (e.g., cash equitisation)

Dynamic portfolio insurance strategies (e.g., CPPI)

Neutralisation of factor exposures of other investments

Tactical bets

Broad market exposure

Short-term/dynamic investment

Long-term/buy-and-holdinvestment

0 20 40 60 80 100

12%

11%

42%

15%

47%

68%

49%

57%

Executive Summary



4. ETFs for Alternative Assets Face ChallengesETF products for the alternative investment universe are currently up against challenges, probably as a result of the current financial and economic crisis. Although ETFs are still gaining market share in the alternative asset class, growing dissatisfaction suggests that these gains may be hard to sustain. As exhibit 9 shows, satisfaction with real estate and hedge fund ETFs has fallen in 2009. In fact, dissatisfaction with hedge fund ETFs has resulted in a fall from 7% to 5% in the share of total hedge fund investment accounted for by ETFs.

Exhibit 9: Satisfaction with ETFs and ETF-like products over time

20%

30%

40%

50%

60%

70%

80%

90%

100%

2006 2008 2009

Equities

Government bonds

Corporate bonds

Commodities

Real estate

Hedge funds

5. Advanced Features of ETFs are Little UsedAdvanced uses of ETFs, such as securities lending, trading options on ETFs, and short selling ETFs are used by only a small fraction of respondents (less than 15%). Inverse-performance ETFs, by contrast, are used by almost 30% of all respondents (see exhibit 10).2 As our survey shows, the failure to use advanced ETF products and advanced ETF trading strategies is not a result of unawareness of such techniques: as it happens, fewer than 10% of all ETF users report that they are unaware of them. Although they are not yet widely used, the survey shows that these features are twice as likely to be used as they were

twelve months earlier. It is likely that this increase will continue.

Exhibit 10: Advanced uses and forms of ETFs

0%

5%

10%

15%

20%

25%

30%

2006 2008 2009

Use of inverse ETFs

Options on ETFs

Shorting ETFs

Lending ETFs

6. ETFs and Futures are the Preferred Indexing InstrumentsWe ask survey respondents to rate ETFs, futures, total return swaps, and index funds on several criteria. The responses indicate that in terms of liquidity, transparency, and cost ETFs are considered advantageous, although on some criteria they are less well regarded than futures. Next, ETFs are ranked highest for the range of indices and asset classes they make available. Futures are perhaps the most serious rivalto ETFs, but ETFs are preferred for their lower minimum subscription, fewer operational constraints, and the friendlier tax and regulatory regimes to which they are subject. Implementation concerns (margin calls, applying exact allocations even for small portfolios) give ETFs an advantage over futures.

7. Future DevelopmentsEmerging market equity ETFs (47%) are the ETF product respondents would most like to see developed (exhibit 11). About a third of the respondents would also like to see products developed in alternative asset classes, especially in commodities (35%), currencies (30%), and hedge funds (28%).

Executive Summary

2 - We have no numbers for 2006 as these products were launched only after 2006.



Exhibit 11: What type of ETF products would you like to see developed further in the future?

0 10 20 30 40 50

18%

14%

31%

25%

34%

35%

30%

21%

28%

29%

47%

Ethical ETFsActively managed equity ETFs

High yield bond ETFsEquity style ETFs

ETFs based on new forms of indices

Commodity ETFs

Currency ETFs

Real estate ETFs

Hedge-fund-like ETFs

Emerging market bond ETFs

Emerging market equity ETFs

ConclusionThe objective of the EDHEC European ETF Survey 2009 is to provide insight into current and potential uses of ETFs. To reach this objective, we analyse the uses to which European investors and asset managers put ETFs as well as their perceptions of these instruments. We also provide an introduction to dynamic core-satellite portfolio management and show how investors can structure risk-controlled portfolios of ETFs to meet particular risk objectives.

Taken together, our illustrations of dynamic core-satellite portfolios highlight the potential benefits of combining the tradability of ETFs and dynamic risk management. Although core-satellite investments can be static, the illustrations show that their full potential is reached when the allocation to the satellite and the core is adjusted dynamically. The main benefit of this dynamic adjustment is that it allows investors to take advantage of rising markets and, at the same time, limits their risk. As a result, dynamic core-satellite strategies often offer a better risk/return trade-off than core or satellite investments. In addition, maximum drawdown, i.e., extreme risk, is limited.

The uses to which European investors put ETFs provide a few interesting insights. First, ETFs are now widely used and practitioners are highly satisfied with their features. Indeed, ETFs are now the preferred indexing instrument. They are considered to have an edge over total return swaps and traditional index funds for their liquidity, transparency, and cost; futures are a more serious challenger. However, respondents appreciate the wide range of products and asset classes made accessible by ETFs (futures do not provide access to quite such a wide range). Second, in some specific market segments, ETFs are currently dealing with challenges. Indeed, dissatisfaction with ETF products for corporate bonds, real estate markets, and hedge funds is growing (probably as a result of the current financial crisis). In these asset classes, the combination of adverse market conditions and relatively illiquid underlying assets presumably poses problems for ETF product providers. Third, our survey shows that ETFs are not yet used to their full potential. For example, the use of ETFs is largely limited to passive holdings of broad market indices. In fact, more than two-thirds of all ETF users report that they frequently use ETFs to obtain broad market exposure; and for more than 50% ETFs are predominantly long-term or buy-and-hold investments. Fewer than 50% of respondents report that they frequently rely on ETFs for short-term investment or for exposure to specific market sub-segments. Nor do most practitioners trade options on ETFs, sell ETFs short, or lend them out, although they are more likely to do so than in years past.

Executive Summary



Executive Summary

1. Introduction




Exchange-traded funds (ETFs), investment vehicles that track a given index or benchmark, are perhaps one of the greatest financial innovations of recent years. Unlike conventional index funds, however, ETF units trade on stock exchanges at market-determined prices, thereby combining the advantages of mutual funds and common stocks.

Although the first European ETF came on the market only in 2000, assets under management of ETFs amounted to USD 143 billion as of late December 2008 (Fuhr 2009). In less than ten years, ETFs have become a serious alternative to other financial products, such as futures or index funds, that allow participation in broad market movements. In addition, ETFs are one of the few products that seem not to have been hit by the financial crisis. European mutual funds suffered outflows of USD 570 billion over the course of 2008; ETFs, by contrast, collected USD 74 billion (Fuhr 2009).

And the ETF market is still growing: whereas the first ETFs attempted to replicate the performance of broad equity markets, newer products are venturing into more exotic markets and asset classes, such as emerging markets and alternative investments, real estate, infrastructure, private equity, and even hedge funds. As the market is maturing, being aware of the views and practices of ETF investors is essential. The aim of this survey is to contribute to this awareness by analysing the current uses and perceptions of ETFs in Europe. It is our hope that this survey will provide insight into how ETFs could further benefit investors. Finally, as it compares results with the results of past EDHEC ETF surveys, this survey sheds light on developments in the ETF market in the last few years.

In addition to their intrinsic appeal, ETFs provide a very convenient means of improving asset allocation in portfolio management. Indeed, this survey also illustrates how ETFs can be used in conjunction with core-satellite portfolio construction. This dynamic risk-budgeting technique, which has attracted growing investor interest in recent years, splits the portfolio into a core, which fully replicates the investor’s specifically designated benchmark, and a return-generating satellite (or satellites), which is allowed a higher tracking error. This survey demonstrates the main benefits of this asset allocation strategy: benefiting from the upside while keeping overall risk low.

The EDHEC European ETF survey 2009 was taken with an online questionnaire and e-mails to European professionals in the asset management industry. For a read on the possibly diverging views and opinions across the industry, this survey targeted institutional investors as well as asset management firms and private wealth managers. The questionnaire consists of sections covering the role played by ETFs in the survey respondents’ asset allocation decisions, practical aspects of ETF investments, as well as the applications of ETFs for portfolio construction. In addition, the questionnaire asks the participants to compare ETFs and other investment instruments that can be considered close substitutes: total return swaps, futures, and index funds. Finally, we invited survey respondents to express their views on future developments in the ETF market.

We find that European investors rely heavily on ETFs for asset allocation. In addition, satisfaction with standard equity ETFs, as high as 94%, is tremendous. However, the use of ETFs is largely limited to passive

1. Introduction



holdings of broad market indices. The wide range of ETFs for sub-segments and styles is not used to its full potential. Likewise, most practitioners do not benefit from the possibilities of trading options on ETFs, selling ETFs short, or lending them out. On the other hand, inverse ETFs are gaining popularity among ETF investors. Almost 30% of European ETF investors report using such products. ETFs are also being increasingly used for exposure to alternative asset classes: survey respondents report that ETFs account for 22% of their commodity investments. This is a considerable fraction, especially compared to the much more popular equity ETFs, which make up 36% of all the respondents’ equity investments. All the same, as they are not immune to the current financial and economic crisis, ETF products for the alternative investment universe are currently facing difficulties. Although they are gaining importance within their asset class, lower reported satisfaction may presage problems. Dissatisfaction with ETFs for hedge fund investment, for example, has already led to a decrease in the share of total hedge fund investment accounted for by ETFs.

ETFs and futures are the preferred indexing instruments. Most of all, ETFs are perceived to have an edge over other products in terms of liquidity, transparency, and cost. Respondents also appreciate the wide range of products and asset classes available with ETFs. So it would come as no surprise if ETFs and futures were to take market share from index funds and total return swaps.

This survey proceeds as follows. In the next section, we review the European ETF market and explain this financial product in more detail. We then illustrate how ETFs can be used in core-satellite investment. The method used to take the survey is described

in section 3. European investors’ views of ETFs, the uses of ETFs, and comparisons of ETFs and other indexing products are presented in section 4. Section 5 draws conclusions from the survey results.

1. Introduction



1. Introduction

2. Background




2.1. Overview of Exchange-Traded FundsAn exchange-traded fund (ETF) is an investment vehicle whose shares (units) are traded on stock exchanges at market-determined prices. Most ETFs are index funds that consist of a basket of securities and attempt to track, as closely as possible, the performance of market indices or benchmarks. Unlike traditional index funds, however, they are distinct investment vehicles with stock-like characteristics: they are listed on exchange and, like normal stocks, they can be bought and sold at market prices during trading hours. They can also be traded in the same ways as other stocks are traded (they can be bought on margin, sold short, and so on). As such, an ETF is a hybrid of a stock and a fund. With these new instruments, investors can gain broad exposure to equity, fixed income, and alternative asset markets with much less effort and at lower cost than they could before. ETFs, then, are regarded as one of most innovative financial instruments of modern times. They have been praised both by academics and by industry practitioners.

2.1.1. The European ETF MarketLaunched on the Toronto Stock Exchange (TSX) in 1990, ETFs have since flourished worldwide. In Europe, the first ETF appeared in April 2000 and was issued by Merrill Lynch to track the Euro Stoxx 50. Although the European ETF industry started late and the US accounts for over 67% of the global ETF market (Fuhr 2009), Europe is catching up. Worlwide, ETF assets fell by nearly 20% over the past twelve months as a result of the financial crisis, but European ETF assets fell by less than 10%.

Growth of the European ETF marketExhibit 2.1 shows that the European ETF market has expanded enormously since its 2000 kick-off. Assets under management had risen to USD 143 billion by the end of December 2008. 632 ETFs were available, almost three times the 273 available just two years earlier. Fixed-income ETFs grew faster than any other ETF product segment; they grew by 250% in less than two years, to EUR 31.7bn as of late February 2009 (Deutsche Bank 2009). And the growth—at least in the number of products—is expected to continue; 107 ETF launches were planned as of late February 2009 (Fuhr 2009).

Exhibit 2.1: European ETF market growth

Assets under management in bn USDNumber of ETF products

0

30

2000

2001

2002

2003

2004

2005

2006

2007

2008

60

90

120

150

100

0

200

300

400

500

600

700

Assets under management

Number ofETF products

Source: Fuhr (2009), data as of February 2009

Market segmentationAccording to Fuhr (2009), as of February 2009, nearly as many ETFs were being offered in Europe (643) as in the United States (694). Taken together, equity ETFs account for the majority of ETFs; more than 60% of all ETF assets are invested

2. Background



in this asset class (see exhibit 2.2). ETFs tracking national indices are the most popular equity ETFs: seventy-one ETFs are pegged to domestic indices; they account for 18% of the European ETF market. With 14% of the market, those covering regional euro-zone indices rank second. Another 10% are international equity ETFs. Fixed-income ETFs are also popular; they account for 30% of the total European ETF market. Commodity ETFs are in third place; there are twenty-seven such ETFs and they manage USD 6.4bn of assets, 5% of the European ETF market.

Exhibit 2.2: Types of European ETFs by assets under management

Fixed incomeCountry exposure Europe — equityRegional exposure euro-zone — equityInternational/emerging markets — equityCountry exposure United States — equityCommoditiesRegional exposure Europe — equityEuropean sector exposure — equityGlobal exposure — equity Style — equityInverseEuro-zone sector exposure — equityOther

30%

18%

10%

14%

6%

5%

4%

3%4%

2%2%1%1%

Source: Fuhr (2009), data as of February 2009

2.1.2. Understanding Exchange-Traded Funds As a combination of an index fund and a stock, an ETF should be analysed from both standpoints. Like a traditional index fund, an ETF usually attempts to track or replicate a particular index of equities, debts or other securities, a broad sector, or a group of international stocks. Like mutual funds, ETFs are registered as open-end funds, continuously offering new fund shares to the public and required to buy back outstanding shares upon a shareholder's request and at a price based on the current value of the fund's net assets. Although the designs of ETFs and mutual funds are similar, investors can treat ETFs as normal stocks, buying or selling ETF shares through a broker or in a brokerage account, just as they would buy the shares of any publicly traded company.1

Physical replication ETFs, statistical replication ETFs, and swap-based ETFsAn ETF’s replication mechanism is one of its defining features. Indeed, ETFs come in three flavours: physical index replication funds, statistical replication, and swap-based replication. An ETF is considered a physical replicating index fund (sometimes also cash-based replication) if the ETF manager holds all the constituents of the underlying index in the same proportion as the constituent securities of the index. This approach offers a natural replication of the target index. The main advantage of this form of replication is its straightforwardness. The drawback is that it is difficult to implement when the index to be replicated is a broad index with a large number of securities. This difficulty arises from liquidity problems with index constituents, clearing and settlement

2. Background

1 - See Brunnermeier et al. (2008) or Jurek (2007) for an analysis of these strategies.



problems, and the high cost of executing the basket of securities. So, even physical replication will lead to tracking error.

ETFs can also use statistical sampling strategies to replicate the chosen index. Instead of fully replicating the index, the fund invests in only a fraction of the total index constituents. The aim is to replicate the index by focusing on highly liquid underlyings. This form is generally used for very broad indices, where it is less costly than full replication. But it necessarily leads to tracking error, the magnitude of which depends on the accuracy of the statistical replication model. In addition, statistical replication may lead to counterparty credit risk. Counterparty risk arises when the manager of the ETF tries to enhance performance by lending or selling securities to buy financial products that reduce the tax burden.

Finally, there are swap-based ETFs. The basic idea of these funds is to outsource the tracking error management to a counterparty: the fund itself invests in a broad basket that does not fully replicate the index. The replication itself is provided by the swap counterparty, and any differences in the performance of the basket and that of the index are counterbalanced by the swap payments. The advantages of this third replication technique are that the ETF does not bear tracking error risk and that swap-based replication comes at low cost. However, it leads to counterparty risk. This risk, however, arises only when the index outperforms the basket of securities, since this difference obliges the counterparty to make cash payments. In addition, this risk is limited to 10% of the fund’s value and before reaching this limit of 10%

the swap position will reset. This limit is imposed by UCITS III regulation in the European Union. ETFs also deal with a single swap counterparty, the identity of which is known to investors. Some ETFs cover the counterparty risk by buying credit protection in the form of credit default swaps.

Dividend distributionLike conventional index funds, ETFs can deal with dividend payments in two ways. They may, for example, pay dividends to their shareholders. Dividend payments on the securities held in the fund remain in the fund in the form of cash until they are paid out at fixed time intervals. This leaves the investor with the task of managing the reinvestment of these dividends, but also allows him to obtain periodic cash flows. In addition, the accumulation of the dividends in the fund may lead to small deviations of performance from the index. ETFs may also reinvest dividends. These ETFs track the total return (including reinvested dividends) on the underlying index. The only cash flows the investor has to deal with are those occurring when he buys the ETFs and when he sells it.

Primary and secondary marketsAlthough ETFs are registered as open-end funds, there are significant structural differences between ETFs and traditional mutual funds both in how their shares are issued and redeemed and in how their shares or units are traded. Exhibits 2.3 and 2.4 explain the operational structure and activities along the ETF transaction chain in the primary and secondary markets.

2. Background



An ETF, as a registered fund company, is supported by a custodian holding its assets, an administrator producing daily net asset value, and a management company looking after operations. The fund is created when authorised market participants such as institutional investors commit capital to seed a fund that will attempt to replicate an index. Unlike traditional mutual funds or unit investment trusts, shares in the ETF are created by the authorised market participant’s depositing a specified block of securities with the ETF. The authorised market participant purchases the block

of the underlying securities directly on the markets, based on the information contained in the portfolio composition file (PCF), a file prepared by the ETF manager. In return for this deposit, the authorised market participant receives a fixed amount of ETF shares with a net asset value (NAV) that is the value of the replicating index. ETF shares are usually created or redeemed in lots of 50,000 or 100,000 shares or some other pre-specified size, known as creation units. Some or all of the ETF shares may then be sold on a stock exchange.

2. Background

Exhibit 2.3: The graph lays out the process of creating and redeeming an ETF in the primary market and trading it in the secondary market, indicating participants involved in this transaction flow. Note that creation and redemption do not necessarily occur based on securities but more often based on cash.

Cash ETF

Cash ETF

Cash

Securities

ETF creation unitsBasket of securities

Produces daily NAV Manages the fund

ETFcustodian

ETFMarket makers;

Liquidity providers;

Authorisedmarket participants

EXCHANGE

Capital markets(securities lending,

borrowing & trading activities)

Broker

Investor

Primary market (creation)

Secondary market (buy)

Sell: (reverse process of buy)

ETFs are exchanged for cash

Redemption: (reverse process of Creation)

ETF market makers swap ETF units with the ETF custodian for the underlying basket of securities.

Securities can be traded in Capital markets for cash

ETFmanager

Fundcompany

ETFadministrator



On the exchange, ETF market makers look at inventories and start quoting bid and ask prices for the ETF shares. Investors can buy ETF shares through their intermediary at the quoted “ask” price or sell shares at the quoted “bid” price. Intraday buy or sell prices depend on supply and demand and on the prices of the underlying securities. If the price of the ETF shares fluctuates and deviates from its NAV, market participants can step in and make an arbitrage profit on the differences. An indicative NAV (iNAV) is published every fifteen seconds for ETFs, so the price can be compared almost continuously to this iNAV. If ETFs are undervalued compared to their NAV, arbitrageurs buy ETF units and redeem then at the custodial bank in exchange for the underlying securities. If ETFs are overvalued, they buy the underlying securities, redeem them for creation units and then sell them on the markets. So deviations between ETF prices and NAV are small and short-lived.

Trading ETFs off exchangeETFs are frequently traded off exchange, especially for very large orders. The first possibility is to engage in over-the-counter trading of ETF shares. These so-

called block trades may allow investors to benefit from tighter bid/ask spreads than they would on the exchange. The second possibility is to buy an ETF at unknown NAV. An order at unknown NAV that is placed during the day will be executed at the closing NAV of the fund. These orders lead to a creation (buy order) or redemption (sell order) of ETF units, similar to what happens in a traditional mutual fund that is not traded on exchange. This means of buying an ETF does not lead to any bid/ask spread since the order is executed at the NAV; the investor does bear creation and redemption costs.

2.1.3. Different Types of Exchanged-Traded FundsIn this description, we will mention only ETFs that allow access to the normal returns of an asset class or segment of assets. We do not mention here ETFs that use structured forms of investment strategies. In addition to ETFs that allow exposure to the beta of assets such as stocks and bonds, money market ETFs have lately been successful. In the following paragraphs, we describe several recent ETF products for different asset classes.

2. Background

1 Liquidity providers and authorised market participants commit capital to seed a fund aiming at replicating an index

2Liquidity providers and authorised market participants purchase a basket of the underlying securities, based on the portfolio composition file (PCF) prepared by the fund company

3The market makers then exchange the basket of the underlying securities with the Fund Company (ETF custodian) for a set number of ETF units with a NAV, that is, the value of the replicating index

4On the exchange, ETF market makers start market making and quote bid and ask prices of the ETF units based on their inventory

5 Investors can buy ETF units through their retail brokers at the quoted "ask" price, in exchange for cash

6Due to continuous intraday trading, the price of the ETF may fluctuate and deviate from its NAV. Moreover, the underlying index value may also go up or down during the trading day. These events create arbitrage opportunities for the market makers

7 ETF units are created or redeemed on a daily basis, which enables the market makers to keep ETF prices close to the NAV

8The market makers can swap a set number of ETF units with the ETF custodian for the underlying basket of securities, which can then be sold for cash in the secondary market

Exhibit 2.4: Typical activities during an ETF transaction in primary and secondary markets



Equity ETFsETFs that replicate stock market indices were first on the market and are still the most important type. Broad market ETFs attempt to replicate the returns of the entire stock market as reflected by a broad index such as the S&P 500 for the US or the Stoxx 600 for Europe. Such broad ETFs offer diversified exposure to general equity markets. They are thus a shortcut for investors seeking to hold a part of the market (Stock 2006).

