Why Understanding Hedge Fund Beta Is Important

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Why understanding hedge fund beta is important?

Portable Alpha Asia 2006 Conference25 – 27 April 2006, Conrad Hotel, Hong Kong

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I E S

This presentation and the analysis herein contains proprietary information and is not to be copied, reproduced, used, or divulged to any person in whole or in part without proper written authorization from an officer or director of Siemens AG. This information is the property of Siemens AG and is subject to completion and amendment.

The content of the presentation should not be interpreted as legal, tax, or investment advice. This document has been prepared by Siemens for discussion purposes only, based upon unaudited financial data. Siemens does not make any representation that the strategy will or is likely to achieve performance comparable to that shown. This document is not an offer to sell or a solicitation for the sale of a security nor shall there be any sale of security in any jurisdiction where such offer, solicitation, or sale would be unlawful. An investment in any of the products may involve a high degree of risk, including the risk of complete loss of an investment, and may only be made pursuant to final offering documents. Past performance of Siemens and / or any of its respective affiliates, employees, members, or principals is not indicative of future results and is no guarantee targeted performance will be achieved.

Siemens is under no obligation to release to the public any revised financial data that reflect anticipated or unanticipated events or circumstances. This presentation does not claim to be all-inclusive or to contain all of the information that any particular party may desire. No representation or guarantee is made regarding the accuracy or completeness of any of the information contained herein. Any person in possession of this presentation agrees that all of the information contained herein is of a confidential nature. Furthermore, the same person will treat the information in a confidential manner and will not directly or indirectly, disclose, or permit agents or affiliates to disclose, any of such information without the prior written consent of Siemens.

BY ACCEPTING THIS DOCUMENT YOU ACKNOWLEDGE THAT ALL OF THE INFORMATION HEREIN SHALL BE KEPT STRICTLY CONFIDENTIAL BY YOU.

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Agenda


Differentiating between hedge fund beta and traditional investment beta

Are hedge funds just repacking beta and selling it as a pure alpha strategy?

Replicating the hedge fund returns synthetically

Why should investors pay for embedded beta?

Conclusion

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Return Alpha = SkillMarket Return

=Market Risk

ResidualsSingle Index Model

ttXY εβα ++= *t

tttt XAY εβδα +++= **

Pure Alpha „Agility“

Hedge Fund

Return

Market Return=

Market RiskResiduals

APT

VolatilityRisk

CommodityRisk

CurrencyRisk

CreditRisk

... FactorsTBD*

ttkkttttt XXXXXY εβββββα +++++++= ***** 44332211 L

*See Appendix A.

Hedge Fund Beta:

Traditional beta:

Beta Agility

Alpha Agility

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An example

Source: CSFB, Siemens/fin4cast

Traditional beta Hedge fund beta

Alpha

Beta

Ag

ility

Risk Facto

rs

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We need to understand what drives the hedge fund return

Pure alpha requires a lot of maintenance. It comes from manager’s skills:

technology and know how

„Agility” is the ability to invest in ways not open to traditional investors:

Beta Agility: derivatives, short-selling, no restrictions on consentration limits

and credit ratings of investments

Alpha Agility: complex trading rules (Model Risk, Momentum Risk), leverage

Market beta is not a good reason to invest in hedge funds

Pure alpha is desirable, not reliable and not replicable

Agility is desirable, reliable and difficult to replicate

Market beta is reliable, easily replicable, but not desirable

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Agenda






Conclusion

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We need to translate the hedge funds returns into risk factors

Principal Component Analysis (PCA)

Dedicated Short

Managed Futures

GlobalMacro

Conv_ArbFI_Arb

Long ShortEquity

Emerging Markets

CSFB/T HFI

Event_D

Multi_Strat

Equity_MN

*See Appendix B for 6 principal components.Source: CSFB, Reuters, Thomson Financial, Siemens/fin4cast

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Let us have a close look at the returns of CSFB/Tremont Hedge Fund Index