The aim of style ETFs is to replicate the returns on a particular investment style. In equity markets, firm size (large cap, small cap) and style (growth, value) have been shown by Fama and French (1996; 1993; 1992) to be important determinants for the cross-sectional variation in expected stock returns. Style ETFs build on these findings and replicate the returns of such investment strategies. Sector ETFs focus on industry sectors, which they attempt to replicate. The motivation for relying on sector exposure to construct an equity portfolio is provided in a study by Ibbotson Associates (2002) that highlights the low correlation of different sectors and the low correlation of sectors and the market. Another study (Hamelink et al. 2001) shows that the benefits of sector diversification outweigh those of country diversification. Further evidence of the importance of sector and style diversification is provided by Vardharaj and Fabozzi (2007). Finally, ETF providers have also moved from developed market exposure to such emerging markets as China.

Fixed-income ETFsIn addition to equity markets, ETFs also provide cheap exposure to fixed-income markets by replicating the returns of

government and corporate bonds. These ETFs can, of course, provide exposure to broad market government bonds, but also to more specific segments. Maturity-segment ETFs reflect the returns on investments in government debt with terms to maturity ranging from short to long. Inflation-protected bond ETFs invest only in inflation-protected bonds.

The corporate debt ETF market is less well developed, at least in Europe. For now, only broad market corporate bonds are available. With the growth of the ETF market, new products specifically designed for sub-segments of the corporate bond market may well be in the works.

Alternative asset class ETFsFinally, the concept of ETFs has been extended to alternative investments. Although not in all cases pure ETFs, but slightly more complicated financial constructions, these investment products enable investors to gain simple access to alternative investment opportunities such as hedge funds, commodities, real estate, or currency baskets. ETFs on alternative asset classes allow investors to diversify portfolios but do not require the infrastructure needed for direct investments and manager selection in alternative asset classes, infrastructure they may be unfamiliar with. The benefits of using alternative index ETFs in a global portfolio have been analysed by Pezier (2008).

ETFs in the alternative investment universe must deal with illiquid underlying assets, an obligation at odds with one of the main objectives of ETFs, that is, to provide high liquidity. As a result, ETFs must usually rely on proxies of the asset class, proxies that can only

2. Background



approximate the price movements in these asset classes.

Hedge fund ETFs, for example, can rely on hedge fund factor models that make it possible to replicate the performance of broad hedge fund indices by investing in more standard and thus more liquid assets. ETFs relying on these models have recently been launched. Hedge fund ETFs can also be set up with the help of managed account platforms: these ETFs enable investors to invest directly in hedge funds via so-called parallel managed accounts of hedge fund managers. To ensure the liquidity of the ETFs, only hedge fund managers who are active in strategies known for their liquidity are selected. Currency ETFs often use certain currency strategies that can be easily implemented in futures markets, such as carry trades.2 Commodity ETFs are based mostly on commodity futures, although some funds also invest in such precious metals as gold. Illiquid underlyings are also a problem for real estate ETFs. Real estate ETFs usually replicate real estate indices that are based on real estate investment trusts (REITs), listed collective equity investment vehicles that provide relatively high liquidity. They may also invest in a basket of real estate stocks. 2.1.4. Alternatives to Exchanged-Traded FundsIn addition to ETFs, there is a variety of financial products that allow simple trades of large baskets of assets: traditional index funds, futures, and total return swaps (TRS). Because of their similar features, they can be regarded—depending on the investment purpose—as alternatives to ETFs.

The closest of these alternatives are traditional index funds, which are in fact the predecessors of ETFs. Index funds can be viewed as unlisted ETFs, to which they are very similar, except that they can be bought from and sold only to the managing company of the mutual fund (primary market).

Investors can also opt for derivative instruments (futures and total return swaps) to trade large baskets of assets. Futures are standardised forward contracts that make it possible to trade baskets of assets (bonds, equities, or commodities) at a certain date in the future. Since these derivatives are traded on exchange, they are highly liquid. Total return swaps (TRSs), by contrast, are not traded on exchange; they are over-the-counter (OTC) contracts. Here, the total return of an index or a single security is swapped for fixed regular cash flows. A TRS is similar to a standard swap except that the total return (cash flows plus capital depreciation/appreciation), not cash flows alone, is swapped. As with any swap, the parties do not transfer actual ownership of the assets. TRSs expose investors to counterparty credit risk because they are traded OTC, while futures are exchange-traded instruments and thus benefit from clearing-house mechanisms that mitigate counterparty credit risk.

2.1.5. Benefits and Use of ETFsBecause they are hybrids of stocks and funds, ETFs provide institutional and private investors with a number of combined benefits and, as a result, improve the ways they invest. ETFs are much easier to trade than funds. And a single ETF trade can provide much broader

2. Background

2 - See Brunnermeier et al. (2008) or Jurek (2007) for an analysis of these strategies.



exposure than a single stock trade. They are also tax efficient.

Ease of tradingThe ease of trading ETFs is the result of their liquidity and transparency. The advantage of highly liquid markets such as the ETF market is that large amounts of assets can be traded without making a large impact on the market. The liquidity of ETFs stems from their listing on exchange and from direct provision of ETFs by authorised participants. Investors can enter or exit at any time. Small trades can be executed whenever the exchange is open and at market prices that change from moment to moment, a possibility that shows a higher degree of liquidity than traditional index funds, priced once a day at the close. Any type of order used in trading stocks can be used in trading ETFs. Larger transactions can be handled efficiently by authorised participants under the in-kind creation and redemption process. As was described above, ETFs can also be traded off exchange.

Second, ETFs are considered more transparent than mutual funds. The detailed composition of the fund is published on a daily basis, and the net asset value is frequently computed and made available to the market during trading hours. Investors are able to see what exactly goes into the ETF, and the investment fees are clearly laid out. In the light of pricing scandals that have affected the mutual fund industry, the transparency of ETFs has become quite a draw; indeed, at the outset, it served as an impetus for the growth of the market.

It is worth mentioning that when an ETF’s market price deviates from the NAV of the

fund, arbitrageurs can step in and profit from the deviation. The arbitrage force contributes to keeping ETF prices very close to their underlying NAV.

Cost One of the primary advantages of ETFs is that they offer all of the benefits associated with index funds at much lower cost. Because of the essence of index tracking, ETFs obviously charge less than actively managed funds. Moreover, even though, like stocks, they involve commissions, their lower costs may make them more attractive than traditional index funds. It is useful to distinguish two aspects of costs, total expense ratios and transaction costs.

First, ETFs charge management fees and other operating fees. The total expense ratio (TER) offers a fair standard by which to compare such costs, since management fees alone might lead to misconceptions. As reported by Euronext (2008), total expense ratios for many standard European equity and government bond indices are now between fifteen and twenty-five basis points. More exotic ETFs in emerging markets, short ETFs, or in specific strategies, are more expensive, ranging from fifty to ninety-five basis points. These costs are significantly lower than those of traditional equity index funds, which usually have expense ratios of around 100 basis points, even when they are simply tracking standard indices. Moreover, the total expense ratios of ETFs have generally fallen over the last three years. In 2006, Fitzrovia reported that the average TER for equity ETFs in Europe was forty-six basis points a year (Morgan Stanley 2006).

2. Background



Second, ETF shares must be bought by the investor, either on or off exchange, and the investor incurs transaction costs. If ETF shares are bought or sold on exchange or over-the-counter, the investor incurs transaction costs that amount to brokerage fees, as well as half the bid/ask spread. If ETFs are bought at unknown NAV, the investor does not bear costs in form of bid/ask spreads but in the form of creation/redemption costs.

Costs differ significantly from one ETF to another. Differences are found in both total expense ratios and transaction costs (either bid/ask spreads or creation/redemption fees). These differences are not merely a result of the different index or asset class tracked by the ETF; indeed, the costs of ETFs tracking similar segments or even the same index may differ greatly.

The cost advantage of ETFs over other indexing instruments obviously depends on the benchmark. For large institutional investors, mandates to replicate an index are usually less costly than an ETF. But ETFs usually charge less than other open-ended index funds. Moreover, the costs are specific to the context in which the index products are used. In particular, the position size and frequency of trading determine the relative merits of each instrument. Kostovetsky (2003), for example, finds that for large investments, ETFs are preferable to index funds, while for small amounts, the high transaction costs make ETFs less attractive unless the holding period is long. Gastineau (2001) points out the reasons that make ETFs more cost efficient than index funds. First, ETFs are usually very large funds, allowing economies of scale and, second,

expenses for the transfer agency function of mutual funds are not incurred with ETFs.

Obtaining broad and diversified market exposureBenefiting from the quality of index funds, investors can gain instant and diversified access to various markets by trading ETFs. Once an investor buys an ETF, he gets exposure to the entire market for the underlying assets and diversification of systematic risk. In addition, the portfolio of ETFs can provide more customised diversification. A cautious investor who wants to invest in real estate and fixed income, for example, could easily form a portfolio by trading ETFs tracking real estate indices and fixed-income ETFs, and he could structure the fixed-income portion by splitting it into medium-term and short-term bonds or government bonds and corporate bonds.

Trading with high tax efficiencyTax-conscious investors have lately begun to prefer ETFs to mutual funds. The special tax rules on conventional mutual funds require that realised capital gains be passed to shareholders, a requirement that is widely regarded as increasing the tax burden on buy-and-hold investors (Dickson and Shoven 1995; Dickson et al. 2000). Although ETFs are subject to the same tax rules as mutual funds, their distinct “redemption in-kind” mechanism, allowing an investor to redeem a large number of ETF shares by swapping ETFs for the underlying stock shares, does not incur capital gains.

Some investors even use ETFs for such tax manoeuvring as realising capital

2. Background



losses, getting around restrictions on wash-sales,3 and even entering into tax swaps (Bansal and Somani 2002).

Immediacy of exposureA basic use of ETFs is to equitise cash and manage cash flows. As a kind of easy trading vehicle, ETFs are useful for cash management. A fund manager could invest cash inflows in ETFs as a temporary investment that minimises cash drag or benchmark risk. By contrast, if the fund manager needs to raise cash immediately, he could buy time by first liquidating ETF holdings on the market and then other positions. Although futures have been used for the same purpose, ETFs can be a good alternative, as small trades can be made, they require no special documents and accounts, are not subject to roll-over problems and margin maintenance, and can cover more benchmarks than futures (Fuhr 2001).

2.1.6. Advanced ETF Products and Portfolio PracticesWe provide below an overview of advanced types of ETF products, as well as of advanced ways of using ETFs in portfolio practice.

Inverse ETFsInverse ETFs, also called short ETFs, are supposed to provide investors with the inverse of the performance of an index, which is achieved through short selling. In addition, these ETFs provide investors with the money market interest on the amount invested and interest earned on the short position.

Leveraged ETFsLeveraged ETFs provide investors more aggressive exposure to the underlying

index, without the operational hassles of making leveraged investments themselves. Leveraged ETFs usually attempt to provide constant leverage in such a way that the excess returns of the index are magnified by, say, a factor of two for the holder of a leveraged ETF. There are also leveraged versions of inverse ETFs, so investors can magnify their inverse exposure in a simple trade.

Options on ETFsOptions on ETFs began trading on derivatives exchanges shortly after the introduction of ETFs. These instruments are limited to a relatively narrow range of the most successful ETFs. The possible advantages of these options include precise exposure to the underlying fund, minimum investments lower than those required by index options, as well as physical delivery of the underlying asset if the option is exercised (index options, by contrast, are settled in cash).

Shorting ETFsUnlike traditional index funds, ETFs may be sold short. Since ETFs can be borrowed and sold short, long/short strategies are possible. With these strategies, long/short exposure to different style or sector indices can be used to capitalise on return differentials between categories while maintaining low or zero exposure to market risk. As a temporary way to become defensive without incurring transaction costs and undesirable capital gains, this mechanism can be used in various ways, including more sophisticated trading strategies involving shorting some combination of several indices. In addition, ETFs can be sold short, as part of a purely speculative trade, to take advantage of market downturns.

2. Background

3 - A wash-sale is the sale of a security at a loss followed by the immediate repurchase of the identical security. Wash-sales are used to reduce the tax burden, since other capital gains can often be offset by these capital losses and therefore reduce total taxable gains.



Lending ETF unitsETF units held by an investor may be lent out to generate additional income for the portfolio. Interest paid by the borrower of the ETF may compensate for management fees and generate income above the management fees in the ETF.

2.1.7. Tracking Error and LiquidityTracking error and liquidity are the two most crucial criteria for evaluating the quality of an ETF. So it is important to know how to assess the tracking and liquidity error of ETFs.

Tracking errorThere are many ways to assess the tracking quality of an ETF. First, and quite evidently, it is possible to analyse the difference between the returns on the ETF and those on the index. Second, the correlation of the two assets can be used to determine the tracking quality. Another simple method of analysing tracking error is to compare the mean returns of both assets. There are, however, more sophisticated means of evaluating tracking error. These means include asymmetric or downside tracking error (which is the relative return equivalent to downside risk measures such as semi-variance in an absolute return context) or co-integration analysis (see Engle and Sarkar 2006 for an application to the tracking quality of ETFs). LiquidityThe second key issue with indexing instruments is liquidity. Practitioners, of course, are highly familiar with liquidity, but the finance literature has yet to come to a consensus on theory and on empirical methodology. Practitioners, for example, have long used a number of liquidity measures, but academic articles

continue to debate their merits. Popular liquidity indicators are market spreads, turnover, and assets under management. Several authors in the finance literature have proposed more advanced liquidity measures. One recently advocated measure, as proposed by Acharya and Pedersen (2005), is explained in more detail in the box below.

Of course, the number of transactions in ETF shares is not necessarily indicative of the liquidity of an ETF. For several reasons, in fact, ETFs may be classified as highly liquid even if relatively few ETF shares change hands. The first is that the market maker has a contractual obligation towards the stock exchange and towards the ETF provider to fulfil its role as market maker for a given transaction size and with a determined maximum spread. Therefore, even if trading volume is low on a given day, ETF investors can trade at any time of the day. The second reason is that in Europe most ETF transaction volume actually takes place off exchange, either by trading ETF shares over the counter or at unknown NAV. The volume traded on exchange is thus not a good indicator of the actual transaction volume.

The true liquidity of an ETF is the liquidity of the underlying securities. After all, any deviation of the price of the ETF from the price of the basket of securities is easily arbitraged away through the creation and redemption mechanism. This arbitrage depends only on the liquidity of the underlying securities. As described above, the market maker swaps ETF units with the ETF custodian for the basket of securities of the ETF, so it is the liquidity of securities in this basket that matters.

2. Background



2. Background

The Acharya and Pedersen (2005) Liquidity MeasureThe Acharya and Pedersen (2005) liquidity measure is derived from the so-called liquidity-adjusted CAPM. This is a multifactor model for expected excess stock returns of the following form:

E [ rti − rt

f ] = E [cti ] + λβ1i + λβ 2 i − λβ 3 i − λβ 4 i

The various beta terms in the regression equation correspond to covariance terms of returns and illiquidity. β1i is the traditional CAPM beta (covariance of the stock return and market return), β2i is the covariance of the stock’s illiquidity and market illiquidity, β3i is the covariance of stock returns and market illiquidity, and β4i is the covariance of stock illiquidity and the market return.

The illiquidity measure for a given stock i in the month t is computed as:

ILLIQt

i =1

Daysti

Rtdi

Vtdi

d =1

Daysti

∑

where Rtd

i is the absolute return of stock i on day d of month t, and Vtdi is the volume

of the stock. The illiquidity measure reflects the idea that the price of an illiquid stock will display large movements in response to a given volume of trading. The authors find that of the four terms in the asset pricing model the co-movement of stock illiquidity and market returns has the greatest influence. This liquidity measure has been constructed to evaluate the liquidity of stocks. It would not be helpful to apply this measure directly to the ETF shares, for the reasons cited above. However, it may be a means of measuring the liquidity of underlying securities.

Pricing and Performance DriftAlthough index ETFs are designed to track an index passively and provide exposure to its risk and performance features, ETFs that for legal reasons cannot fully replicate an index need to be managed more actively. Any deviation of an ETF’s returns from the underlying index returns results in a performance gap. Unlike index funds, which can be bought and sold only at their daily NAV, ETFs can be exchanged in secondary markets at ask/bid prices that may differ from their NAV. Exhibit 2.5 provides a description of the sources of deviation that ETFs may encounter.

For an investor, the total performance shortfall (or gain) is the right measure with which to identify the gap between the performance of the ETF and that of its underlying index. This gap should be measured as the return difference between the underlying index and the ETF—taking into account the investor’s actual buying price. This price, however, is not easy to obtain, and might require studying specific transactions to take into consideration the specific market impact of such trades.



2. Background

Exhibit 2.5: Performance shortfall of an ETF

Secondary market Primary market

Market controlled

Investors' buy/sellprice of the ETF

NAV of the exchange—traded fund

Pricing efficiency Management efficiency

Total performance shortfall, from an investor's perspective

UnderlyingIndex value

Manager controlled

The total performance shortfall can be viewed as the sum of the ETF management inefficiencies and market inefficiencies. Since the former lie within the ETF management itself, they can be controlled by the fund management company. The latter are beyond the control of the ETF company, since they depend on the market makers, supply and demand, and transaction costs.

Net asset value versus market priceAn ETF has a net asset value (NAV) calculated with reference to the market value of the securities held. NAV is the total value of the fund after netting the market value of each underlying share in its holdings, cash, accruals, fees, operating costs and other liabilities and divided by the number of issued shares. For fully replicated index trackers, the NAV should be exactly the same as or very close to the fund’s underlying index value (taking into consideration a multiplier if required).

On exchange, however, the market price of an ETF, like that of a stock, is determined by supply and demand. ETFs are bought and sold at their market prices, which may be at a premium or discount to their NAVs. When the market price of an ETF is not equal to its NAV, arbitrage opportunities are created and the creation and redemption process brings the fund’s market price back to its NAV.

The intraday NAVs of ETFs are also usually calculated every fifteen seconds by third-party vendors; the market prices of the underlying index constituents are taken into account so that investors can tell whether the ETF is fairly priced. This intraday NAV, also known as indicative net asset value (iNAV) or indicative optimised portfolio value (IOPV), is different from the daily NAV of the fund, which is computed after the market closes for the day.

In an empirical study, Engle and Sarkar (2006) find that in the US ETFs have highly efficient prices, though their conclusions for international ETFs are different.



2. Background

In fact, the authors find that the premiums or discounts on fund NAVs are usually small and disappear very quickly, a disappearance that confirms the view that the creation and redemption mechanism of ETFs effectively limits and destroys arbitrage opportunities. Performance driftIdeally, ETFs should derive their value and volatility only from the market movements of the underlying index or market prices of the constituent securities of this index. But perfect replication is not always possible; in fact, performance drift is inevitable. An index portfolio is only a paper portfolio and requires virtually no management, administration, asset buying or selling, custody, and so on. An ETF, by contrast, holds assets physically, manages them, distributes dividends and handles a relationship with investors. These operations incur costs. So to keep costs down and make sure they are consistent it is necessary to understand the components of these costs. Several costs can be a drag on ETF performance, some related to the direct costs of implementing the strategy, others to the way the index is replicated and exceptions handled.• Implementation: ETFs need not replicate indices by buying or selling the underlying securities. They are paper portfolios calculated on the basis of market prices and weightings of their underlying securities. The underlying securities may not be very liquid and, given the large size of an ETF portfolio, the price of a constituent security may go up as a result of high demand during implementation. This cost, also known as portfolio construction/rebalancing cost or transition cost, which also includes the actual transaction costs, results in a performance drag on the ETF portfolio.• Management fees and other operational expenses: unlike ETF portfolios, indices do not incur management fees, administrative costs and other operating expenses. Often expressed in terms of total expense ratio as a percentage of the NAV, these costs are deducted from the ETF assets and the daily NAV is affected accordingly (daily accrual). When dividends and interest income are paid, usually every quarter or twice a year, total management expenses are deducted from the payment and the NAV of the ETF returns to the index value.• Transaction costs in the secondary market: investors buying or selling ETFs on exchange through their broker are subject to brokerage commission, bid/ask spreads, the market impact of a large transaction, stamp duty, transaction levies charged by the exchange, and so on. All these costs reduce the ETF returns compared to those of the underlying index • Cash drag: if ETFs pay dividends they usually do so every quarter or twice a year. However, the underlying securities pay dividends sporadically throughout the year. While the index value reflects full dividend reinvestment, an ETF portfolio holds extra cash that has no capital appreciation, no returns. This generates a minor disparity between the ETF portfolio value and the underlying index value. Tracking error caused by this phenomenon is called “cash drag” because the ETF portfolio holds extra cash that drags its performance down.



2.2. Asset Allocation with ETFs We turn now to the uses to which ETFs can be put in portfolio management. First, we provide an introduction to core-satellite portfolio management. We then provide examples of how ETFs may be used to do the risk budgeting required by this portfolio management technique.

2.2.1. Core-Satellite Portfolio ManagementCore-satellite management, a new paradigm in investment management, has received increasing attention from investors. The objective of this section is to give a short introduction to the core-satellite approach. We first present static core-satellite management techniques. We then turn to dynamic portfolio strategies based on the core-satellite approach.

Introduction to core-satellite managementThe core-satellite approach divides the portfolio into a core component, which

is passively managed and fully replicates the investor’s specifically designated benchmark, and an outperformance-seeking component, made up of one or more satellites, that is allowed higher tracking error. This satellite can be active or passive, depending on the preferences of the investor.

The core portfolio exists to provide the best possible risk/return trade-off while respecting the constraints—with respect to long-term risk exposures—to which the investor may be subject. The core can be made up of a pure index fund or an ETF. However, any long-term allocation to different indices can constitute the benchmark, which is not necessarily limited to market indices, but can also be made up of an allocation to a variety of sectors or styles and to new forms of indexing. The important point is that this allocation is the long-term benchmark representing the investor’s risk/return preferences.

2. Background

• Mispricing costs in secondary markets: an ETF may trade at lower than (discount) its NAV or higher than (premium) its NAV. Factors such as uneven supply and demand, illiquid underlying securities, and market inefficiency may contribute to the move of trading prices away from NAV. Since ETF shares can be created/redeemed anytime during trading hours by authorised market participants or arbitrageurs, this disparity does not last long.

On the other hand, there are also several benefits that may allow an ETF to compensate for replication costs. In some cases an ETF can yield higher returns than the index to be replicated.• Securities lending: ETF providers can lend their securities to other market participants and thereby earn lending fees.• Tax benefits: in some countries it is possible to partly recover withholding taxes through the purchase of single stocks during the period of dividend payments.• Management of index events: intelligent management of index component changes and other events can generate additional returns for the ETF. However, if done unsuccessfully, such management may also lead to underperformance of the index.



The satellite is not subject to the constraints to which the core is subject. In the satellite, the investor seeks to outperform the core. The counterpart of the potential for outperformance is the risk of deviating from the investors’ long-term risk exposure as defined in the core. Satellites are often actively managed and usually invested in markets that require more specialised managers. However, outperformance of the benchmark may stem not only from active management but also—and perhaps more consistently—from passive investment products that track asset classes or sub-segments whose

longer-term performance is significantly better than those in the benchmark.

The separation of funds into a core and a satellite is not done for practical reasons alone; indeed, this separation is grounded on economic theory. It is optimal for investors who benchmark to a specific external benchmark.4

Core-satellite management has become the standard means of designing portfolios. A detailed analytical derivation of the core-satellite portfolio design is presented in the box below.

2. Background

4 - For an analytical derivation of this separation in an asset/liability management context, see Martellini (2008) or Martellini and Milhau (2009).

The Arithmetic of Core-Satellite InvestingIn this box, we set the problem in a simple mean-variance analysis to show how to derive the optimal proportions to invest in the satellite and the core.

We first take a core-satellite construction with a single satellite. The mathematics is then straightforward. The overall portfolio P, which is a combination of the core portfolio and the satellite portfolio, can be expressed as follows:

P = wS + 1 −w( )C

where w is the fraction invested in the satellite S, and 1-w is the fraction invested in the core C. The difference between the portfolio and its benchmark B is computed as follows:

P − B = wS + 1 −w( )C − B = w S − B( ) + 1 −w( ) C − B( )

Assuming for the sake of simplicity that the core replicates the benchmark perfectly, we get C = B; we then have: P − B = w S − B( )

Using this formulation, we can now calculate the tracking error of the portfolio TE(P) with respect to its benchmark B. It is given as a function of the tracking error of the satellite TE(S):

TE P( ) = var P − B( ) = w var S − B( ) = wTE S( )This formulation makes it possible to assess the efficiency of a core-satelliteportfolio with respect to tracking error management. Consider, for example, an active investor who is allowed a 2.5% tracking error budget. The investor either hires one manager with a tracking error equal to 2.5% for the entire portfolio or forms a passive core, consisting of 80% of his overall portfolio, and leaves 20% in an aggressively managed satellite with a tracking error chosen so that the overall



2. Background

portfolio tracking error does not break his risk budget; here, the satellite is permitted a 12.5% tracking error, as given by the following computation:

TE( S ) =

TE P( )w

=2.5%20%

=12.5%

The next step consists of deriving the optimal proportion w* to invest in the satellite and in the core. This problem can be solved by simple mean-variance analysis, using the following utility function U(x):

U( x ) = E [ x ] − λσ 2 ( x )

where E[x] is the expectation operator, σ2(x) its variance, and λ the investor’s risk aversion. Hence, we have

U( P − B) = E [P − B] − λσ 2( P − B) = IR P( ) ×TE P( ) − λTE 2 P( ) (1)

where IR(P) is the information ratio of the portfolio P with respect to the benchmark (see Grinold and Kahn (2000), for example). Hence:

IR P( ) =

E [P − B]σ( P − B)

=E [P − B]TE P( )

When the core perfectly replicates the benchmark, the information ratio of the overall portfolio IR(P) is actually independent of the proportions in the core and the satellite and equal to the information ratio of the satellite IR(S) (as long as the proportion w invested in the satellite is strictly positive). This can be easily seen from the following:

IR P( ) =

E [wS + 1 −w( )C − B]

σ(wS + 1 −w( )C − B)=

wE[ S − B]wTE S( )

= IR S( )

Replacing TE(P) by its expression as a function of TE(S) in equation (1), we may rewrite the optimisation program as follows:

U w( ) = IR ×w ×TE( S ) − λw 2TE 2( S )

The first-order condition, enabling us to obtain the optimal value of the proportion w* to be invested in the satellite portfolio, reads:

∂U∂w

w *( ) = 0 ⇒w * =IR

2λTE S( )

For example, let us assume that the tracking error of the active fund is 5%, that the information ratio IR is 0.5, and that the coefficient of risk-aversion with respect to relative risk λ is 20. Then, the optimal proportion invested in the active portfolio is:

w * =

IR2λTE S( )

=0.5

2 × 20 × 5%= 25%

The tracking error resulting from this allocation is: TE P( ) = 25% × 5% =1.25%



Benefits of the core-satellite approachThe core-satellite approach has many advantages over conventional portfolio management, especially when the satellites are actively managed. First, it makes it possible to directly control the portfolio’s overall tracking error. The core portfolio simply tracks the long-term benchmark; it does not deviate from that benchmark and therefore has no tracking error. The performance-seeking satellite, by

contrast, is allowed significant tracking error. But as the satellite is only a fraction of the total investment, overall tracking error is of course much lower. To maintain the tracking error required for the overall portfolio, one needs only strike the proper balance between the tracking error of the satellite and its weight in the overall portfolio. Hence, rather than imposing a direct tracking error constraint on the overall portfolio, the core-satellite approach

2. Background

So far we have assumed that the satellite is made up of a single portfolio. As suggested before, the satellite investment might again consist of several components, each with a different manager. The point is then to find the optimal allocation to the active managers. Extending the analysis to the case of a satellite S invested in a number n of active portfolio managers Si according to the proportions wi is straightforward.

If S = wi Si

i =1

n

∑ , the excess return on the satellite portfolio is then:

S − B = wi Si − B( )

i =1

n

∑

and the tracking error of the satellite portfolio reads:

TE S( ) = wiwj σij

i , j =1

N

∑ − 2 wi σiB + σB2

i =1

N

∑⎛

⎝⎜⎞

⎠⎟

12

where σij is the covariance between portfolio managers Si and Sj , and σB is the volatility of the benchmark.

One can then find the optimal fraction invested in each active manager so as to achieve the highest possible information ratio. One can show (Scherer 2002) that the optimal condition is that the ratio of return to risk contribution is the same for all managers:

wk αk

wk2σαk

2 + wkwj σkjj

∑⎛

⎝⎜⎞

⎠⎟

TE( S )

=wl αl

wl2σα l

2 + wlwj σkjj

∑⎛

⎝⎜⎞

⎠⎟

TE( S )

where αi is the alpha that portfolio manager i can generate with respect to the core portfolio.



limits the tracking error by controlling the weights of the satellites.

As a consequence, the core-satellite approach allows a better exploitation of a fund manager’s skills—if the satellite is actively managed. Since the satellite allows substantial deviation from the benchmark, the fund manager is freer to use his personal skills and thus perhaps to outperform the benchmark; he need not be fixated on maintaining low tracking error. To see this more clearly, consider an active manager with a 5% tracking error constraint. In fact, such a manager is95% passive, and thereby very limited in his active portfolio choices.

At the same time, the restriction of the weight attributed to the satellite in the overall portfolio keeps risks under control. In short, the skills of asset and fund managers can be exploited much more efficiently and in a risk-controlled manner.

Finally, if the investor uses active managers, the separation into a core and a satellite may help reduce fees: high management fees will be paid only for this actively managed satellite; the passively managed core usually involves much lower fees.5

To show the possible cost advantages of using an active manager in the framework of the core-satellite approach, consider the following example. Assume that an investor has a relative risk tracking error budget equal to 5% and wishes to use the skills of an active manager. The first solution is to allocate 100% of the portfolio to an active manager who will commit to respecting the 5% tracking error constraint. We assume that the

management fees charged by this active manager are equal to forty basis points. The second solution consists of allocating 75% of the portfolio to a purely passive product, e.g., an ETF or to a strategy based on an efficient benchmark, and the remaining 25% of the portfolio to a 20% tracking error manager. We assume that the management fees of a core portfolio passively invested in ETFs are equal to sixteen basis points.

The second solution, consistent with a core-satellite approach to active asset management, offers two benefits. First, allowing the active manager to deviate significantly from the benchmark leads to a better use of the manager’s skills. If the manager has reliable views of the market, a 5% tracking error constraint leaves him with too little room to make active decisions consistent with these views. Beating the market is notoriously tough. Starting out with a hand tied behind your back is unlikely to make things any easier. The second benefit of the core-satellite approach is that it makes it possible to differentiate the value added by the core (the benchmark) and the outperformance generated by deviation from that benchmark. Finally, the core-satellite approach keeps management fees under control. Based on our assumptions, i.e., forty basis points for the active manager and sixteen basis points for the core—and these are realistic assumptions—one gets total fees for the portfolio equal to twenty-two basis points, lower than the fees usually charged by active managers with the same tracking error constraints (5%). Asset managers must compete for business, of course, and such a difference in fees may be decisive. Exhibit 2.6 illustrates

2. Background

5 - In fact, the origins of core-satellite portfolio management are linked to an attempt to optimise the costs of active portfolio management.



the benefits provided by the core-satellite approach in terms of tracking error control and management fees in the case of an actively managed satellite.

Exhibit 2.6: This table illustrates how the core-satellite approach provides benefits to asset managers in terms of tracking error control and management fees if the investor uses an active manager.

Core Satellite Overall

Weight 75% 25% 100%

Tracking error 0% 20% 5%(0%×0.75+20%×0.25)

Management fees

16bps 40bps 22bps(16×0.75+40×0.25)

Dynamic core-satellite investingThe core-satellite investment concept can also be extended to a dynamic context, where the proportion invested in the performance-seeking portfolio (i.e., the satellite) can vary as a function of the current cumulative outperformance of the overall portfolio.

The dynamic core-satellite concept builds on the constant proportion portfolio insurance (CPPI) principle. This principle, described by Black and Jones (1987) and Black and Perold (1992), allows the production of option-like positions through systematic trading rules. The CPPI technique dynamically allocates total assets to a risky asset in proportion to a multiple of a cushion defined as the difference between current portfolio value and a desired protective floor. This produces an effect similar to that of owning a put option. In such a strategy, the portfolio’s exposure tends to zero as the cushion approaches zero; when the cushion is zero, the portfolio is completely invested in cash. Thus, in theory, the guarantee is perfect: the strategy of exposure ensures that the portfolio never descends below the floor; in the event that

it touches the floor, the fund is “dead”, i.e., it can deliver no performance beyond the guarantee.

This CPPI procedure can be transferred to a relative return context. Amenc et al. (2004) show that an approach similar to standard CPPI can be taken to offer the investor a relative performance guarantee (underperformance of the benchmark is capped). Conventional CPPI techniques still apply, as long as the risky asset is re-interpreted as the satellite portfolio, which contains risk with respect to the benchmark, and the risk-free asset is re-interpreted as the core portfolio, which contains no risk with respect to the benchmark. The key difference from CPPI is that the core or benchmark portfolio can itself be risky. In a relative-risk context, the dynamic core-satellite investment can be used to improve the performance of a broad equity portfolio by adding riskier asset classes to the satellite. Dynamic core-satellite investing may also be of interest to pension funds, which must manage their liabilities: the core then is made up of a liability-hedging portfolio, and the satellite is expected to deliver outperformance.

Exhibit 2.7: This table compares the traditional CPPI and the relative CPPI approaches

Traditional CPPI Relative CPPI Approach

Risky asset Satellite portfolio

Risk-free asset Core portfolio

This dynamic version of a core-satellite approach, which can be seen as a structured form of portfolio management, is hence a natural extension of CPPI techniques. The advantage of the approach is that it allows an investor to truncate the relative return distribution so as to allocate the probability weights away from severe

2. Background



relative underperformance and towards greater potential outperformance.

Core-satellite portfolios are usually constructed by putting assets that are supposed to outperform the core in the satellite. But if economic conditions become temporarily unfavourable the satellite may in fact underperform the core. The dynamic core-satellite approach makes it possible to reduce a satellite’s impact on performance during a period of relative underperformance, while maximising the benefits of the periods of outperformance.

As it happens, investor expectations are rarely symmetric. In other words, when stock market indices perform well, investors are happy to be engaged in relative return strategies. On the other hand, when stock market indices perform poorly, they express a strong desire for absolute return strategies. Value-at-Risk minimisation and volatility minimisation allow only symmetric risk management. For example, the minimum-variance process leads to forgoing upside potential in the performance of commercial indices in exchange for lower exposure to downside risk. Although this strategy allows long-term outperformance, it can lead to significant short-term underperformance. It is also very hard to recover from severe market drawdowns. The dynamic core-satellite technique, by contrast, focuses on asymmetric risk management.

From an absolute-return perspective, it is possible to propose a trade-off between the performance of the core and satellite. This trade-off is not symmetric, as it involves maximising the investment in

the satellite when it is outperforming the core and, conversely, minimising it when it is underperforming. The aim of this dynamic allocation is to produce greater risk-adjusted returns than those produced by static core-satellite management. Like standard CPPI, this dynamic allocation first requires the imposition of a lower limit on underperformance of the benchmark at the terminal date. This so-called floor is usually a fraction of the benchmark portfolio, say 90%. Investment in the satellite then provides access to potential outperformance of the benchmark.

This method, derived analytically in the box below, leads to an increase in the fraction allocated to the satellite when the satellite outperforms the benchmark. An accumulation of past outperformance results in an increase in the cushion and therefore in the potential for a more aggressive strategy in the future. If the satellite has underperformed the benchmark, however, the fraction invested in the satellite decreases in an attempt to ensure the guarantee of the relative performance objective.

At this point, it may be useful to summarise the various possibilities (see exhibit 2.8 below). The investor must first choose his long-term benchmark, the core portfolio. He must then identify attractive sources of outperformance for the satellite. Using these components, the investor may manage his tracking error by defining, statically or dynamically, the allocations to the two.

2. Background



Exhibit 2.8 Allocation to the core and satellite keeps tracking error under control

Core SatelliteDefines the investor’s long-term choices in terms of risk/return profile through

• exposure to standard commercial indices• improved allocation to sub-segments (such as sectors, styles, and so on) or asset classes• improved allocation to constituents of commercial indices (new forms of indexing)

Static core-satellite approach Symmetric management of tracking error by fixing allocation to

the core and satellite

Dynamic core-satellite approach Asymmetric management of tracking error by using a strategy to limit the underperformance of the core while benefiting from the

upside potential of the satellite

Seeks to outperform the core while inducing tracking error through

• exposure to additional risk premia • abnormal returns (alpha) obtained by an active strategy

Core-satellite portfolios may be constructed either across asset classes or for individual asset classes. An equity portfolio that invests in a standard index in the core and in an index for emerging market small caps in the satellite, for example, is a single-class core-satellite portfolio. A core portfolio made up of equity, real estate and bond indices, and a satellite portfolio of active equity-market-neutral hedge funds is a multi-asset class portfolio.

2. Background

The Arithmetic of the Dynamic Core-Satellite ProcessThe dynamic core-satellite investment process has two objectives: to increase the fraction allocated to the satellite when the satellite has outperformed the benchmark and to reduce this fraction invested when the satellite has underperformed the benchmark.

This dual objective can be met with a suitable extension of CPPI to relative risk management. Let Pt be the value of the portfolio at date t. The portfolio Pt can be broken down into a floor Ft and a cushion Ct, according to the relation Pt = Ft + Ct. Note this slightly abusive notation compared to the derivation of the static core-satellite investment approach, in which Ct was the core portfolio. We use Bt to denote both the core and the benchmark. The floor is given by Ft = kBt, where k is a constant smaller than 1. Finally, let the investment in the satellite be Et = wSt= mCt = m(Pt - Ft), with m a constant multiplier larger than 1 and w the fraction invested in the satellite. The remainder of the portfolio Pt-Et = (1-w)Bt is invested in the benchmark.

In a relative investment context, the core will contain some assets that closely track a given benchmark, whereas the satellite will have assets that ought to outperform this benchmark. For example, the B for the core investment can be thought of as investments in a bond portfolio, whereas the investment in the satellite S is made up of stocks. In the following, dSt/St can then be thought of as the continuous-time stock process, and dBt/Bt as the respective bond process. By the same token, Et can be thought of as the total invested in equities.

We assume the following dynamics for the assets and the portfolio value:

dSt

St

= μdt + σdWt

dBt

Bt

= rdt



dPt = Et ×

dSt

St

+ Pt − Et( ) ×dBt

Bt

where Wt is a standard Brownian motion, and r the continuously compounded interest rate.

The process for cushion growth tells us about the upside potential and allows us to calibrate an optimal value for m:

dCt = dPt − dFt = Et ×

dSt

St

+ ( Pt − Et ) ×dBt

Bt

− dFt

dCt = mCt ×

dSt

St

+ (Ct + Ft − mCt ) ×dBt

Bt

− Ft ×dBt

Bt

dCt = Ct m

dSt

St

+ (1 − m)dBt

Bt

⎛

⎝⎜⎞

⎠⎟

It is useful to derive explicitly the final value of the portfolio PT as well as the cushion CT. The floor is given as FT = exp(-r(T-t))FT, where FT corresponds to the guaranteed capital at the terminal date. The value of the portfolio at terminal date is then PT = FT + CT. To estimate CT, note that

dCt

Ct

= mdSt

St

+ (1 − m)dBt

Bt

⎛

⎝⎜⎞

⎠⎟=

dBt

Bt

+ mdSt

St

−dBt

Bt

⎛

⎝⎜⎞

⎠⎟

One can solve the previous stochastic differential equation to obtain the expected value of the cushion at the terminal date CT, as well as the expected total portfolio value PT:

CT = C0

ST

S0

⎛

⎝⎜⎞

⎠⎟

m

exp 1 − m( )( r +1

2mσ 2 )T

⎡

⎣⎢

⎤

⎦⎥

PT = FT + C0

ST

S0

⎛

⎝⎜⎞

⎠⎟

m

exp 1 − m( )( r +1

2mσ 2 )T

⎡

⎣⎢

⎤

⎦⎥

When no leverage is allowed, the exposure is limited to total assets PT. In this case, ET=min(Pt, m(Pt - Ft)), and there is no closed-form solution for the payoff PT in terms of the level of the stock market St (Sharpe and Perold 1988).

2. Background

Dynamic management of the tracking errorStatic core-satellite management makes it easy to manage overall tracking error. If the investor has a particular tracking error budget, fixing the proportions invested in the core and in the satellite ensures that he will stay within this budget.

Dynamic management of tracking error, on the other hand, offers investors full access to good tracking error, while keeping bad tracking error to an acceptable minimum, and it does so by adjusting the fractions invested in the core and the satellite. Relative risk control is thus made more efficient.



2. Background

Illustrative ExampleAssume that the benchmark is a passive investment, e.g., a bond index. The guarantee is set at 90% of the benchmark value and we assume that the multiplier is equal to four (further details can be found in the box on the arithmetic of the dynamic core-satellite process).