Model with Beta Agility Beta T-Statistic Significance

Commodity Risk 0.3303 3.2349 0.0020Credit Risk -0.0355 -0.3157 0.7533Currency Risk -0.2056 -1.9300 0.0583Emerging Market Risk 0.4078 3.2941 0.0016Interest Rate Risk (short) -0.1566 -1.6451 0.1051Interest Rate Risk (long) 0.0284 0.2352 0.8148Liquidity Risk 0.2178 2.1539 0.0352Yield Curve Risk -0.1633 -1.6347 0.1073Market Risk -0.2591 -1.8066 0.0758Style Risk 0.1531 1.5825 0.1187Volatility Risk -0.2400 -1.6176 0.1109

R-squared 0.5041Adjusted R-squared 0.4146S.E. of regression 0.0077Correlation 0.7100Durbin-Watson stat 1.6143F-statistic 5.6365Prob(F-statistic) 0.0000

Model with Beta and Alpha Agility

Beta T-Statistic Significance

Commodity Risk 0.2211 2.3716 0.0210Credit Risk -0.0485 -0.4927 0.6241Currency Risk -0.2031 -2.1428 0.0363Emerging Market Risk 0.2320 2.0081 0.0492Interest Rate Risk (short) -0.1346 -1.6093 0.1129Interest Rate Risk (long) 0.0962 0.8881 0.3781Liquidity Risk 0.1451 1.5458 0.1275Yield Curve Risk -0.0926 -1.0405 0.3024Market Risk -0.1859 -1.4425 0.1545Style Risk 0.1102 1.2597 0.2128Volatility Risk -0.2800 -2.1415 0.0364Model Risk -0.0022 -0.0175 0.9861Momentum Risk 0.4136 3.4698 0.0010

R-squared 0.6316Adjusted R-squared 0.5505S.E. of regression 0.0067Correlation 0.7947Durbin-Watson stat 1.8638F-statistic 7.7817Prob(F-statistic) 0.0000

Model with Beta Agility, Alpha Agility and Pure Alpha

Beta T-Statistic Significance

Pure Alpha 0.0738 0.4701 0.6420Commodity Risk 0.2410 1.6106 0.1185Credit Risk 0.1043 0.6836 0.4998Currency Risk -0.2993 -1.8470 0.0753Emerging Market Risk 0.4532 2.0792 0.0469Interest Rate Risk (short) -0.0161 -0.1019 0.9196Interest Rate Risk (long) 0.0406 0.1930 0.8484Liquidity Risk 0.2482 1.6690 0.1063Yield Curve Risk -0.1555 -0.9538 0.3483Market Risk -0.0960 -0.4366 0.6658Style Risk 0.1668 1.0569 0.2996Volatility Risk -0.1787 -0.8711 0.3911Model Risk -0.1621 -0.9293 0.3607Momentum Risk -0.0733 -0.4289 0.6713R-squared 0.54277Adjusted R-squared 0.33048S.E. of regression 0.00611Correlation 0.73673Durbin-Watson stat 1.70051F-statistic 2.55677Prob(F-statistic) 0.01822

Beta agility and alpha agility seem to be significant sources of hedge fund return,

but is there any pure alpha?

Pure alpha tends to be less a matter of pure skill, but more a matter of flexibility

It is difficult to separate the alpha agility (flexibility) from the pure alpha (skill)

Source: CSFB, Reuters, Thomson Financial, Siemens/fin4cast

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Agenda





Why should investors pay for embedded beta

Conclusion

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It was necessary to understand where the hedge fund returns

came from in the past

It is more important to understand what will drive the hedge fund

returns in the future

Building Autoregressive Models with exogenous Variables (ARX)

to predict the future returns of:

CSFB/Tremont Hedge Fund Index

CSFB/Tremont Equity Long/Short Index (see Appendix D)

CSFB/Tremont Emerging Market Index (see Appendix E)

CSFB/Tremont Global Macro Index (see Appendix F)

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Interest Rate Risk (US long)

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Forecasting the returns of the CSFB/Tremont Hedge Fund Index

Pure alpha

Momentum Risk

Market Risk

Interest Risk (EU long)

Commodity Risk

Emerging Market Risk

Credit Risk

Source: Siemens/fin4cast

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What sources of return are more likely to drive the performance of CSFB/Tremont Hedge Fund Index in the future?