At the date T0, the initial portfolio value and benchmark value are set at 100, with a floor of 90% of the benchmark value. The floor is thus 0.9×100 = 90. The cushion is therefore equal to 100 – 90 = 10. The investment in the satellite is then 10 × 4 = 40, which results in 100 – 40 = 60 in the core. Assume that at date T1 the difference between the satellite and the benchmark is +10% (as it would be if, for example, the satellite returned 0% and the core returned -10%). In this case, the investment in the core has fallen by 10% (from 60 to 54). The value of the satellite portfolio has remained at 40, while the benchmark has also decreased by 10% (from 100 to 90). The difference between the fund value (94 = 54 + 40) and the benchmark value (90) is now equal to 4. The floor has dropped from 0.9 × 100 to 0.9 × 90 = 81. Thus the cushion is now 94-81=13. The new optimal fraction to invest in the satellite is 13 × 4 = 52, which leaves 94 – 52 = 42 in the core. On date T1 the resulting allocation is therefore 52/94 = 55% in the satellite and 42/94 = 45% in the core.

Let us assume, on the other hand, that the difference between the satellite and the benchmark is -10% (as it would be, for example, if the satellite remained unchanged and the core returned +10%). In this case, the value of the core has increased by 10% (from 60 to 66). The value of the satellite has remained at 40, while the benchmark has increased by 10% (from 100 to 110). The floor is now at 0.9×110 = 99. The difference between the fund value (106 = 66 + 40) and the floor (99) is now equal to 7, meaning that the cushion has decreased from its initial value of 10. The new optimal fraction to invest in the satellite is 7 × 4 = 28, which leaves 106 – 28 = 78 in the core. On date T1 the resulting allocation is therefore 28/106 = 26% in the satellite and 78/106 = 74% in the core.

As can be seen from this example, the method leads to an increase in the fraction allocated to the satellite (from 40% to 55% in the example) when the satellite has outperformed the benchmark. The accumulation of past outperformance has resulted in an increase in the cushion, and thus greater potential for a more aggressive (higher tracking error) strategy in the future. If, on the other hand, the satellite has underperformed the benchmark, the method leads to a tighter tracking error strategy (through a decrease of the fraction invested in the satellite portfolio) in an attempt to ensure that the relative performance objective is met.



Extensions to dynamic core-satellite analysisSetting the floor is the key to dynamic core-satellite management, since it ensures asymmetric risk management of the overall portfolio. If the difference between the floor and the total portfolio value increases, that is, if the cushion becomes larger, more of the assets are allocated to the risky satellite. By contrast, if the cushion becomes smaller, the fraction of investments in the satellite decreases.

In the standard case presented above, the floor is defined as a constant fraction of the benchmark value. Ft = kBt. However, depending on the investment purpose, different floors might be used to better exploit the benefits of core-satellite management. Indeed, the core-satellite approach can be extended in a number of directions, allowing the introduction of more complex floors or of so-called investment goals. Instead of imposing a lower limit for the total portfolio value, a goal (or cap) restricts the upside potential of the portfolio. It can also be extended to account for a state-dependent risk budget, as opposed to the constant expenditure of the risk budget implied by the basic dynamic core-satellite strategy. We list below several possible floor designs, and we then discuss the option of making a goal part of the investment process.

Capital guarantee floor: this is the most basic expression of a risk budget given by Ft = ke-r(T-t)Ao, where r is the risk-free rate (here assumed to be a constant), k is a constant <1, and A0 the initial amount of wealth. The capital guarantee floor is what is usually used in CPPI.

Benchmark protection floor: this is the basic dynamic core-satellite structure; it protects k% of the value of any given stochastic benchmark: Ft = kBt. In asset management, the benchmark can be any given target (e.g., a stock index). In asset-liability management, the benchmark will be given by the liability value, so At ≥ Ft = kBt is a minimum funding ratio constraint.6

Maximum drawdown (maxDD) floor: extensions of the standard dynamic asset allocation strategy can accommodate various forms of time-varying multipliers and floors. In particular, Grossman and Zhou (1993) have considered a “drawdown constraint” that requires the asset value At to satisfy at all times At >αMt , where Mt is the maximum asset value reached between date 0 and date t: max(As)s<t. In other words, only portfolios that never fall below 100α% of their maximum-to-date value are admitted, for some given constant α. The interpretation is that any drawdown must always be smaller than 1-α. This maxDD floor was originally introduced in an absolute risk management context, but can also be extended to relative risk management by taking: At/Bt >α max(As/Bs)s<t, where Bt is the value of any benchmark.

Trailing performance floor: this floor imposes a constraint that a portfolio shall avoid negative performance over a twelve-month trailing horizon, regardless of the performance of equity markets. More formally, it is given by Ft = At-12, where At-12 is the portfolio value twelve months ago. Again, this constraint can be extended to relative risk budgets by taking, for example: Ft = Bt-12.

2. Background

6 - On this subject we refer the reader to Martellini and Milhau (2009).



Conventional strategies consider the floor but ignore investment goals. Goal-directed strategies recognise that an investor might have no additional utility gain once a total wealth Gt beyond a given goal is reached. This goal, or investment cap, may be constant; it may also be a deterministic or a stochastic function of time. Goal-directed strategies involve optimal switching at some suitably defined threshold above which hope becomes fear (Browne 2000).

It is not immediately clear why any investor would want to impose a strict limit on upside potential. But the intuition is that by forgoing performance beyond a certain threshold, where the relative utility of greater wealth is lower, investors benefit from a decrease in the cost of downside protection. In other words, without a performance cap or goal, investors run a higher risk of missing an already almost reached investment goal.

The presence of a goal can be accommodated by a strategy in which the fraction invested in the performance-seeking satellite is a multiple m2 of the distance to the goal, while the presence of the floor can be accommodated by a strategy in which the fraction invested in the performance-seeking satellite is a multiple m1 of the distance to the floor. If, in addition, one defines the threshold wealth (denoted by Tt) under which the investor shifts from a goal-oriented focus to a risk-management focus, one obtains a piece-wise dynamic allocation strategy, with the threshold Tt to ensure smooth-pasting. The aforementioned floors (capital guarantee, benchmark protection, maxDD, trailing performance, etc.) have equivalents in goals.

In this section, our aim was to show that core-satellite portfolio management allows a coherent structure of investment decisions. The core portfolio makes it possible to meet the investor’s long-term risk/return objectives, while the satellite portfolio provides access to upside potential.

Dynamic allocation will then allow a systematic increase in exposure to the satellite portfolio when it does well, while controlling risks by shifting to the core when the satellite does poorly. As a result, the approach allows asymmetric tracking error management: underperformance of the benchmark will be limited and the investor will have access to the excess returns potentially generated by the satellite, which can be managed actively or invested in a market segment that tends to have higher average returns than the benchmark. Finally, we have provided a list of possible extensions of the basic version of the dynamic core-satellite approach.

This approach, of course, has a wide variety of applications. Different kinds of floors or inclusion of goals make possible strategies that meet particular requirements. The inclusion of a maximum drawdown constraint, for example, is of particular interest to open-ended funds, since it diminishes the investor’s dependence of holding period performance on the entry point. Thus, asset managers can use maximum drawdown constraints to satisfy the needs of investors who enter and exit the fund at different times. The trailing performance floor is particularly useful for absolute return products, where the investor expects the probability of losing money over any one year period to be extremely low. In addition, this dynamic risk management can be applied to

2. Background



absolute wealth risk or relative performance risk, reflecting liabilities or investor objectives.

We now turn to some examples that illustrate how ETFs can be used in core-satellite investing.

2.2.2. Examples of Core-Satellite Asset Allocation Based on ETFsWe now turn to applications of core-satellite allocation using equity and fixed-income ETFs. The first example deals with relative risk management, while the second extends the approach to the design and management of absolute return funds. The third compares the dynamic core-satellite approach and an actively managed portfolio strategy, and presents ways to combine both approaches. Finally, the fourth example shows how investors can use the dynamic core-satellite technique in an opportunistic way to benefit from the return to “normal” values of credit spreads, while keeping risk under control.

Managing relative risk: optimal packaging of small-cap exposureIn this first example, we consider a core-satellite portfolio in which the core is made up of the Euro Stoxx 50 index and the satellite of the Euro Stoxx small-cap index. This configuration allows us to illustrate how to manage the risk that the small-cap stocks will underperform the large-cap Euro Stoxx 50. Since small-cap stocks have historically outperformed large stocks over long periods (albeit at higher risk), periods of underperformance of small-cap stocks may be characterised as an anomaly in the small-/large-cap spread.

We consider monthly return data from January 1994 to December 2008 for these

two equity indices. The starting date is limited by the availability of the data for the small-cap index. Exhibit 2.9 shows that small-cap stocks did not outperform their large-cap counterparts over the period we examine. A portfolio that is long small caps and short large caps would be valued at less than EUR 80 at the end of 2008 for EUR 100 invested at the start of 1994. Looking at the evolution of the value of this long-short portfolio, we see that small caps underperformed until 2000 then kept pace with large caps and finally rallied in 2002. This rally ended in 2007, when small-cap stock again underperformed through to December 2008.

Exhibit 2.9: Small-cap minus large-cap spread, cumulative returns, 1994-2008

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Our objective is to see how the dynamic risk budgeting described above fares in such market conditions. The parameters we use are a multiplier of m=5 and a relative guarantee of k=0.90. Consequently, the initial allocation to the satellite is 50% of the overall portfolio. Instead of using the simple rule for the floor Ft = kBt

(Bt denoting the benchmark), we rely on the dynamic rule for the floorFt = kPt-12 Bt/Bt-12, as presented in the insert on the relevant floors. This specific floor makes it possible to decrease the dependence of the results on the entry point, as discussed above. Exhibit 2.10 shows the risk and return statistics for the two ETFs and for the dynamic risk

2. Background



budgeting approach for the period. In addition, the table shows the same statistics for a static 50-50 mix of the core and the satellite.

Exhibit 2.10: Risk and return statistics for different portfolios in the small-cap example

As exhibit 2.10 shows, the fixed-mix portfolio that at each period invests 50% in the core and 50% in the satellite yields an annualised rate of return of 3.3%. With the dynamic core-satellite technique, the investor obtains an annualised average return of 5.47%, thus reaping more than 200 basis points per annum more than in the static allocation. Most active managers would be envious of outperformance of this magnitude. In addition, the average performance of the DCS is better than that of the core or the satellite, and risk is lower. This outperformance is achieved with sophisticated packaging of small-cap exposure, not with naïve static exposure.

Exhibit 2.11 shows the changes in the allocation to the core (a large-cap ETF) and the satellite (a small-cap ETF) in the dynamic core-satellite strategy. Over the first half of the period, the allocation to small caps gradually decreases, as small-cap stocks underperform the large-cap benchmark. Starting in 1998, the dynamic core-satellite strategy increases the allocation to small-cap stocks, as the satellite outperforms and the cushion increases. In 2006, the allocation to the satellite decreases again, reflecting poor small-cap performance. This increased

allocation in times of outperformance, combined with downside protection in times of underperformance, accounts for the relatively good performance of this strategy.

Exhibit 2.11: Changes in allocation in the small-cap example

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Managing absolute risk: designing absolute return funds with ETFsAbsolute return funds have seen widespread growth in the asset management industry in recent years. These funds claim to provide relatively smooth returns with limited risk. Not unlike hedge funds, absolute return funds, as their name suggests, seek absolute returns; they are not benchmarked to a market index or peer group.

While there is no unique definition of the concept, most investors interested in so-called absolute return strategies have two main expectations, one having to do with performance management and the other with risk management. In other words, investors interested in these strategies have a performance target (usually expressed as a multiple of a cash rate or as a constant target) that they expect to hit regardless of market conditions, and these investors expect to avoid large drawdowns (with a maximum drawdown set at 10% in the example that follows). In an absolute return product, the investor also expects the probability of losing money over any one year period to be extremely low. Our

2. Background

January 1993 – December 2008

Average return*

Maximum drawdown

Volatility*Sharpe ratio*/**

Large-cap core 3.63% -61.60% 19.12% 0.09

Small-cap satellite 2.68% -59.28% 18.29% 0.04

Static core-satellite 3.30% -54.76% 17.95% 0.07

Dynamic core-satellite 5.47% -52.76% 17.96% 0.19

* annualised statistics; ** risk-free rate fixed at 2%



assumption is that on twelve-month trailing performance, an absolute return product will avoid posting negative performance, however the equity market performs. This constraint can be accommodated with a twelve-month trailing performance floor.

We take the following ingredients. First, we specify a maximum drawdown equal to 10%, a twelve-month trailing performance floor, and a soft landing objective with respect to a performance cap (investment goal) set at 2.5 times the cash rate.7 Second, we implement a dynamic core-satellite allocation process with the core invested in a bond ETF and the satellite in a large-cap stock ETF (both in the euro zone), with a maximum allocation set at 50%.

More precisely, we combine a core that invests in medium-term bonds (EuroMTS for bonds with three to five years to maturity) and a satellite that invests in an ETF on the Euro Stoxx 50 index. The objective of the proposed strategy is to optimise returns while limiting the drawdown risk of the portfolio to 10%. The intuition behind the maximum-drawdown constraint is that the investment in the risky asset depends not only on the risk aversion but also on the margin for error.8 When the risk budget is spent, one should be prepared to move away from the risky asset. The idea is to benefit from the returns on the stock market ETF if stocks outperform bonds, while securing protection from the downside risk of the equity investment.

The data used consists of monthly returns including reinvestment of coupon or dividend payments for the period from January 1999 to December 2008. The starting period is different from the

application above because bond data is available starting only with the introduction of the euro, as is usual for euro-denominated bond indices.

The strategy for this form of absolute return fund is, of course, one of many possible means of meeting the objectives of absolute return investors. The dynamic core-satellite strategy, in short, is flexible enough to design a broad variety of investment strategies.

Exhibit 2.12 shows the cumulative returns of the strategy we implemented, as well as of the core and the satellite portfolios. In addition, to highlight the built-in protection of this investment strategy, the floor is displayed as well.

Exhibit 2.12: Absolute return fund: change in the core, the satellite, the floor and the dynamic core-satellite portfolio

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We can draw a number of conclusions from this figure. The dynamics of the core portfolio confirm the conservative character of the core investment. However, we also see that performance of the bond core was quite flat over extended time periods, such as from 1998 to 2000 or from 2004 to 2006. The returns of the equity ETF in the satellite portfolio were, by contrast, negative over the entire period. The fluctuations in the value of the large-cap equity ETF in the satellite are tremendous, with a sharp increase before 2000 and steep falls from 2000 to 2002 and in 2008. The dynamic core-satellite (DCS) portfolio combines the advantages of

2. Background

7 - For a detailed discussion of this floor and the various investment goals, please refer to the end of section 2.2.18 - These strategies were introduced by Ested and Kritzman (1988), who labelled them “time invariant portfolio protection strategies” (TIPP), and later formalised by Grossman and Zhou (1993) and Cvitanic and Katzas (1995).



each of its ingredients, namely the smooth performance of the bond core with the upside potential of the equity satellite. As a result, performance is smooth over the entire period, and cumulative returns at the end of the period are actually higher than those of both the core and the satellite. The graph also shows the dynamics of the floor, which reflects the degree of protection. It is also instructive to look at the performance in the stock market downturn beginning in the year 2000. In fact, the dynamic core-satellite portfolio is largely unaffected. As the portfolio value approaches the floor, the allocation shifts to the core. This behaviour is also illustrated in exhibit 2.13, which shows the weights held in the satellite portfolio over time.

Exhibit 2.13: Absolute return fund: changes in the allocation to the satellite

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The objective of the strategy analysed here should be kept in mind. The conservative nature of the core and the dynamic risk management process are meant to result in smooth returns. Exhibit 2.14 shows the returns over rolling periods of one year. We see that the DCS portfolio achieves positive returns over most rolling windows of one year. Even for the most recent observations of trailing returns, the strategy generates positive numbers, unlike the satellite, which, for the same periods, posts returns worse than -40%. In fact, the behaviour of the DCS portfolio is close to that of the defensive bond portfolio that makes up the core.

Exhibit 2.14: Absolute return fund: performance of the core, the satellite and the DCS over a one-year rolling period

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Risk and return statistics for the dynamic core-satellite strategy confirm the conclusions from the figures analysed above. In particular, exhibit 2.15 shows that the average return exceeds that of the core by almost 200 basis points, while keeping risk at lows similar to those of the defensive core.

Exhibit 2.15: Absolute return fund: risk and return statistics for the core, the satellite and both static and dynamic core-satellite investments

December 1998 – December 2008

Average return*

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Core 4.44% -3.08% 2.61% 0.94

Satellite -0.99% -59.90% 19.46% -0.15

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DCS 6.41% -3.11% 3.83% 1.15

* annualised statistics; ** risk-free rate fixed at 2%

Structuring portfolios to benefit from predictive skills of active managersWe have seen that dynamic risk budgeting can ensure sound absolute return management. What is remarkable is the absence of reliance on prediction. Systematic allocation based on past values of the core and satellite portfolios means that the investor bears no forecasting risk. Of course, investment houses may have access to proprietary forecasts that they may wish to use to move the allocation between risk-free and risky assets. In fact, an asset manager may well wish to benefit from his forecasting skill.

2. Background



Forecasts may be generated in many different ways, including econometric models and qualitative analysis. It is not our objective here to consider how forecasts are best generated. But once they are, a crucial question is how to translate them into portfolio decisions. Obviously, the weight of the satellite in any forecast-based core-satellite portfolio will increase when the satellite is expected to outperform the core. We consider two ways of translating these forecasts into action. We look first at forecasts used in a standard tactical asset allocation approach that simply increases the allocation to the satellite to a fixed weight when it is expected to outperform and resets it to the lower weight when it is expected to underperform. We then look at whether the manager could actually benefit from using such forecasts on the outperformance of the satellite in the dynamic core-satellite approach. We turn now to these two examples.

The performance of forecast-based investment depends, of course, on the accuracy of the forecasts. If the forecast is right most of the time, the portfolio should perform well. In this section, we assess the performance of a manager with varying degrees of positive prediction skill.

The detailed setup of the analysis is as follows: we simulate an active manager’s approach with the following assumptions:• If the manager thinks the core will outperform the satellite in the following month he will allocate 100% of the portfolio to the core.• If the manager thinks the satellite will outperform the core in the following month he will allocate 50% of his portfolio to the satellite. The remaining 50% is allocated to the core.

• The manager rebalances his holdings monthly.• We assume that the manager has positive forecasting skill, that is, that he correctly forecasts satellite outperformance over a month at least seven times a year. In other words, we assume a hit ratio of at least 7/12. We look at hit ratios ranging from 7/12 to 11/12.

To assess the performance of this approach, we simulate 1,000 scenarios for the period from January 1999 to December 2008. The investments used in the core and satellite correspond to the previous example, i.e., we use a defensive euro government bond portfolio in the core and a large-cap equity satellite. Each scenario corresponds to a time series of returns for the active manager, given his bets. Thus, the 1,000 scenarios represent the returns obtained by 1,000 hypothetical active managers who have a given hit ratio.

Exhibit 2.16 shows risk and return statistics for these scenarios. Since every scenario represents the returns of a hypothetical manager, the average for expected return and for maximum drawdown over all scenarios corresponds to the result for the average active manager according to our hypothetical hit ratios.

An obvious result from exhibit 2.16 is that higher hit ratios lead to higher average expected returns. But the table also shows that the average for the maximum drawdown statistic computed across the 1,000 hypothetical managers is relatively high even in the presence of positive forecasting skill. For a hit ratio of 7/11 maximum drawdown is approximately -13% on average. This shows the impact of poor forecasts. In fact, even though these

2. Background



managers are right most of the time, they err five months a year, thus exposing the investor to significant downside risk.

The average value of risk and return statistics across 1,000 scenarios does not show the impact of manager-selection risk. Using a single manager leads to uncertainty, as results may be much better or much worse than the average across 1,000 managers. First, the results obtained by a single manager depend on the actual hit ratio displayed over the sample period as opposed to his true long-term forecasting ability. Second, given a realised hit ratio, portfolio performance depends on the consequences of his correct or wrong predictions. Predicting outperformance over a month during which the satellite underperforms by 1% is not the same as predicting outperformance over a month during which it underperforms by 10%, even though both are instances of forecast error. Likewise, predicting outperformance over a month during which the satellite outperforms by 10% is more valuable than predicting outperformance over a month during which it outperforms by 1%, though both are instances of forecast accuracy.

This dispersion of the managers with the same forecasting ability is shown in the lower panel of exhibit 2.16. The worst performing manager (or scenario) draws down a maximum of between -28% to

-16% depending on the hit ratio we assume. Likewise, the worst return over a one-year rolling period ranges from -23% to -13% depending on the hit ratio. So it is clear that relying on active forecasting leads to additional risk, even if the manager is known to have positive forecasting skill.

The severe drawdown shown even for managers with positive forecasting skill underscores the inability of these tactical allocation strategies to provide absolute return portfolios with smooth return profiles. Even with extremely high and clearly unrealistic hit ratios of 11/12 maximum drawdown is considerably higher than in the absolute-return portfolio based on the dynamic core-satellite approach we described above. So risk control can reduce risk more than forecasting ability can.

One naturally wonders if it is possible to combine the return potential of forecasting and downside risk management that would mitigate the high figures for maximum drawdown. As it happens, it may be possible by making the active manager’s forecasting ability an integral part of the dynamic core-satellite investment. We will thus condition the dynamic core-satellite strategy on the return forecasts for the satellite, all while respecting the dynamic risk budget used in the examples above.

2. Background

Exhibit 2.16: Forecast-based standard tactical allocation: this table shows results for the tactical asset allocation strategy that shifts allocations based on forecasts of outperformance of the satellite. Results are shown for different hit ratios of forecasts, based on a simulation of 1,000 scenarios.

Hit ratio 7/12 8/12 9/12 10/12 11/12

Average expected return 5.68% 8.01% 10.48% 12.91% 15.49%

Average maximum drawdown -13.24% -10.57% -8.49% -6.66% -4.52%

Worst maximum drawdown -28.09% -25.21% -22.92% -19.03% -16.17%

Worst performance over a rolling one-year period

-22.91% -22.07% -18.38% -15.76% -12.55%



Since the main objective is to reduce the drawdown statistics that result from the errors made by skilled forecasters, we impose a maximum drawdown of 10%. Next, we incorporate the manager’s forecasting ability by introducing a time-varying multiplier m. If the manager expects the satellite to outperform the core, the multiplier is set to m=5, thus allowing a considerable fraction to be invested in the equity satellite. If the manager expects the satellite to underperform the core, the multiplier is set to m=0. So the portfolio is fully protected from the expected negative performance of the satellite.

As before, we simulate 1,000 scenarios to assess the average performance of this risk-controlled strategy. The results (exhibit 2.17) show the benefits of using dynamic core-satellite management to limit the extreme drawdown induced by forecast error.

Again, active management provides high returns that evidently increase as forecasting ability (the hit ratio) improves. However, the approach that makes forecasts part of a DCS approach manages downside risk much better; for a hit ratio of 7/12 the average maximum drawdown is only -7.89%. In the simple tactical allocation strategy, by comparison, the average maximum drawdown is -13.24%. This dynamic risk budgeting makes it possible to

limit the severe drawdown in the standard tactical allocation. This reduction is more pronounced for relatively low ratios. But even with the higher hit ratios it leads to considerable risk reduction. Exhibit 2.18 shows the reduction in maximum drawdown for each hit ratio. Risk control, then, clearly leads to significant benefits.

Exhibit 2.18: Risk reduction: risk controlled forecast-based strategy versus standard tactical allocation strategy. This exhibit shows the reduction of the average drawdown obtained by using the DCS approach to implement portfolios based on predictions of outperformance of the satellite portfolio over a one-month horizon.

Hit ratio 7/12 8/12 9/12 10/12 11/12

Risk reduction (reduction in magnitude of avg. max drawdown)

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The two strategies assume identical forecasting ability. The results demonstrate that the dynamic core-satellite approach reduces the risk of tactical bets based on return forecasts.

This example underscores the benefits of dynamic core-satellite investment even for managers who prefer to rely, as it were, on their crystal balls. Dynamic core-satellite management may also improve the downside risk management of portfolios when an asset manager wishes to use forecasts to make tactical bets. Having thus provided a taste of an application of the dynamic core-satellite approach, we turn to an example relevant to recent market—credit market—conditions.

2. Background

Exhibit 2.17: Forecast-based strategy made part of DCS management: the table shows the performance and maximum drawdown of the strategy that integrates forecasts into a DCS framework. Forecasts are based on a simulation of 1,000 scenarios with various hit ratios.

Hit ratio 7/12 8/12 9/12 10/12 11/12

Average return 5.96% 7.93% 10.19% 12.57% 15.28%

Average maximum drawdown -7.89% -7.45% -6.81% -5.89% -4.32%

Worst maximum drawdown -9.65% -9.56% -9.51% -9.51% -9.04%

Worst performance over a rolling one-year period

-8.57% -8.57% -8.57% -7.96% -7.13%



Benefiting from the return to “normal” credit spreads with core-satellite investingSince the onset of the financial crisis in summer 2007, credit spreads in the euro zone have reached historic highs: with credit defaults or illiquidity looming, corporate bonds must provide much higher compensation than in previous years. The financial crisis may be far from over, but some investors assume that in the long term credit spreads will revert to their historic levels, driving up corporate bond prices.

Investors can exploit this expected trend by investing in a dynamic core-satellite strategy based on ETFs. They can thus profit when the credit spread narrows, but the risk-control mechanism inherent to the dynamic core-satellite strategy protects them from further deterioration in the credit markets. Here, broad corporate bond ETFs are very useful, as they make it possible to profit from an expected narrowing of overall credit spreads, without heightening exposure to the idiosyncratic default risk of individual issues. Hence, we combine a core investment in euro-zone sovereign funds (iBoxx Liquid Euro Sovereign Index) and a satellite investment in corporate bonds (iBoxx Liquid Euro Corporate Index).9 The rationale for this portfolio is to benefit from the historically high credit spreads in the euro zone and the expected return to normal conditions to enhance the performance of a government bond portfolio.

Both bond ETFs we rely on have an average duration of six years; they thus entail interest rate risk (which the investor can easily hedge away). Again, the portfolio is rebalanced monthly. Since the objective

is to take advantage of the return to “normal” of credit spreads, we will implement the following explicit risk management limits: the floor is set to Ft = 0.9 kPt-12 Bt/Bt-12 (see the list of ways to define the floor above), with a multiplier of m=8. Thus, the strategy makes it possible to preserve 90% of the value of the government bond core over a one-year rolling period. We also apply a ratchet effect to lock in profits when the current credit spread is close to its long-term average.

Since the narrowing of credit spreads is only expected, we must simulate future developments in the credit market. To do so, we combine the historical data over the period from January 1999 to January 2008, during which credit spreads hovered around their long-term average, and a simulated linear fall in credit spreads from the current value (206bp) to the historical average (57bp) over a three-year period.

Exhibit 2.19 displays the allocation of the core-satellite strategy over time. As the exhibit shows, the allocation to the satellite—ranging from 60% to 100% over the nine-year simulation—is always relatively high. In the first six years, during which it is assumed that the credit spread will fall (increasing corporate bond prices), allocation to the satellite increases. Over the last three years, this comparative advantage of corporate bonds is no longer present, and the allocation to the satellite falls.

2. Background

9 - This idea can be transposed to an ALM context, either via an overlay program or the choice of underlying assets whose duration matches that of the client liabilities.



Exhibit 2.19: Change in core-satellite allocation in the credit spread example, assuming a falling credit spread over three years.

Exhibit 2.20 takes another look at this strategy by showing the cumulative returns of the dynamic core-satellite strategy, as well as of the core and the satellite portfolios. As a large proportion of the total portfolio is invested in the satellite, cumulative returns of the DCS portfolio and the satellite move very much in tandem. But the returns of the DCS approach are achieved from the narrowing credit spread while checking the downside risk arising from a possible widening of the spread. Since the floor is set with reference to a government bond portfolio, the investor in this strategy has a guarantee that he will not fare considerably worse than the performance of the government bond portfolio in the core. And he will benefit from any recovery in corporate bonds.

Exhibit 2.20: Credit spread example: change in the core, the satellite and the dynamic core-satellite portfolio, assuming a narrowing credit spread over three years.

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Finally, exhibit 2.21 presents the summary statistics of the various portfolios for investment horizons of three and six years, confirming the conclusions from the figures

analysed above. In particular, exhibit 2.21 shows that the average annual returns exceed those of the core by more than 100 basis points a year over the time horizons analysed.

Exhibit 2.21: Risk and return statistics for the core, the satellite, the dynamic core-satellite (DCS), and the difference between the DCS and the core for the credit example, assuming a narrowing credit spread over three years.

Six-year statistics

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Annual volatility

Core 5.29% -4.99% 3.60%

Satellite 6.71% -5.92% 3.61%

DCS 6.51% -5.82% 3.54%

DCS-Core 1.22%

Three-year statistics

Annual return

Maximum drawdown

Annual volatility

Core 3.35% -4.99% 3.58%

Satellite 5.53% -5.92% 4.04%

DCS 5.29% -5.82% 3.93%

DCS-Core 1.95%

The dynamic core-satellite portfolio’s outperformance of the core is not limited to the period of the simulated narrowing of the credit spread. In fact, even after the fall, as it were, the strategy continues to outperform the government bond core. In this scenario, we assumed that credit spreads returned to their average long-run levels over a horizon of three years. However, credit markets could recover over a shorter or longer horizon, recovery could start later, and changes might not be linear. More importantly, perhaps, the credit situation could worsen before it gets better or it may not get better at all. We have analysed how the investment strategy performs in these scenarios. We have also used various parameters in the DCS strategy to assess the robustness of performance with respect to parameter choice. For the sake of brevity, we

2. Background

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2.0%

2.5%

Allocation to satellite Spreads



do not show the full results here. However, we provide an overview of some of the robustness tests we have done. Detailed results are available from the authors on request.

To generate scenarios with different recovery periods, we have simulated a linear fall in credit spreads over a six-, a three-, and a one-year period. We have then observed the behaviour of DCS portfolios with different parameters for the relative level of protection k and for the multiplier m in this context. We also vary the recovery start date, which we let occur at any time at random until the last month while allowing for a full recovery. Another aspect is the shape of the recovery. So we look at various possibilities in which credit spreads do not narrow linearly but widen and narrow irregularly over various periods of recovery. More importantly, we assess scenarios in which the expected recovery does not take place. These results show that the dynamic core-satellite portfolio offers natural protection from the downside of the corporate bond exposure in the satellite. This risk control occurs in much the same way as in the examples on equity risk exposure in the satellite we presented above.

In general, this additional analysis shows that the risk-return characteristics of the example, as well as the qualitative conclusion of outperformance, are robust to changes in such assumptions. This finding stems from the flexibility of the dynamic core-satellite approach, since it continuously adjusts the strategy to prevailing market conditions. So it can benefit from the narrowing of credit spreads in several general scenarios, all while protecting the investor from an even greater widening of the credit spread.

Taken together, the examples presented in this section highlight the potential benefits of using ETFs to gain exposure to several asset classes and the advantages of the core-satellite approach. Although core-satellite investments can be made statically, the illustrations clearly show that they reach their full potential when the allocation to the satellite and the core are adjusted dynamically. The main benefit of this asset allocation strategy is the combination of participation in upside market movements and limited risk exposure. As a result, in many cases dynamic core-satellite strategies offer better risk/return tradeoffs than either core or satellite investments. In addition, maximum drawdown—extreme risk—is limited. We proceed now to the presentation of the survey methodology and data. The main results of the survey—European investors’ views of ETFs, the usage of ETFs, and their comparative advantages and disadvantages—are found in section 4.

2. Background



2. Background

3. Methodology and Data




3.1. MethodologyThe EDHEC European ETF Survey 2009 was taken with an online questionnaire; electronic mail was sent to European professionals in the asset management industry, professionals targeted for their experience with ETFs, among them institutional investors, asset management firms, and private wealth managers.

The questionnaire itself has three parts. In the first part, the survey respondents are asked about the role ETFs play in their asset allocation decisions. The next set of questions turned to practical aspects of ETF investment, such as satisfaction with ETF products and applications of ETFs for portfolio optimisation. In the last set of questions, the questionnaire asks the respondents to compare ETFs and other investment instruments that can be considered close substitutes: index funds, futures, and total return swaps. Finally, we invited the survey respondents to express their views of future developments in the ETF market.

3.2. DataThe email containing a link to the questionnaire was sent in January 2009. The first response was received on 23 January, the last on 26 February.

The 360 respondents1 to the survey are based in Europe, many from France, the UK, and Switzerland (exhibit 3.1). 62% of the survey participants are institutional asset managers, who are the largest professional group represented in this study (see exhibit 3.2). About one-third of the respondents are in private wealth management. The remaining 6% are other professionals in the financial services industry, such as investment bankers or brokers. Investment

and asset management firms account for the largest share of institutional investors covered in this survey (exhibit 3.3). Pension funds, insurance companies and hedge funds account for another 14% of the institutional investors. But each subgroup of institutional investors provides broadly similar responses. Responses from pension funds and insurance companies, for example, are no different from those from other institutional investors, primarily asset management companies.

Exhibit 3.1: Country distribution of respondents

FranceUnited KingdomSwitzerlandGermanyItalyLuxembourgNetherlandsOther EUNon-EUNo answer

25%

20%

7%

17%

5%

4%

3%

14%

3%2%


1 - In total, we received 527 answers to our survey. However, about 32% of respondents reported that they had never invested in ETFs. Since our aim is to include only experienced ETF investors in this survey, we discarded these responses.



Exhibit 3.2: Main activity of respondents

No answerInstitutional investor (pension fund, asset management company, insurance company), institutional investment consultantPrivate wealth managementOther

62%

32%

5%1%

Many of the respondents occupy high-ranking positions: 11% are CEOs, and roughly 30% are chief investment officers (CIOs), chief risk officers (CROs), or heads of portfolio management. Another third of the respondents are portfolio or fund managers (see exhibit 3.4). Finally, exhibit 3.5 shows the assets under management of the companies for which the survey respondents work. As was to be expected, there are a few large firms in the asset management industry that have more than EUR 100bn in assets under management. However, the EDHEC ETF Survey 2009 mainly reflects the views of medium-sized companies, with assets under management of between EUR 100mn and 10bn. 12% of our respondents manage less than EUR 100mn.

Taken together, we believe that this regional diversity and fair balance of asset management professionals make the survey largely representative of European ETF investors.

Exhibit 3.3: Type of institutional investors

Investment/asset managementLife insuranceNon-life insurancePension fundOther

86%2%

4%

7%1%

Exhibit 3.4: Function of survey respondents

Supervisory board memberCEO/managing director/presidentCIO/CFO/treasurerCRO/head of risk managementHead of asset allocation/head of portfolio managementPortfolio manager/fund managerVice presidentAssociate/analystMarketing positionIndependent private wealth managerNo answer

4%

12%

10%

3%

11%

30%

8%

8%

4%

1%

9%




Exhibit 3.5: Assets under management (EUR)

<10mn10-100mn100mn-1bn1-10bn10-100bn>100bn

0

5

10

15

20

25

8.8%

12.5%

22.3%21.7%

19.2%

15.2%


4. Results




In this section, we present the main results of this survey and discuss possible explanations for the respondents’ answers. In the first part, we take a close look at the use of and satisfaction with ETFs. In addition, we invite survey respondents to express their views of future developments in the ETF market. In section 4.2, we compare practitioners’ views of ETFs and their views of investment instruments that can be considered close substitutes: index funds, futures, and total return swaps. Finally, we look into the role of ETFs in asset allocation; our emphasis is on the core-satellite portfolio concept. In the last section, we compare the results of this year’s survey and those of EDHEC’s past ETF surveys.

4.1. Exchange-Traded Funds in Practice

4.1.1. Satisfaction with ETFsWe begin our analysis with a general assessment of the satisfaction with ETF products for different asset classes. Evidently, only those respondents who use ETFs in the respective asset class are asked to report their degree of satisfaction. Exhibit 4.1 shows that, over all asset classes, a large majority of users are satisfied with their ETFs. Satisfaction is most pronounced for equity ETFs (94%) and least pronounced for ETF-like products on hedge funds. Private wealth managers are satisfied with equity ETFs to the tune of an astonishing 98.2%.1 Satisfaction is also very high for commodity ETFs (79%) and government bond ETFs (78%). Satisfaction with corporate bond ETFs, on the other hand, is relatively low; indeed, less than two-thirds of users state that they are satisfied with them. With 55% of users reporting that they are satisfied,

real estate ETFs obtain a satisfaction score that is considerably lower than that of commodity ETFs, but still higher than that of ETF-like products linked to hedge funds.

The reasons for satisfaction or dissatisfaction may be manifold. With corporate bonds, liquidity is naturally an issue and may pose a challenge to ETF providers. In alternative investment classes, except for commodities, for which there is a liquid futures market, liquidity problems may also resurface. In addition, constructing truly representative indices in alternative asset classes may be a challenge, especially when doing so involves attempts to attain the liquidity necessary for the construction of an instrument such as an ETF. Despite the existence of solutions to these problems (see Goltz et al. 2007), current product offerings do not fully satisfy investors, as evidenced by the responses to our questionnaire.

Exhibit 4.1: If you use ETFs or ETF-like products are you satisfied with them?

EquitiesGvt. bondsCorp. bondsCommoditiesReal estateHedge funds

0

20

40

60

80

10094%

78%

61%

79%

55%

28%

4. Results

1 - 90.7% of the institutional investors are satisfied with equity ETFs. The difference (7.5%) from private wealth managers is highly significant (p<0.05, Chi-square test).



4. Results

4.1.2. Replication Methods for Exchanged-Traded FundsThe wide range of ETFs is one of their perceived advantages. Most ETFs are passively managed and replicate indices. More recently, actively managed ETFs have been launched as well. However, as exhibit 4.2 shows, the majority of respondents prefer passive ETFs; active ETFs are preferred by only about 15% of respondents. Active ETFs, which allow immediate trading in actively managed funds, fly in the face of the investment philosophy that would have the manager eschew stock-picking and concentrate on asset allocation. Therefore, the logical application of such funds would be short-term manager selection, not asset allocation.

Exhibit 4.2: What type of ETFs do you prefer?

No answerActive ETFsPassive ETFs

15%

82%

3%

In fact, besides pure replication, passive ETFs may rely on synthetic replication (derivatives written on the index) or statistical replication (using an optimisation procedure to match the returns on the index with a selection of securities that may differ from the index). A large majority of the respondents (76%) who favour passive ETFs express a preference for conventional pure replication

ETFs (see exhibit 4.3). Synthetic replication and statistical replication are still seen as less attractive than full replication. Synthetic replication through derivatives is, however, significantly more popular (preferred by 14% of respondents) than statistical replication (preferred by 8%).

Synthetic replication and statistical replication are usually less costly than pure replication. We did not determine whether these lower costs outweigh preferences for pure replication. Moreover, the relative unpopularity of statistical replication may constitute a potential barrier to the expansion of ETFs in asset classes with low liquidity, where full replication may not be feasible, and where liquid derivatives are not available. On the other hand, the preference of investors for synthetic replication may justify the development of derivatives in various asset classes, since ETF providers could exploit the presence of these products to launch new ETFs.

Exhibit 4.3: Which replication method do you prefer?

No answerPure replicationSynthetic replication (i.e., using derivatives)Statistical replication (i.e., sampling)

2%

76%

14%

8%



4. Results

2 - Only 5.2% of private wealth mangers lend their ETF units compared to 14.7% of institutional investors. This significant difference (p<0.05, Chi-square test) is not a surprise, since securities lending is traditionally the province of institutional investors.

4.1.3. Trading Exchange-Traded FundsOne of the great advantages of ETF is that they are easily traded on conventional stock exchanges. So we ask respondents how much of their ETF trading is done over the counter rather than on exchange (see exhibit 4.4). Many (67%) respondents, as it turns out, do not trade a significant share of their ETF investments over the counter, but 15% of respondents do more than half of their ETF trading on OTC markets.

Exhibit 4.4: How much of you ETF trading is done OTC rather than on exchange?

No answer<10%10% to 25%25% to 50% 50% to 75%75% to 90%>90%

7%

67%

3%

9%

6%

2%

6%

Next, we turn to the number of counterparties used when trading ETFs. On average, respondents use about three counterparties, with their mean at 2.9 and their median at 3 (see exhibit 4.5). Roughly 50% of the participants have up to two counterparties, and another 46% have three to five counterparties. Only 5% rely on more than five counterparties.

Exhibit 4.5: How any conterparties do you use when trading ETFs?

Less than 223From 4 to 5From 6 to 78 and more

25%

24%26%

20%

2%3%

4.1.4. Advanced Exchange-Traded Fund Products and Use of ETFsAs mentioned in section 2.1.6, ETFs stand out for a number of more advanced features. Exhibit 4.6 summarises how these features are used by European investors and asset managers. We ask in particular about the use of inverse ETFs, options written on ETFs, short selling of ETFs, and the use of ETF shares in securities lending.

One can draw several main conclusions from this chart. First, the majority of respondents do not take advantage of these additional features of ETFs; indeed, most of these advanced features are used by only 6% to 14% of respondents.2 The notable exception is inverse ETFs, used by 28% of respondents. Unfamiliarity with the advanced uses of ETFs is not a reason for the neglect of these products: no more than 10% of respondents report that they are unfamiliar with any one of these features.



4. Results

3 - See Engle and Sarkar (2006) for an application to the tracking quality of ETFs.4 - Please refer to box in section 2 for more discussion of the Acharya and Pedersen (2005) liquidity measure.