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How the model did and what it expects for April 2006?

Forecast for April 2006 +0.73%Source: Siemens/fin4cast

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Agenda






Conclusion

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Embedded beta could be divided into market beta and beta agility.

Investors should be prepared to pay for pure alpha, alpha agility and beta

agility, but not for market beta.

Market beta is easy to replicate. Investors should buy it cheaper form the

traditional managers.

Although beta agility is replicable, few hedge fund managers are in a position to

replicate it.

Investors should be prepared to pay for alpha agility, which is difficult to

replicate.

Investors should pay for pure alpha, which comes from innovation, technology

and know how and is not replicable.

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Agenda






Conclusion

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Summary: Why understanding hedge fund beta is important?

Traditional beta (single factor model) vs. hedge fund beta (multi factor model):

agility explains most of hedge fund returns

Pure alpha is desirable, not reliable and not replicable

Hedge Funds sell beta agility and alpha agility:

Both seem to be significant sources of hedge fund returns

Pure alpha tends to be less a matter of pure skill, but more a matter of flexibility

Deep understanding of past hedge funds performance is not enough. We need

a better grip on what will drive future hedge fund returns.

alpha agility: momentum and trading model

beta agility: emerging market factor, commodity factor, stock market factor

Investors should pay for pure alpha and be prepared to pay for agility

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Appendix A: Considered (Hidden) Risk Factors of Hedge Funds

Market Risk: S&P 500

Commodity Risk: Dow Jones AIG Commodity Index

Credit Risk: Spread (Yield M.L. US Corp BBB Bonds – Yield M.L. US Corp. AAA Bonds)

Emerging Market Risk: JPMorgan EMBI+ Composite

FX Risk: NYBOT US Dollar Index

Style Risk: Spread (S&P 500 – Russell 2000)

Interest Rate Risk: US Treasury Bill 90 day (short), Euro Bund (long EU), US T-Note 10y (long US)

Volatility Risk: VIX Index (implicit volatility of S&P 100 options)

Yield Curv Risk: Spred (US 30 year Treasury Bond – US Tresury Bill 90 day)

Liquidity Risk: NYSE Traded Volume

Model Risk: fin4cast Global Macro Diversified Futures Index I

Momentum Risk: Autoregressive Term

Source: Reuters, Thomson Financial, Siemens/fin4cast

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Appendix B: Principal Component Analyses

Source: CSFB, Reuters, Thomson Financial, Siemens/fin4cast

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Appendix C: Descriptive Statistics and Correlation Matrix

Descriptive StatisticsConvertible Arbitrage

CSFB Hedge Fund Index

Dedicated Short

Emerging Market

Equity Market Neutral

Event Driven

Fixed Income

Arbitrage

Global Macro

Long Short Equity

Managed Futures

Multi Strategy

Mean 0.78% 0.61% -0.16% 1.52% 0.48% 0.75% 0.45% 0.72% 0.31% 0.78% 0.70% Median 0.85% 0.61% -0.48% 1.98% 0.43% 0.82% 0.56% 0.50% 0.39% 0.45% 0.71% Maximum 3.40% 2.72% 10.89% 7.34% 2.39% 2.71% 2.97% 4.36% 10.31% 9.20% 4.86% Minimum -2.36% -1.09% -13.56% -5.06% -0.49% -1.76% -2.21% -1.77% -7.99% -8.71% -1.54% Std. Dev. 0.011 0.008 0.045 0.026 0.005 0.009 0.010 0.012 0.029 0.038 0.010 Skewness -0.14 0.24 -0.10 -0.31 1.05 -0.25 -0.41 0.47 0.40 -0.20 0.79 Kurtosis 3.07 2.91 3.44 2.75 4.63 3.45 3.42 3.10 5.81 2.77 5.72Autocorrelation 1st Order 0.55 0.10 0.13 0.07 0.18 0.26 0.19 0.20 0.00 0.10 0.24 Jarque-Bera 0.2354 0.7093 0.6950 1.3361 21.2498 1.3784 25.7536 2.6895 25.5599 0.6553 29.7101 Probability 0.8889 0.7014 0.7065 0.5127 0.0000 0.5020 0.2759 0.2606 0.0000 0.7206 0.0000 Observations 72 72 72 72 72 72 72 72 72 72 72