We also ask whether those who do not currently take advantage of these advanced features of ETFs intend to do so in the future. We do not include a graph for these results, which can be summarised as follows: inverse ETFs are the most attractive to investors. Some 20% of all respondents who are not using inverse ETFs intend to do so in the near future. Options on ETFs, shorting ETFs and ETF lending, by contrast, are likely to remain virgin territory to ETF investors. Fewer than 10% plan to engage in these practices in the near future.

Exhibit 4.6: Advanced uses of ETFs

YesNoNot familiar with this practiceNo answer

0 10 20 30 40 50 60 70 80

28%62%

6%4%

6%79%

10%5%

14%74%

6%6%

12%73%

7%8%

Do you inverse ETFs (short ETFs) as a

hedging tool?

Do you use options on ETFs?

Do you short ETFs yourself?

Do you lend your ETF units?

4.1.5. Measuring Tracking Error and LiquidityAs noted in section 2.1.7, tracking error and liquidity are the two most important means of evaluating the quality of ETFs and other indexing vehicles. In this section, then, we look at how respondents measure tracking error and liquidity.

Exhibit 4.7 shows that respondents (73%) generally use standard measures of tracking error, such as the difference between the returns on the instrument and those on the index, the correlation of the two assets (44%), or simply the comparison of mean returns (23%). That 11% of respondents express no opinion on this issue may be the result of the

assumption that the tracking quality of these instruments is good. Interestingly, some respondents use quite advanced measures of tracking error such as the asymmetric or downside tracking error (Amenc et al. 2004) or co-integration analysis.3

Exhibit 4.7: Which method do you use to assess the tracking quality?

0 10 20 30 40 50 60 70 80

73%

44%

23%

11%

9%

6%

2%

4%

Tracking error analysis

Correlation analysis

Comparison ofmean returns

I do not knowAsymmetric tracking

error analysis

Co-integration analysis

Other

No answer

The second key issue with indexing instruments is liquidity. Although practitioners are highly familiar with liquidity measures, the finance literature has yet to come to a consensus on theory and on empirical methodology.

Survey respondents rely largely on market spreads (73%), turnover (46%) and assets under management (44%) as measures of liquidity (see exhibit 4.8). However, a significant percentage of respondents (18%) rely on the co-movement of liquidity in the instrument and the returns on the index, as proposed by Acharya and Pedersen (2005).4

Exhibit 4.8: Which method do you use to assess the liquidity?

0 10 20 30 40 50 60 70 80

73%

46%

44%

18%

7%

4%

3%

Market spreads

Turnover

AUM

Co-movement of liquidity and returns(representing concern over low liquidity in "bad times")

I do not know

Other

No answer



4.1.6. Future Development of ETFsIn a final set of questions in this section on ETFs we ask survey participants to identify the ways they are likely to use ETFs in the future and the products they would like to see developed.

First, we ask those surveyed to identify the area in which they predict the greatest increase in the use of ETFs. Exhibit 4.9 shows that the greatest increase (chosen by 41% of the respondents) is expected in the area of accessing new asset classes. The other main areas in which they expect increased use are optimal portfolio construction (31%) and risk management/hedging (17%). An increase in using ETFs for cash equitisation is predicted by only 6% of respondents. These predictions seem to justify ETF providers’ coverage of new asset classes such as listed real estate, listed private equity, and commodities.

Exhibit 4.9: In which area do you predict the greatest future increase in your use of ETFs?

No answerExposure to new asset classes through ETFsConstructing optimal portfolios of ETFsHedging and risk management with ETFsCash equitisation with ETFs

5%

41%31%

17%

6%

Second, we turn to ETF products that European investors and asset managers

would like to see developed. As shown in exhibit 4.10, emerging market equity ETFs (47%) are the top concerns of respondents. Some one-third of respondents would like to see new products for alternative asset classes, especially in commodities (35%), currencies (30%), and hedge funds (28%). With 34% of respondents, ETFs based on new forms of indices are also high on the wish-list. Unlike standard market-capitalisation-weighted indices, these indices are weighted either equally or by fundamental company characteristics (Arnott et al. 2005; Amenc et al. 2009). Emerging market bond ETFs are also in demand (29%). New products in ethical investment (18%) and actively managed equity (14%), by contrast, are not in such high demand.

Exhibit 4.10: What type of ETF products would you like to see developed futher in the future?

0 10 20 30 40 50

18%

14%

31%

25%

34%

35%

30%

21%

28%

29%

47%

Ethical ETFsActively managed equity ETFs

High yield bond ETFsEquity style ETFs

ETFs based on new forms of indices

Commodity ETFs

Currency ETFs

Real estate ETFs

Hedge-fund-like ETFs

Emerging market bond ETFs

Emerging market equity ETFs

4.2. The Pros and Cons of ETFs, Futures, Total Return Swaps, and Index FundsIn this section, we compare four investment instruments that allow the simple execution of trades in large baskets of stocks: ETFs, futures, total return swaps (TRSs), and traditional index funds. Our criteria for evaluation are based loosely on Rubinstein’s (1989) early examination of such instruments. We first look at the advantages and

4. Results



disadvantages of each instrument, and then at the respondents’ views of each of these products. In addition, an assessment of the future use of these instruments by European institutional investors and asset managers enables us to shed light on developing trends.

4.2.1. Comparing ETFs and Their AlternativesWe ask survey respondents to rate ETFs, futures, total return swaps, and index funds according to various criteria. The responses—described in more detail below—make possible a few general conclusions. First, in terms of liquidity, transparency, and cost, ETFs are considered advantageous, although on some criteria they are less well regarded than futures. Second, ETFs are viewed as making available the widest range of indices and asset classes. So European investors and asset managers seem to be well aware of the diversity of ETFs, diversity that has grown apace in recent years. Third, futures are the most serious alternative to ETFs, but ETFs are perceived as superior with respect to the minimum subscription, operational constraints, and the relevant tax and regulatory regimes. So it would seem that implementation concerns with futures (such as margin calls, and making exact allocations even for small portfolios) give ETFs an advantage. Fourth, the respondents believe that ETFs perform much better than total return swaps on each criterion.5 One should of course bear in mind that a high percentage of survey respondents do not answer all questions. Rather than adjusting the rankings for non-responses, we indicate the percentage of non-responses, since this allows us to draw conclusions as to the familiarity of

survey participants with each instrument or evaluation criterion.

As exhibit 4.11 shows, 59% of respondents believe that futures are very good in terms of liquidity and almost no respondents state that liquidity is “poor”. The liquidity of ETFs ranks second; it is viewed as very good by 48% of respondents and poor by 6%. By contrast, only 25% of respondents view index funds as very liquid. The liquidity of total return swaps, viewed as the least liquid of these instruments, is considered very good by a mere 9% of respondents. These results show that respondents value the ability to trade immediately with futures and ETFs, an option not provided by standard index funds.

Exhibit 4.11: Respondent views of liquidity

0 10 20 30 40 50 60

3%48%

43%6%

18%59%

21%3%

31%9%

35%25%

18%25%

48%9%

ETFs

Futures

Total return swaps

Index funds

No answerVery goodFairly goodPoor

Survey respondents express opinions of the cost of liquidity that are rather similar to their opinions of liquidity. Exhibit 4.12 shows that by this criterion futures are held in highest esteem (49% judging them very good), followed by ETFs. Index funds and TRSs are viewed as costly.

4. Results

5 - This belief seemingly conflicts with that expressed by Lhabitant et al. (2006), who concluded that indexation with derivatives provides better performance than ETFs and that, when considering both costs and tracking error, swaps are the most efficient mechanism for tracking an index. These conflicting beliefs may be explained, to some extent, by a lack of familiarity with total return swaps, as a considerable share of respondents do not answer this particular question. Even among those who do, however, total return swaps are not considered superior.



Exhibit 4.12: Respondent views of the cost of liquidity

0 10 20 30 40 50 60

5%26%

58%10%

21%49%

26%3%

33%6%

36%25%

22%17%

44%16%

ETFs

Futures

Total return swaps

Index funds


Exhibit 4.13 shows that most respondents think that ETFs incur the lowest costs and other fees. 87% judge ETFs either very good or fairly good in terms of costs; only 9% consider them poor. All the same, by the percentage (48%) of respondents who view their cost advantage as very good, futures take the top spot. Total return swaps are viewed as very good by only 9% of respondents, but a sizeable fraction of investors (20%) think they are poor in terms of cost. Similarly, index funds do not do as well on cost as ETFs and futures. The evaluation of the costs of these instruments is, of course, specific to the context in which they are used. Our respondents’ preferences for ETFs and futures may be accounted for by their large position sizes, as indicated by the average amount of assets under management they report.

Exhibit 4.13: Respondent views of other costs (fees, expenses)

0 10 20 30 40 50

4%41%

46%9%

21%48%

25%6%

33%9%

38%20%

22%14%

40%24%

ETFs

Futures

Total return swaps

Index funds


Only a marginal percentage of respondents believe that the reliability of the tracking error of these four instruments is poor (see exhibit 4.14). On this score, high percentages of respondents view both ETFs and futures as very good. However, ETFs garner more total fairly good and very good responses than do futures. Total return swaps and index funds bring up the rear. Nonetheless, these products get rather good marks, as only 7% indicate that they are poor in terms of tracking error. However, a sizeable fraction of respondents express no opinion of the tracking error quality of these instruments.

Exhibit 4.14: Respondent views of the tracking error quality

0 10 20 30 40 50

4%42%

50%5%

22%43%

31%4%

32%31%

30%7%

21%26%

46%7%

ETFs

Futures

Total return swaps

Index funds


4. Results



48% of respondents state that the ETF product range is very good (see exhibit 4.15). This finding is consistent with recent developments in the ETF industry, developments intended to offer exposure to a wide range of indices (Demaine 2002). The product range of futures is less well diversified, but this range is viewed as very good by 21% of respondents; only 15% to 16% of the respondents believe that TRSs and index funds have very good product ranges.

Exhibit 4.15: Respondent views of the available product range

0 10 20 30 40 50

6%48%

42%4%

23%21%

45%11%

34%16%

37%13%

23%15%

50%12%

ETFs

Futures

Total return swaps

Index funds


As exhibit 4.16 shows, very few respondents (6%) believe that the transparency of ETFs or futures is poor. On this score, however, total return swaps are considered poor by 23% of respondents and index funds by 10%. Futures (considered very good by 56% of respondents) and ETFs (47%) are preferred to index funds (considered very good by 24%) and total return swaps (12%). However, taking positive opinions together, ETFs outscore futures (90% versus 74%).

Exhibit 4.16 Respondent views of the transparency

0 10 20 30 40 50 60

5%47%

43%6%

21%56%

18%6%

31%12%

34%23%

22%24%

44%10%

ETFs

Futures

Total return swaps

Index funds


ETFs are clearly the preferred instrument when it comes to the minimum subscription requirement (see exhibit 4.17). They are considered very good by 73% of respondents and poor by only 3%. The positive views of ETFs are to be compared with the views of futures, which are considered very good by only 22% of respondents but poor by 16%. In fact, futures rank even slightly behind traditional index funds (viewed as very good by 30% of respondents). Total return swaps were viewed with the greatest degree of dissatisfaction (poor) by the highest percentage of respondents (39%).

Exhibit 4.17: Respondent views of the minimum subscription

0 10 20 30 40 50 60 70 80

5%73%

20%3%

21%22%

42%16%

32%4%

25%39%

22%30%

39%8%

ETFs

Futures

Total return swaps

Index funds


4. Results



As exhibit 4.18 shows, ETFs are deemed less susceptible to operational constraints than the other three instruments. Indeed, more than half of our respondents believe that ETFs are very good in terms of such constraints and 38% consider them fairly good. Traditional index funds and futures are ranked behind ETFs, with about 26% to 30% of respondents seeing them as very good, and 37% to 38% seeing them as fairly good. Here again, then, respondents have slightly more positive views of index funds than of futures. TRSs are clearly perceived as the instrument most susceptible to operational constraints, with 35% of respondents viewing them as poor. Hence, the answers to this question confirm markedly differing views of exchange-traded (futures) and over-the-counter derivatives (swaps).

Exhibit 4.18: Respondent views of the operational constraints

0 10 20 30 40 50 60

7%51%

38%3%

23%26%

37%14%

33%5%

28%35%

24%30%

38%8%

ETFs

Futures

Total return swaps

Index funds


Exhibit 4.19 shows that respondents prefer ETFs (45% view them as very good) when it comes to the regulatory regime. Only 5% deem them poor on this criterion. Interestingly, ETFs also receive the fewest non-responses, suggesting respondent familiarity with the applicable regulatory regime. Likewise, index funds and futures are mostly regarded positively. However, there is considerable discontent with the

regulatory framework for total return swaps. Only 4% of respondents rate it as very good and 33% as fairly good; 29% rate them as poor.

Exhibit 4.19: Respondent views of the regulatory regime

0 10 20 30 40 50

8%45%

42%5%

23%36%

35%6%

33%4%

33%29%

25%33%

38%4%

ETFs

Futures

Total return swaps

Index funds


Finally, the majority of respondents (81%) consider the tax efficiency of ETFs either very good or fairly good; for futures and index funds, the figure is 64% (see exhibit 4.20). The tax regimes for ETFs and futures are rated very good by similar percentages of respondents (26% to 27%). Total return swaps receive fewer total positive responses (52%) than the other instruments. This difference is affected by a fraction of non-responses that varies from instrument to instrument; these non-responses may be the result of the relative unfamiliarity of respondents with these instruments. The percentage of respondents (around 10%) who believe that the tax regime to which these products are subject is poor is, however, constant.

4. Results



Exhibit 4.20: Respondent views of the tax regime

0 10 20 30 40 50 60

11%27%

54%9%

27%26%

38%9%

37%14%

38%11%

27%16%

48%9%

ETFs

Futures

Total return swaps

Index funds


4.2.2. Specific Issues Related to Investment in Total Return Swaps, Futures, and ETFsTotal return swapsConsidering responses across all criteria, we find, broadly, that total return swaps are viewed more poorly than the other three instruments. In addition, TRSs are the object of the highest percentage of non-responses, suggesting that respondents are relatively unfamiliar with them. Our survey addresses two specific issues with TRSs: the requirement for over-the-counter trading and the associated counterparty credit risk.

As exhibit 4.21 shows, trading over the counter is problematic for the majority of respondents (60%). They view counterparty risk (76%) as an even greater problem. The prominent failures of the last twelve months provide ample justification of this fear.

Interestingly, the percentage of non-responses (around 12%) to these specific questions is significantly lower than those to the more general questions above, where non-responses on TRSs come,

on average, to approximately 33%. These figures suggest that respondents do not consider the advantages and disadvantages of TRSs in detail because counterparty risk and OTC transactions dissuade them from using them in the first place.

Exhibit 4.21: Concerning total return swaps…

12%

60%9%

29%

13%

27%76%

11%

Is the fact that they are traded OTC

a problem for you?

Is the counterparty risk a problem

for you?

No answerYesNo

0 10 20 30 40 50 60 70 80

FuturesIn a direct comparison of all four instruments, futures fared remarkably well and can be viewed as the greatest rival to ETFs in implementing indexing strategies. A drawback of futures is that they are derivative instruments, require roll-over transactions, and involve margin calls. In addition, as exhibit 4.18 shows, the operational constraints of futures are considered more burdensome than those of ETFs.

When asked directly, respondents report that margin calls (39%) and the roll-over of positions (30%) are problematic for them (see exhibit 4.22). That futures are derivatives is seen as problematic by only 27% of respondents. Overall, the significant percentage of respondents seeing margin calls and position-rolling as problems jibes with the relatively jaundiced view of the operational burdens futures entail. On the other hand, many respondents do not see margin calls and position-rolling

4. Results



as problems, a finding that strengthens the perception of futures as a possible alternative to ETFs.

Still, it is interesting that investors perceive margin calls as the main problem of futures. The financial crisis may again be the reason: the combination of high market volatility and dried up liquidity makes it harder to supply the cash to meet the margin calls.

Exhibit 4.22: Concerning futures…

0 10 20 30 40 50 60 70

10%

63%

27%

11%

39%

10%

30%

60%

50%

Is the fact that theyare derivative instruments

a problem for you?

Are margin calls a problem for you?

Is the requirement to roll over positions a problem

for you?

No answerYesNo

Exchange-traded fundsWe then ask respondents for their opinions of pricing errors with respect to the net asset value (NAV) of the ETF and of the advantages of securities lending. Possible mispricing with respect to NAV was of concern to 57% of respondents (see exhibit 4.23). This finding is somewhat surprising, as Engle and Sarkar (2006) find that the premiums or discounts on fund NAVs are typically small and disappear very quickly (see background). It may be that the respondents to our survey associate the problem of non-synchronous observations of fund prices and fund NAVs with the problem of mispricing, which is in fact another problem altogether. 28% of the respondents view securities lending as an advantage; it is perhaps viewed as a means of generating additional returns to compensate for the replication costs.

Exhibit 4.23: Concerning ETFs…

0 10 20 30 40 50 60 70

4%

39%

57%

5%

28%

68%

Is the mispricing with regard to NAV

a problem for you?

Is securities lending an important advantage

when considering ETFs?

No answerYesNo

4.2.3. Looking AheadFinally, we attempt to catch a glimpse into the future by asking respondents to identify the ways they are likely to use ETFs and other financial instruments in the future. As exhibit 4.24 shows, respondents report that in the future they expect to use both futures and ETFs more heavily. In fact, those who plan to use these two instruments more heavily far outnumber those who plan to use them less heavily. Indeed, 83% of respondents plan to increase their use of ETFs, while only 3% plan to decrease it. 31% of respondents plan to increase their use of futures, while 8% plan a decrease. That this survey covers only respondents who are already ETF investors makes this expected increase even more remarkable.

Total return swaps and traditional index funds, by contrast, are likely to play smaller and smaller roles: the asset managers who expect to rely less on these instruments outnumber those who expect to rely more heavily on them. Only 10% plan to increase their use of total return swaps, but 24% plan to decrease it. Index funds are similarly unpopular: 19% plan an increase and 26% a decrease. Overall, it seems that the anticipated growth in ETF use will come at

4. Results



the expense of other indexing vehicles such as total return swaps and index funds.

Exhibit 4.24: How do you predict your future use of the following instruments?

0 20 40 60 80 100

26%42%

19%13%

24%47%

10%19%

8%49%

3%13%

83%2%

31%12%

DecreaseStay the sameIncreaseNo answer

ETFs

Futures

Total return swaps

Index funds

4.3. The Role of ETFs in the Asset Allocation ProcessIn this section we analyse the use of ETFs for different asset classes and their role in core-satellite investing.

4.3.1. Use of ETFs in Different Asset ClassesFirst, we look into the relative importance accorded ETFs and other investment instruments in each asset class. For each asset class, exhibit 4.25 shows the percentages of amounts invested that are accounted for by ETFs or ETF-like products. ETFs are now a sizeable share of overall amounts in the equity universe. Indeed, for the average respondent to this question, they account for 36% of total equity investment and for a relatively high (22%) share of commodities investment. ETFs account for 17% of investment in government bonds. Corporate bond ETFs and real estate ETFs account for 12% of average investment in these asset classes. Hedge fund ETFs are the least popular, with

just 5% of hedge fund assets invested in such products. Interestingly, institutional investors are much more reluctant to invest large fractions of their wealth in equity ETFs than are private wealth managers. For example, 70% of private wealth managers report that they put more than a fifth of their equity investments in ETFs, but less than 50% of institutional investors can say the same. Furthermore, this difference is highly significant (p<0.01, Chi-square test).

Exhibit 4.25: For each asset class, indicate the percentage of total investment accounted for by ETFs or ETF-like products

36%

17%

22%

12%

12%

5%

Commodities

Real estate

Hedge funds

Corporate bonds

Government bonds

Equities

0 5 10 15 20 25 30 35 40

4.3.2. Purpose of ETF InvestmentsNext, we turn to the investors’ rationales for their use of ETF products. Hence, we ask the survey respondents to report how often, on a scale of 0 (never) to 6 (always) they use ETFs for different investment purposes. Exhibit 4.26 shows the answers by classifying all respondents as either frequent (higher than 3) or infrequent (3 or lower) users of ETFs. The results show that more than 50% of respondents use ETFs for long-term (buy-and-hold) investment strategy and/or for broad market exposure. ETFs are also often used for short-term (dynamic) investment or for specific sub-segment exposure. They are rarely used for dynamic portfolio insurance strategies, neutralisation of factor exposures related to other investments, or management of cash flows.

4. Results



These results show that investment in ETFs is associated mainly with long-term exposure to broad market indices. Still, frequent use of ETFs for short-term exposure to specific market sub-segments and for tactical bets suggests that other investment purposes are increasingly important as well.

Exhibit 4.26: How often do you use ETFs for the following purposes?

RarelyFrequently

Specific sub-segment exposure (sector, style)

Management of cash flows (e.g., cash equitisation)

Dynamic portfolio insurance strategies (e.g., CPPI)

Neutralisation of factor exposures of other investments

Tactical bets

Broad market exposure

Short-term/dynamic investment

Long-term/buy-and-holdinvestment

0 20 40 60 80 100

12%

11%

42%

15%

47%

68%

49%

57%

4.3.3. Exchange-Traded Funds in the Core-Satellite Allocation In this section, our survey addresses the use of ETFs in core-satellite methods of asset allocation. The core-satellite strategy, as discussed in section 2.2, is widely regarded as an effective means of organising asset allocation. Despite its advantages, only 50% of ETF investors have taken a core-satellite approach to portfolio construction (see exhibit 4.27). Only about 9% of respondents report that they are unfamiliar with this approach.

Exhibit 4.27: Have you implemented a "core-satellite"-type allocation?

YesNoNot familiar with it

50%

41%

9%

41% of all survey respondents have not taken a core-satellite-style investment approach. For this reason, we ask them if they are considering doing so. As exhibit 4.28 shows, 9% are; 57%, by contrast, state that it is unlikely. One-third of our respondents are undecided.

Exhibit 4.28: Will you implement a "core-satellite"- type allocation in the future?

Unlikely in the futureWe will soonNo answer

57%

9%

34%

ETFs are convenient for core-satellite allocation. Although indexing is sometimes perceived to be restricted to the core (for strategic allocation purposes), ETFs can

4. Results



also be beneficial in the satellite (for tactical bets), as highlighted in section 2.2. So in the next questions we examine how respondents use ETFs in their core and/or satellite portfolios. For a clearer understanding of the role each asset class plays in the core-satellite investment, we examine ETF use for each class separately.

Exhibit 4.29 summarises the percentage of core-satellite investment managers who use ETFs or ETF-like products for different asset classes. The figure shows that almost all respondents use equity ETFs for their core-satellite portfolio. For government bonds and commodities, ETFs are used by 80% and 62% respectively. Finally, for real estate and hedge funds, only about one-third of core-satellite managers use ETFs or ETF-like products.

Exhibit 4.29: Percentage of core-satellite investors who use ETFs for the following asset classes

47% 95%

80%

55%

62%

40%

34%

Equities

Government bonds

Corporate bonds

Commodities

Real estate

Hedge funds

0 20 40 60 80 100

Next, we ask the respondents to our survey to identify the ETFs they use in each asset class, i.e., what types of ETFs they prefer for their allocations to equity, fixed income, and alternative asset classes in the core and/or satellite portfolios. Responses are non-exclusive, as a given type of ETF may be used in both the core and the satellite. Moreover, different ETFs may be used simultaneously.

EquitiesThe equity ETFs most popular with core-satellite investors are broad-based ETFs. Exhibit 4.30 shows that 72% of equity ETF users use these vehicles in the core, while only 9% use them in the satellite. Another 11% use them in both core and satellite. Style and sector ETFs are clearly less popular than broad-based ETFs, especially for use in the core. In the satellite, however, style ETFs are used by 42% of respondents, while sector ETFs are used by 52%. Again, very few investors use style and sector ETFs in both the core and the satellite portfolios.

As the academic literature has insisted on the importance of style factors, this finding is surprising. As investment styles are not highly correlated, and as this correlation is remarkably stable across market states, equity style diversification is in fact one of the most promising ways of building a diversified core portfolio.

Exhibit 4.30: If you use equity ETFs, please indicate whether you use them in the core, in the satellite or in both

72%

9%

11%

28%

42%

4%

12%

52%

5%

Broad market ETFs

Style ETFs

Sector ETFs

In the coreIn the satelliteIn both

0 10 20 30 40 50 60 70 80

Government bondsFor investment in government bonds, 54% of our respondents use broad market ETFs in the core (see exhibit 4.31). Maturity-segment ETFs and inflation-protected-bond

4. Results



ETFs are less popular. If they are used, they are more likely to be made part of the satellite (26% to 31%) than of the core (21% for both). This is interesting, since different maturity segments are natural media for tactical timing strategies in the satellite. In addition, the time-series behaviour of these instruments, which differs with their differing exposure to interest rate changes, also makes them a useful means of constructing an efficient core portfolio. Very few investors (3% to 6%) use government bond ETFs in both core and satellite.

Exhibit 4.31: If you use government bond ETFs, please indicate whether you use them in the core, in the satellite or in both

0 10 20 30 40 50 60

54%

9%

6%

21%

31%

3%

21%

26%

3%

Broad market ETFs

Maturitysegment ETFs

Inflation-protectedbond ETFs


Corporate bondsBroad market ETFs predominate in corporate bond investment much as they do in core government bond investment. Exhibit 4.32 shows that, in the core, broad market ETFs are the most widely used. ETFs on indices that subdivide corporate bonds into finer categories, such as sectors, maturity, or ratings are used less frequently in the core. However, corporate bonds are generally more widely used in the satellite than government bonds are and investors rely more heavily on specific sub-segment ETFs. In the satellite, the most popular ETFs are

ratings-segment ETFs (46% of corporate bond ETF users), followed by sector and maturity-segment ETFs (41% and 36%). These results show that practitioners seem to agree with academic research that points to the significant benefits of active allocation to such finer categories of the bond market as maturity segments. Examples of papers on tactical asset allocation involving bond markets include Shiller (1979), Fama (1981), Ilmanen (1997; 1995), and Ilmanen and Sayood (2002).

Exhibit 4.32: If you use corporate bond ETFs, please indicate whether you use them in the core, in the satellite or in both

62%21%

4%

25%36%

3%

16%46%

5%

11%41%

3%

Broad market ETFs

Maturitysegment ETFs

ETFs by credit rating segment

Sector ETFs


0 10 20 30 40 50 60 70 80

Alternative asset classesFinally, we look at how European core-satellite investors use ETFs or ETF-like index products for alternative investments. The predominance of commodity ETFs or ETF-like products is clear. These products are used mainly in the satellite. Some 58% of core-satellite ETF investors in the alternative investment universe use such products in the satellite to invest in commodities. Investable index products are not as popular in hedge fund investments, possibly as a result of their relatively recent appearance. Only half of the investors who use ETFs for alternative asset classes rely on such

4. Results



products, and those who do put them mostly in the satellite. Real estate ETFs are only slightly more popular with investors. For all three alternative asset classes, ETF use is considerably lower in the core than in the satellite. Very few ETF users rely on alternative ETFs for both their core and satellite investments.

Exhibit 4.33: If you use alternative asset ETFs or ETF-like products, please indicate whether you use them in the core, in the satellite or in both

16%

38%

6%

22%

58%

8%

12%

32%

4%

Real estate

Commodities

Hedge funds


0 10 20 30 40 50 60

The results suggest that different types of ETFs are used for each part of the portfolio. ETFs on finer segments of the respective markets are frequently used in the satellite; broad market ETFs dominate the core. This dominance is not confined to equities alone, as these ETFs also account for the major share of the demand for government bond ETFs and corporate bond ETFs. Perhaps the most important result of our analysis is that, instead of actively managing their long-term beta exposure to obtain the most efficient risk-return trade off, European institutional investors and asset managers focus on using broad market indices in their core portfolios.

4.4. Trends: Use of and Satisfaction with ETFs over TimeIn the past decade, investment in ETFs has increased significantly, as already shown in section 2.1.1. However, since ETFs are relatively new, the benefits and possible uses are not yet fully known to all potential investors. So, investment in standard ETFs is growing, and more advanced products and sophisticated uses of these products are gaining popularity. In this section, we compare the results of this year’s survey and those of the surveys taken in 2006 and 2008. This comparison should shed additional light on the current use of ETFs and make it possible to draw conclusions about future developments.

4.4.1. Use and SatisfactionA diachronic comparison of ETF and ETF-like products underscores the tremendous recent growth of this financial product. Exhibit 4.34 shows that in all asset classes ETFs gained popularity in the past three years. ETF use in the equity universe has increased from 45% to 95%.6 The greatest one-year jump in popularity was for government bond ETFs (used by 40% of survey respondents in 2008 but by 80% in 2009). The use of ETFs in commodities and corporate bonds also increased significantly. The use of real estate ETFs and hedge fund ETFs increased only slightly; they are now used by about one-third of investors.

Exhibit 4.34: Use of ETFs and ETF-like products over time

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2006 2008 2009

Equities

Government bonds

Corporate bonds

Commodities

Real estate

Hedge funds

4. Results

6 - The corresponding question changed slightly from one survey to the next, in such a way that the answers to the question might be biased upwards. In 2006, the question was: “Do you use ETFs for this type of asset class?” In 2008, the question was: “Concerning your investment in the following asset class, please indicate which of the following ETFs you use for the core and/or satellite”. In 2009, the question was identical to the question asked the year before, but only to investors who take a core-satellite approach to investment. The 45% in 2006 thus applies to a much broader base than the 95% in 2009. Still, the increase in use is remarkable.



The increase in popularity of ETFs is made clear not only by the share of investors who use them but also by the percentage of total investment in each asset class accounted for by ETF-like products. Exhibit 4.35 compares the shares of our respondents’ portfolios invested in ETFs for each asset class.7 Again, equity ETFs are the most popular with investors. In 2009 equity ETFs account for 35% of total equity investment, compared to only 20% in 2008. Government bond ETFs also gained significant market share over the past twelve months. Only investment in hedge-fund-like ETFs fell; it now accounts for 5% of investment in hedge funds.

Exhibit 4.35: Percentage of total investments accounted for by ETFs or ETF-like products

22%

36%

10%

17%

7%

12%

16%

22%

7%

12%

7%

5%

Commodities

Corporate bonds

Government bonds

Equities

Real estate

Hedge funds

2008 2009

0 5 10 15 20 25 30 35 40

Satisfaction with standard equity and government bond ETFs has remained high, reaching about 80% for equity ETFs and 94% for government bond ETFs. Similarly, commodity ETFs do fairly well in terms of investor satisfaction. Users of other alternative asset ETFs, however, became less satisfied, especially in the case of hedge fund ETFs, where satisfaction has fallen to 2006 levels.

The reasons for the relative dissatisfaction with hedge fund ETFs may be different from those for the relative dissatisfaction with real estate ETFs. Recent surveys, for example, have shown that many practitioners are not convinced of the value of the hedge fund ETFs offered on the markets (Amenc and Schröder 2008). They believe that the behaviour of hedge fund managers is not replicable as such and consequently that any replication product is unlikely to replicate any managerial skill. And with the crisis many of these products have performed poorly.

Real estate ETFs suffer from different problems. Instead of directly investing in real estate, real estate ETFs usually replicate real estate indices that are based on real estate investment trusts (REITs), listed collective equity investment vehicles that provide relatively high liquidity. However, these indices cannot be considered fully representative of institutional investment in real estate, basically because of the criteria—listing, capitalisation, liquidity—on which their components are selected. As a result of this design, real estate ETFs usually have much higher equity market beta than unlisted funds and therefore offer different risk and return characteristics (Lee and Stevenson 2005). So, in the wake of the current financial crisis, real estate ETFs have lost considerably more than comparable directly invested assets, which has presumably led to greater dissatisfaction.

4. Results

7 - Since this question was not asked in the EDHEC European ETF Survey 2006, we can only provide a comparison with last year’s answers here.



Exhibit 4.36: Satisfaction with ETFs and ETF-like products over time

20%

30%

40%

50%

60%

70%

80%

90%

100%

2006 2008 2009

Equities

Government bonds

Corporate bonds

Commodities

Real estate

Hedge funds

When investor attitudes toward actively and passively managed ETFs are looked at over time, passive ETFs seem likely, at least for the moment (see exhibit 4.37), to enjoy greater favour. In fact, passive ETFs have become even more popular than their active counterparts. The increased awareness of indexing vehicles is seemingly accompanied by a desire to limit ETFs to this field, as opposed to seeing them venture into active management.

Exhibit 4.37: Preferred ETF type — active or passive?

2006 2008 2009

Passive ActiveNo answer

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

These findings allow us to draw two main conclusions. First, well-established forms of ETFs such as equity and government bond ETFs have become more popular despite the financial turmoil in 2009. This growth in popularity was especially great for government bond ETFs, the use and relative market share of which increased from 2008 to 2009. Second, alternative asset class ETFs and corporate bond ETFs clearly face more hurdles in 2009 than in 2008. Although they are still growing at a fast pace and gaining importance in

their respective asset classes, increasing dissatisfaction bodes ill for these products. Dissatisfaction with hedge fund ETFs has already led to relative falls in their use.

4.4.2. Developments in Advanced Uses of ETFs, Challenges for Total Return Swaps, and Outlook for Indexing ProductsExhibit 4.38 shows that the use of sophisticated forms of ETFs has increased in the last year—albeit from a low basis. The use of inverse ETFs (also called short ETFs) has doubled; about one in three ETF investors now use them. Other advanced practices, such as a shorting ETFs and securities lending, are more widespread as well. But options on ETFs are still used by only 6% of our respondents.

Using inverse ETFs or shorting ETFs is a straightforward means of hedging the portfolio’s exposure to declining markets, a practice that might well have some appeal in times of financial crisis. These products enjoyed little favour before 2008; now, however, views of them have changed.8

Exhibit 4.38: Advanced uses and forms of ETFs

0%

5%

10%

15%

20%

25%

30%

2006 2008 2009

Use of inverse ETFs

Options on ETFs

Shorting ETFs

Lending ETFs

The financial crisis in 2008/2009 has also influenced investor views of total return swaps, a possible alternative to ETFs. Both the fact that they are traded OTC and the related counterparty risk are viewed as more

4. Results

8 - We have no numbers for 2006 as these products were launched only after 2006.



problematic than before: more than 75% of the respondents express concern about the counterparty risk of TRSs, and 60% argue that over-the-counter trading is an obstacle to investment. Before 2008, both issues were far less important to investors.

Exhibit 4.39: Regarding total return swaps…

30%

35%

40%

45%

50%

55%

60%

65%

70%

75%

80%

2006 2008 2009

...is the fact that they are traded OTC a problem?

… is counterparty risk a problem?

Finally, we look at expected developments in the four indexing products analysed in this survey and compare these expectations over time. Exhibit 4.40 shows very clearly that investors have for several years been expecting to increase investment in ETFs. The figures in this survey (see exhibit 4.34, for example) indicate that these expectations have been met. Despite the strong past growth of ETFs, investors expect to rely on these products even more heavily in the future. Again, it is very likely that this growth will come at the expense of other indexing products, most of all total return swaps and traditional index funds.

Exhibit 4.40: Will you increase your use of the following indexing products?

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2006 2008 2009

ETFs

Futures

Total return swaps

Index funds

4. Results

5. Conclusion




The EDHEC European ETF Survey 2009 presents the results of a comprehensive survey of 360 institutional investors and private wealth managers. In addition to analysing ETF investment, this survey sheds light on the role of ETFs in asset allocation and compares ETFs and other investment products traditionally used as indexing vehicles: futures, index funds, and total return swaps.

The results of our survey convey a clear message: ETFs are now widely used and practitioners are highly satisfied with their features. For indexing, ETFs and futures are preferred to other financial products that enable investors to participate passively in broad market movements. In particular, ETFs are perceived to offer greater liquidity and transparency and lower costs than total return swaps and—to a lesser extent—traditional index funds. Respondents also appreciate the wide range of products and asset classes made available by ETFs. Futures, whose transparency and liquidity are deemed superior to those of ETFs, are the greatest rival to ETFs. So it would come as no surprise if ETFs were to gain a greater share of the index-products market; indeed, when asked about the future use of passive index products, respondents indicate that future growth in ETFs will come at the expense of index funds and total return swaps.

In some specific market segments, however, ETFs are currently facing obstacles. Satisfaction with ETF products for corporate bonds, real estate markets, and hedge funds is falling, probably as a result of the financial and economic crisis. Indeed, dissatisfaction with ETFs for hedge funds has already led to a drop in the share of total hedge fund investment accounted for by ETFs.

In these asset classes, the combination of adverse market conditions and relatively illiquid underlying assets presumably poses severe problems for ETF providers. Furthermore, many of these ETFs do not fully represent the asset class they are meant to replicate, but only some liquid components.

The EDHEC survey also shows that ETFs are not yet used to their full potential. As it happens, ETFs are used largely for passive holdings of broad market indices. More than two-thirds of all ETF users, for example, report that they frequently rely on ETFs for broad market exposure; and for more than 50% of the respondents ETFs are predominantly long-term or buy-and-hold investments. By contrast, fewer than 50% of respondents state that they frequently rely on ETFs for short-term investment or for exposure to specific market sub-segments. And the large majority of investors who take a core-satellite approach to portfolio management report that they rely on broad market indices only, especially in the core. But the wide range of ETFs for sub-categories and styles is left partly unused. When ETFs across asset classes are compared, equity ETFs are still predominant. First of all, satisfaction is highest in this asset class. Second, 95% of respondents relying on a core-satellite investment strategy use equity ETFs, an extremely high percentage. In addition, the 36% of equity investment accounted for by equity ETFs is larger than the share of investment in any other asset class accounted for by the corresponding form of ETF. As with the relative neglect of exotic ETFs, most practitioners do not benefit from the possibilities of trading options on ETFs, selling ETFs short, or lending them out, although these practices are more common than in years past. Inverse ETFs, on

5. Conclusion



the other hand, are becoming significantly more popular with ETF investors; the percentage of respondents saying they use them doubled over the past twelve months.

By offering a glimpse into the future, this survey shows that investors are increasingly interested in ETFs covering emerging markets. Despite current challenges, ETFs covering alternative asset classes are also high on the wish-list of ETF investors, especially for commodities, currencies and high-yield bonds. Another promising market segment is ETFs based on new forms of indices that do not take the standard capitalisation-weighting approach.

The results in this paper have a number of implications. Although ETF providers have enjoyed unprecedented growth in recent years, these providers must still overcome many obstacles. And while equity ETFs are viewed with favour by the market, this survey shows that there is high investor demand for ETFs on more exotic asset classes. So ETF providers would do well to seize the opportunity and to develop products to meet this growing demand. On the other hand, dissatisfaction with some ETF products in the alternative investment universe underscores the need for better products in this market. More research, so it seems, would enable the development of better products in these very illiquid markets.

That ETF investment has many advantages is now well known. It is now up to the investors to benefit from these advantages. This survey provides many illustrations of the ways in which ETFs can help improve investors’ portfolios. In particular, we

provide many examples that use the core-satellite technique to construct optimal investments. These illustrations are not meant to be complete solutions; we simply hope to have provided some food for thought on the future use of ETFs.

We are aware that this survey cannot shed light on every single issue of what is a very complex market. All the same, it is our belief that these results and the conclusions we draw can serve as a source of inspiration both to providers of ETFs and to investors; the survey should thus contribute to advances in this market.

5. Conclusion



5. Conclusion

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References



• Martellini, L. 2008. Dynamic allocation decisions in the presence of liability constraints. EDHEC working paper.

• Martellini, L., and V. Milhau. 2009. How costly is regulatory short-termism for defined-benefit pension funds? EDHEC working paper.

• Morgan Stanley. 2006. Exchange traded funds—First half of 2006 global review. Working paper.

• Pezier, J. 2008. The relative merits of alternative investments in passive portfolios. Journal of Alternative Investments 10(4): 37-49

• Rubinstein, M. 1989. Market basket alternatives. Financial Analysts Journal 45:20-29.

• Scherer, B. 2002. Portfolio construction and risk budgeting. Risk Books.

• Sharpe, W., and A. Perold. 1988. Dynamic strategies for asset allocation. Financial Analysts Journal 44(1): 16-27.

• Shiller, R. 1979. The volatility of long-term interest rates and expectations models of the term structure. Journal of Political Economy 87:1190-1219.

• Stock, H. J. 2006. The ETF explosion. Bank Investment Consultant 14(5): 22-27.

• Vardharaj, R., and F. Fabozzi. 2007. Sector, style, region: Explaining stock allocation performance. Financial Analysts Journal 63(3): 59-70

References



References



About the EDHEC Risk and Asset Management Research Centre

The choice of asset allocationThe EDHEC Risk and Asset Management Research Centre structures all of its research work around asset allocation. This issue corresponds to a genuine expectation from the market. On the one hand, the prevailing stock market situation in recent years has shown the limitations of active management based solely on stock picking as a source of performance.

40% Strategic Asset Allocation

3.5% Fees

11% Stock Picking

45.5 Tactical Asset Allocation

Source EDHEC (2002) and Ibbotson, Kaplan (2000)

On the other, the appearance of new asset classes (hedge funds, private equity), with risk profiles that are very different from those of the traditional investment universe, constitutes a new opportunity in both conceptual and operational terms. This strategic choice is applied to all of the Centre's research programmes, whether they involve proposing new methods of strategic allocation, which integrate the alternative class; measuring the performance of funds while taking the tactical allocation dimension of the alpha into account; taking extreme risks into account in the allocation; or studying the usefulness of derivatives in constructing the portfolio.

An applied research approachIn an attempt to ensure that the research it carries out is truly applicable, EDHEC has implemented a dual validation system for the work of the EDHEC Risk and Asset Management Research Centre. All research work must be

part of a research programme, the relevance and goals of which have been validated from both an academic and a business viewpoint by the Centre's advisory board. This board is made up of both internationally recognised researchers and the Centre's business partners. The management of the research programmes respects a rigorous validation process, which guarantees the scientific quality and the operational usefulness of the programmes.