Correlation MatrixConvertible Arbitrage

CSFB Hedge Fund Index

Dedicated Short

Emerging Market

Equity Market Neutral

Event Driven

Fixed Income

Arbitrage

Global Macro

Long Short Equity

Managed Futures

Multi Strategy

Convertible Arbitrage 1.00CSFB Hedge Fund Index 0.41 1.00Dedicated Short -0.22 -0.45 1.00Emerging Market 0.20 0.51 -0.60 1.00Equity Market Neutral 0.31 -0.05 -0.10 0.00 1.00Event Driven 0.48 0.59 -0.59 0.61 0.02 1.00Fixed Income Arbitrage 0.39 0.25 -0.03 0.12 0.13 0.35 1.00Global Macro 0.27 0.39 0.17 -0.06 -0.04 0.09 0.17 1.00Long Short Equity -0.02 0.68 -0.64 0.52 -0.18 0.37 0.00 -0.07 1.00Managed Futures 0.05 0.47 0.14 -0.14 -0.08 -0.05 -0.21 0.38 -0.03 1.00Multi Strategy 0.60 0.61 -0.52 0.54 0.15 0.64 0.42 0.06 0.40 -0.08 1.00Period: 31 January 2000 - 31 January 2006

Source: CSFB/Tremont, Siemens/fin4cast

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Appendix D: Forecasting Model for CSFB/Tremont Long Short Equity Index

Yield Curve Risk

Pure alpha

Momentum Risk

Market Risk


Commodity Risk


Credit Risk


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Appendix D: What sources of return are more likely to drive the performance of CSFB/Tremont Equity Long/Short Index in the future?


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Appendix D: How the model did and what it expects for April 2006?

Forecast for April 2006 +0.61%Source: Siemens/fin4cast

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Appendix E: Forecasting Model for CSFB/Tremont Emerging Market Index

Yield Curve Risk

Pure alpha

Momentum Risk

Market Risk


Commodity Risk


Credit Risk


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Appendix D: What sources of return are more likely to drive the performance of CSFB/Tremont Emerging Market Index in the future?


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Forecast for April 2006 -0.63%


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Appendix D: Forecasting Model for CSFB/Tremont Global Macro Index

Yield Curve Risk

Pure alpha

Momentum Risk

Market Risk


Commodity Risk


Credit Risk


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Appendix D: ARX Model for CSFB/Tremont Global Macro Index


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Forecast for April 2006 -0.02%

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Biographies

Dr Miroslav Mitev is a managing director and head of quantitative research and strategy development at Siemens/fin4cast. Dr Mitev is responsible for the development of innovative, systematic long-short investment strategies for institutional investors world wide based on Siemens/fin4cast technology. After joining Siemens in 2001 Dr Mitev successfully formed a qualified team of 25 professionals which is continuously developing the Siemens/fin4cast Technology and building mathematical forecasting models for a variety of financial instruments like currency futures, commodity futures, stock index futures, bond futures, single stocks and hedge fund indices. Dr Mitev is in charge of the Siemens/fin4cast’s research cooperation with various universities and is actively involved in the scientific management of numerous master thesis and dissertations. Dr Mitev is a regular speaker at international conventions on liability driven investing, asset management, hedge funds, portable alpha, advanced quantitative studies, algo-trading and system research. Dr Mitev’s research is published on a regular basis in international journals and presented on international scientific conferences. Prior to joining Siemens Dr Mitev was at CA IB, the Investment Bank of Bank Austria Group, where he was in charge of the quantitative research of the securities research division. Dr Mitev received a Master of Economics and Business Administration with main focus on Investment Banking and Capital Markets. Dr Mitev also received a PhD in Economics with main focus on Finance and Econometrics.

Dr. Miroslav MitevSiemens AG ÖsterreichSiemens IT Solutions and Services PSE/fin4castPhone: +43 (0) 51707 46253Fax: +43 (0) 51707 56465Mobile: +43 (0) 676 9050903Email: [email protected]

Why Understanding Hedge Fund Beta Is Important

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