To date, the Centre has implemented six research programmes:Asset Allocation and Alternative Diversification Sponsored by SG Asset Management and Newedge Prime Brokerage

The research carried out focuses on the benefits, risks and integration methods of the alternative class in asset allocation. From that perspective, EDHEC is making a significant contribution to the research conducted in the area of multi-style/multi-class portfolio construction.

Performance and Style AnalysisPart of a business partnership withEuroPerformanceThe scientific goal of the research is to adapt the portfolio performance and style analysis models and methods to tactical allocation. The results of the research carried out by EDHEC thereby allow portfolio alpha to be measured not only for stock picking but also for style timing.

Indices and BenchmarkingSponsored by Af2i, Barclays Global Investors, BNP Paribas Investment Partners, NYSE Euronext, Lyxor Asset Management, and UBS Global Asset ManagementThis research programme has given rise to extensive research on the subject of indices and benchmarks in both the hedge fund universe and more traditional investment

EDHEC is one of the top five business schools in France.

Its reputation is built on the high quality of its faculty (110

professors and researchers from France and abroad) and

the privileged relationship with professionals that the school has been developing since its

establishment in 1906. EDHEC Business School has decided

to draw on its extensive knowledge of the professional

environment and has therefore focused its research on themes

that satisfy the needs of professionals. EDHEC is

also one of the few business schools in Europe to have received the triple

international accreditation: AACSB (US-Global), Equis

(Europe-Global) andAssociation of MBAs

(UK-Global).EDHEC pursues an active

research policy in the field of finance. The EDHEC Risk and Asset Management Research Centre carries out numerous research programmes in the areas of asset allocation and

risk management in both the traditional and alternative

investment universes.




classes. Its main focus is on analysing the quality of indices and the criteria for choosing indices for institutional investors. EDHEC also proposes an original proprietary style index construction methodology for both the traditional and alternative universes. These indices are intended to be a response to the critiques relating to the lack of representativeness of the style indices that are available on the market. In 2003, EDHEC launched the first composite hedge fund strategy indices.

Asset Allocation and DerivativesSponsored by Eurex, SGCIB and the French Banking FederationThis research programme focuses on the usefulness of employing derivative instruments in the area of portfolio construction, whether it involves implementing active portfolio allocation or replicating indices. “Passive” replication of “active” hedge fund indices through portfolios of derivative instruments is a key area in the research carried out by EDHEC. This programme includes the “Structured Products and Derivatives Instruments” research chair sponsored by the French Banking Federation.

Best Execution and Operational PerformanceSponsored by CACEIS, NYSE Euronext, and SunGard This research programme deals with two topics: best execution and, more generally, the issue of operational risk. The goal of the research programme is to develop a complete framework for measuring transaction costs: EBEX (“Estimated Best Execution”) but also to develop the existing framework for specific situations (constrained orders, listed derivatives, etc.). Research also focuses on risk-adjusted performance measurement

of execution strategies, analysis of market impact and opportunity costs on listed derivatives order books, the impact of explicit and implicit transaction costs on portfolio performances, and the impact of market fragmentation resulting from MiFID on the quality of execution in European listed securities markets. This programme includes the “MiFID and Best Execution” research chair, sponsored by CACEIS, NYSE Euronext, and SunGard.

ALM and Asset ManagementSponsored by BNP Paribas Investment Partners, AXA Investment Managers and ORTEC FinanceThis research programme concentrates on the application of recent research in the area of asset-liability management for pension plans and insurance companies. The research centre is working on the idea that improving asset management techniques and particularly strategic allocation techniques has a positive impact on the performance of asset-liability management programmes. The programme includes research on the benefits of alternative investments, such as hedge funds, in long-term portfolio management. Particular attention is given to the institutional context of ALM and notably the integration of the impact of the IFRS standards and the Solvency II directive project. It also aims to develop an ALM approach addressing the particular needs, constraints, and objectives of the private banking clientele. This programme includes the “Regulation and Institutional Investment” research chair, sponsored by AXA Investment Managers, the “Asset-Liability Management and Institutional Investment Management” research chair, sponsored by BNP Paribas Investment Partners and the "Private Asset-Liability Management" research chair, in partnership with ORTEC Finance.




Ten research chairs have been endowed:

Regulation and Institutional InvestmentIn partnership with AXA Investment Managers

The chair investigates the interaction between regulation and institutional investment management on a European scale and highlights the challenges of regulatory developments for institutional investment managers.

Asset-Liability Management and Institutional Investment ManagementIn partnership with BNP Paribas Investment Partners

The chair examines advanced asset-liability management topics such as dynamic allocation strategies, rational pricing of liability schemes, and formulation of an ALM model integrating the financial circumstances of pension plan sponsors.

MiFID and Best ExecutionIn partnership with NYSE Euronext, SunGard, and

CACEIS Investor Services

The chair looks at two crucial issues linked to the Markets in Financial Instruments Directive: building a complete framework for transaction cost analysis and analysing the consequences of market fragmentation.

Structured Products and Derivative InstrumentsIn partnership with the French Banking Federation (FBF)

The chair investigates the optimal design of structured products in an ALM context and studies structured products and derivatives on relatively illiquid underlying instruments.

Financial Engineering and Global Alternative Portfolios for Institutional InvestorsIn partnership with Morgan Stanley Investment

Management

The chair adapts risk budgeting and risk management concepts and techniques to the specificities of alternative investments, both in the context of asset management and asset-liability management.

Private Asset-Liability ManagementIn partnership with ORTEC Finance

The chair will focus on the benefits of the asset-liability management approach to private wealth management, with particular attention being given to life-cycle asset allocation.

Dynamic Allocation Models and New Forms of Target FundsIn partnership with Groupe UFG

The chair consists of academic research devoted to the analysis and improvement of dynamic allocation models and new forms of target funds.

Advanced Modelling for Alternative InvestmentsIn partnership with Newedge Prime Brokerage

The chair involves a three-year project whereby academic research dedicated to alternative investments and to the analysis and modelling of their returns will be conducted.

Asset-Liability Management Techniques for Sovereign Wealth Fund (SWF) ManagementIn partnership with Deutsche Bank

The chair introduces a formal dynamic asset allocation model that will incorporate the most salient factors in sovereign wealth fund management and propose




an empirical analysis of the risk factors impacting the inflows and outflows of cash for various sovereign funds.

Core-Satellite and ETF Investment In partnership with CASAM

The research chair consists of academic research on exchange-traded funds (ETFs) and their use in the framework of core-satellite investment.

The EDHEC PhD in FinanceThe PhD in Finance at EDHEC Business School is designed for professionals who aspire to higher intellectual levels and aim to redefine the investment banking and asset management industries.

It is offered in two tracks: a residential track for high-potential graduate students who will hold part-time positions at EDHEC Business School, and an executive track for practitioners who will keep their full-time jobs.

Drawing its faculty from the world’s best universities and enjoying the support of the research centre with the greatest impact on the European financial industry, the EDHEC PhD in Finance creates an extraordinary platform for professional development and industry innovation.

Research for BusinessTo optimise exchanges between the academic and business worlds, the EDHEC Risk and Asset Management Research Centre maintains a website devoted to asset management research for the industry(www.edhec-risk.com), circulates a monthlynewsletter to over 250,000 practitioners, conducts regular industry surveys

and consultations, and organises annual conferences for the benefit of institutional investors and asset managers. The Centre’s activities have also given rise to the business offshoot EDHEC Asset Management Education.

EDHEC Asset Management Education helps investment professionals to upgrade theirskills with advanced risk and asset management training across traditional and alternative classes.



EDHEC Risk and Asset Management Research Centre2009 Position Papers• Lioui, A. The undesirable effects of banning short sales (April).

• Gregoriou, G., and F.-S. Lhabitant. Madoff: A riot of red flags (January).

2008 Position Papers• Sender, S. The European pension fund industry again beset by deficits (May).

• Amenc, N., and S. Sender. Assessing the European banking sector bailout plans (December).

• Amenc, N., and S. Sender. Les mesures de recapitalisation et de soutien à la liquidité du secteur bancaire européen (December).

• Amenc, N., F. Ducoulombier, and P. Foulquier. Reactions to an EDHEC study on the fair value controversy (December). With the EDHEC Financial Analysis and Accounting Research Centre.

• Amenc, N., F. Ducoulombier, and P. Foulquier. Réactions après l’étude. Juste valeur ou non : un débat mal posé (December). With the EDHEC Financial Analysis and Accounting Research Centre.

• Amenc, N., and V. Le Sourd. Les performances de l’investissement socialement responsable en France (December).

• Amenc, N., and V. Le Sourd. Socially responsible investment performance in France (December).

• Amenc, N., B. Maffei, and H. Till. Les causes structurelles du troisième choc pétrolier (November).

• Amenc, N., B. Maffei, and H. Till. Oil prices: The true role of speculation (November).

• Sender, S. Banking: Why does regulation alone not suffice? Why must governments intervene? (November).

• Till, H. The oil markets: let the data speak for itself (October).

• Amenc, N., F. Goltz, and V. Le Sourd. A comparison of fundamentally weighted indices: Overview and performance analysis (March).

• Sender, S. QIS4: Significant improvements, but the main risk for life insurance is not taken into account in the standard formula (February). With the Financial Analysis and Accounting Research Centre.

2009 Publications• Martellini, L., and V. Milhau. Measuring the benefits of dynamic asset allocation strategies in the presence of liability constraints (March).

• Le Sourd, V. Hedge fund performance in 2008 (February).

EDHEC Position Papers and Publications from the last four years




• La gestion indicielle dans l'immobilier et l'indice EDHEC IEIF Immobilier d'Entreprise France (February).

• Real estate indexing and the EDHEC IEIF Commercial Property (France) Index (February).

• Amenc, N., L. Martellini, and S. Sender. Impact of regulations on the ALM of European pension funds (January).

• Goltz, F. A long road ahead for portfolio construction: Practitioners' views of an EDHEC survey. (January).

2008 Publications• Amenc, N., L. Martellini, and V. Ziemann. Alternative investments for institutional investors: Risk budgeting techniques in asset management and asset-liability management (December).

• Goltz, F., and D. Schröder. Hedge fund reporting survey (November).

• D’Hondt, C., and J.-R. Giraud. Transaction cost analysis A-Z: A step towards best execution in the post-MiFID Landscape (November).

• Amenc, N., and D. Schröder. The pros and cons of passive hedge fund replication (October).

• Amenc, N., F. Goltz, and D. Schröder. Reactions to an EDHEC study on asset-liability management decisions in wealth management (September).

• Amenc, N., F. Goltz, A. Grigoriu, V. Le Sourd, and L. Martellini. The EDHEC European ETF survey 2008 (June).

• Amenc, N., F. Goltz, and V. Le Sourd. Fundamental differences? Comparing alternative index weighting mechanisms (April).

• Le Sourd, V. Hedge fund performance in 2007 (February).

• Amenc, N., F. Goltz, V. Le Sourd, and L. Martellini. The EDHEC European investment practices survey 2008 (January).

2007 Position Papers • Amenc, N. Trois premières leçons de la crise des crédits « subprime » (August).

• Amenc, N. Three early lessons from the subprime lending crisis (August).

• Amenc, N., W. Géhin, L. Martellini, and J.-C. Meyfredi. The myths and limits of passive hedge fund replication (June).

• Sender, S., and P. Foulquier. QIS3: Meaningful progress towards the implementation of Solvency II, but ground remains to be covered (June). With the EDHEC Financial Analysis and Accounting Research Centre.

• D’Hondt, C., and J.-R. Giraud. MiFID: The (in)famous European directive (February).



• Hedge Fund Indices for the Purpose of UCITS: Answers to the CESR Issues Paper (January).

• Foulquier, P., and S. Sender. CP 20: Significant improvements in the Solvency II framework but grave incoherencies remain. EDHEC response to consultation paper n° 20 (January).

• Géhin, W. The Challenge of hedge fund measurement: A toolbox rather than a Pandora's box (January).

• Christory, C., S. Daul, and J.-R. Giraud. Quantification of hedge fund default risk (January).

2007 Publications• Ducoulombier, F. Etude EDHEC sur l'investissement et la gestion du risque immobiliers en Europe (November/December).

• Ducoulombier, F. EDHEC European real estate investment and risk management survey (November).

• Goltz, F., and G. Feng. Reactions to the EDHEC study "Assessing the quality of stock market indices" (September).

• Le Sourd, V. Hedge fund performance in 2006: A vintage year for hedge funds? (March).

• Amenc, N., L. Martellini, and V. Ziemann. Asset-liability management decisions in private banking (February).

• Le Sourd, V. Performance measurement for traditional investment (literature survey) (January).

2006 Position Papers • Till, H. EDHEC Comments on the Amaranth case: Early lesson from the debacle (September).

• Amenc, N., and F. Goltz. Disorderly exits from crowded trades? On the systemic risks of hedge funds (June).

• Foulquier, P., and S. Sender. QIS 2: Modelling that is at odds with the prudential objectives of Solvency II (November). With the EDHEC Financial Analysis and Accounting Research Centre.

• Amenc, N., and F. Goltz. A reply to the CESR recommendations on the eligibility of hedge fund indices for investment of UCITS (December). 2006 Publications• Amenc, N., F. Goltz, and V. Le Sourd. Assessing the quality of stock market indices: Requirements for asset allocation and performance measurement (September).




• Amenc, N., J.-R. Giraud, F. Goltz, V. Le Sourd, L. Martellini, and X. Ma. The EDHEC European ETF survey 2006 (October).

• Amenc, N., P. Foulquier, L. Martellini, and S. Sender. The impact of IFRS and Solvency II on asset-liability management and asset management in insurance companies (November). With the EDHEC Financial Analysis and Accounting Research Centre.

EDHEC Financial Analysis and Accounting Research Centre2008 Position Papers • Amenc, N., F. Ducoulombier, and P. Foulquier. Reactions to an EDHEC study on the fair value controversy (December). With the EDHEC Risk and Asset Management Research Centre.

• Amenc, N., F. Ducoulombier, and P. Foulquier. Réactions après l’étude. Juste valeur ou non : un débat mal posé (December). With the EDHEC Risk and Asset Management Research Centre.

• Escaffre, L., P. Foulquier, and P. Touron. The fair value controversy: Ignoring the real issue (November).

• Escaffre, L., P. Foulquier, and P. Touron. Juste valeur ou non : un débat mal posé (November).

• Sender, S. QIS4: Significant improvements, but the main risk for life insurance is not taken into account in the standard formula (February). With the EDHEC Risk and Asset Management Research Centre.

2007 Position Papers• Sender, S., and P. Foulquier. QIS3: Meaningful progress towards the implementation of Solvency II, but ground remains to be covered (June). With the EDHEC Risk and Asset Management Research Centre.

2006 Position Papers • Foulquier, P., and S. Sender. QIS 2: Modelling that is at odds with the prudential objectives of Solvency II (November). With the EDHEC Risk and Asset Management Research Centre.

2006 Publications• Amenc, N., P. Foulquier, L. Martellini, and S. Sender. The impact of IFRS and Solvency II on asset-liability management and asset management in insurance companies (November). With the EDHEC Risk and Asset Management Research Centre.




EDHEC Economics Research Centre2009 Position Papers • Chéron, A. Quelle protection de l’emploi pour les seniors ? (January).

• Courtioux, P. Peut-on financer l’éducation du supérieur de manière plus équitable ? (January).

• Gregoir, S. L’incertitude liée à la contraction du marché immobilier pèse sur l’évolution des prix (January).

2008 Position Papers • Gregoir, S. Les prêts étudiants peuvent-ils être un outil de progrès social ? (October).

• Chéron, A. Que peut-on attendre d'une augmentation de l'âge de départ en retraite ? (June).

• Chéron, A. De l'optimalité des allégements de charges sur les bas salaires (February).

• Chéron, A., and S. Gregoir. Mais où est passé le contrat unique à droits progressifs ? (February).

2007 Position Papers • Chéron, A. Faut-il subventionner la formation professionnelle des séniors ? (October).

• Courtioux, P. La TVA acquittée par les ménages : une évaluation de sa charge tout au long de la vie (October).

• Courtioux, P. Les effets redistributifs de la « TVA sociale » : un exercice de microsimulation (July).

• Maarek, G. La réforme du financement de la protection sociale. Essais comparatifs entre la « TVA sociale » et la « TVA emploi » (July).

• Chéron, A. Analyse économique des grandes propositions en matière d'emploi des candidats à l'élection présidentielle (March).

• Chéron, A. Would a new form of employment contract provide greater security for French workers? (March).

2007 Publications• Amenc, N., P. Courtioux, A.-F. Malvache, and G. Maarek. La « TVA emploi » (April).

• Amenc, N., P. Courtioux, A.-F. Malvache, and G. Maarek. Pro-employment VAT (April).

• Chéron, A. Reconsidérer les effets de la protection de l'emploi en France. L'apport d'une approche en termes de cycle de vie (January).

2006 Position Papers • Chéron, A. Le plan national d’action pour l’emploi des seniors : bien, mais peut mieux faire (October).




• Bacache-Beauvallet, M. Les limites de l'usage des primes à la performance dans la fonction publique (October).

• Courtioux, P., and O. Thévenon. Politiques familiales et objectifs européens : il faut améliorer le benchmarking (November).

EDHEC Leadership and Corporate Governance Research Centre2009 position papers• Petit, V. Leadership : ce que pensent les top managers (May)

• Petit, V., and I. Mari. La légitimité des équipes dirigeantes : une dimension négligée de la gouvernance d'entreprise (January).

• Petit, V., and I. Mari. Taking care of executive legitimacy: A neglected issue of corporate governance (January).

EDHEC Marketing and Consumption Research Centre – InteraCT2007 Position Papers• Bonnin, Gaël. Piloter l’interaction avec le consommateur : un impératif pour le marketing. (January).




CASAM ETF: Simple, Liquid, Transparent and Cheaper!CASAM ETF is a dynamic range of efficient investment vehicles called exchange traded funds which are designed, managed and promoted by Crédit Agricole Structured Asset Management (CASAM).

In June 2008, CASAM launched an ambitious development plan to offer investors high quality ETFs with fees among the lowest in the market:• Cost efficiencyThe CASAM ETF range offers management fees among the lowest in the European market for each category of ETFs to offer a true competitive advantage to its clients.

• Low tracking error To ensure optimal management, the CASAM ETF range of products benefits from a synthetic or swap-based replication* method to minimize tracking error and therefore to replicate the benchmark index as closely as possible.

• A selection of high quality TRN indicesTotal Net Return (TRN) indices seek to optimize exposure to the index by reinvesting the net dividends(1) received by the fund and therefore keeping cash exposure to a minimum.

* except for CASAM ETF S&P Euro, CASAM ETF S&P Europe 350 & CASAM ETF CAC 40(1) except for CASAM ETF MSCI EUROPE MID CAP.

An extensive rangeThe CASAM ETF range is currently undergoing rapid development to provide investors with a wide choice of:• asset class: equities, fixed income and money market exposure• geographic exposure to Europe, European countries, the US, Asia and Asian countries• sector diversification offering the main industrial sectors in European equities• investment style: growth, value, high dividend, cap size and leveraged products.

Further information can be found at www.casametf.com

Contacts:

+33 (0)1 43 23 88 [email protected]

About CASAM ETF



SALES

Laetitia Roche-Hintzy +33 (0)1 41 89 75 96Fannie Wurtz +33 (0)1 41 89 75 95

Caroline de Coincy +33 (0)1 43 23 02 15

MARKET MAKER

ETF Trading +33 (0)1 41 89 74 70Or your usual sales or sales-trading contact.

Crédit Agricole Structured Asset ManagementCrédit Agricole Structured Asset Management (CASAM) is part of the Crédit Agricole Group and its business is dedicated to providing structured solutions, alternative investment solutions, and exchange-traded funds (ETFs).

CASAM, a pioneer in the ETF market since 2001, is the tenth largest manager, by assets under management, in the European ETF market (source: Barclays Global Investors—ETF Industry Preview Year End 2008).

CASAM’s investment solutions are offered to investors worldwide through the sales networks of Calyon Crédit Agricole CIB, CA Cheuvreux, and Crédit Agricole Asset Management.

CASAM manages around 600 funds amounting to total assets of €46.9 billion (as of 31 December 2008) and it is the market leader in structured funds in France, Japan, and Italy.

For more information, visit www.casam.com and www.casametf.com.

About CASAM ETF



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EDHEC Risk and Asset ManagementResearch Centre393-400 promenade des AnglaisBP 311606202 Nice Cedex 3 - FranceTel.: +33 (0)4 93 18 78 24Fax: +33 (0)4 93 18 78 41E-mail: [email protected]: www.edhec-risk.com

The EDHEC European ETF Survey 2009 - EDHEC-Risk Institute · 2018. 7. 30. · 6 An EDHEC Risk and...

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