LCR Portfoliooptimisation

Liquidity Coverage Ratio

Part 1: Optimisation of the liquidity buffer (Basel III)

March, 2014

Peter Bichl CAIA, Asset Management Martin Blum, Asset Management

Emanuel Schörnig, Portfolio Advisory

Regulatory changes

In 2010 the Basel Committee on Banking Supervision (BCBS) announced the

introduction of a liquidity coverage ratio (LCR). In January 2013 the definition

of high-quality liquid assets (HQLA) and the timetable for the implementation of

the standard (2015-2019) were revised. European regulatory guidelines for LCR

under Basel III were defined by the capital requirements provisions in CRR I and

CRD IV in June 2013. The definitive version of the LCR is to be established by

30 June 2014. Starting on 1 January 2015, banks in the European Union must

satisfy the following requirements:

a) Daily calculation of the Liquidity Coverage Ratio

b) Availability of a liquidity portfolio sufficient to cover at least the net cash outflow of the next 30 days in certain stress scenarios.

Lower profitability for banks

2015 2016 2017 2018 2019

Liquidity Coverage Requirement

60% 70% 80% 90% 100%

Table 1: LCR Implementation timetable

Source: BIS liquidity coverage ratio disclosure standards, January 2014

Implementing the Basel III LCR provisions is accompanied by a variety of

difficulties which together have a negative effect on banks’ profitability.

Achieving the minimum liquidity requirements under LCR primarily means

holding a higher proportion of high quality liquid assets (HQLAs), which in turn

leads to lower expected returns.

In addition to lower expected yields, implementing the LCR also means higher

costs on the liability side, including additional system costs for inputting and

structuring data, reporting, scenario analysis and cash flow modelling.

The key challenge faced by the banks is to implement the LCR provisions as

efficiently as possible so as to counter the adverse economic effects (lower

expected yields and higher costs) in the most effective way.

This article goes on to deal with the optimisation of compliance with the

liquidity coverage requirements by concentrating on risk-return optimisation of

the liquidity portfolio. For the sake of completeness, there is a brief mentioning

of the topic on optimizing the net cash flows, which will be discussed in more

depth at a later date (Q2 2014).

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Figure 1: LCR Components

LCR (min. 100%) = Total high-quality liquid assets

Total net cash outflow over a period of 30 days

Optimising the liquidity coverage ratio has two major components. One way in

which the LCR can be improved is through a structured investment and

optimisation process. In this context, we recommend optimising the portfolio by

selecting investments which maximize the risk-return potential from the

intersecting set between regulatory provisions and the bank’s internal risk

management guidelines.

Determining and calculating the precise total net outflow of liquid funds is

another source of improvement. Forecasting net cash outflows as exactly as

possible results in a more accurate calculation of the fluctuations in the

required liquidity buffer. The key here is to accurately predict cash inflows and

outflows over the next 30 days, based on a detailed balance sheet and cash

flow analysis of individual items.

As mentioned in the beginning, the focus of this paper is on optimising the

liquidity buffer; optimising net cash outflows will be the subject of a separate

publication, probably in Q2 2014.

The rest of this article is designed to give an insight into the investment and

optimisation process for a portfolio of HQLAs. The elements of the investment

process are briefly explained, and illustrated using examples.

We concentrate on explaining the points that are most important for the

investment and optimisation of the liquidity buffer. Application in practice

involves a wider range of asset classes, constraints and objectives. We are

happy to discuss these topics in more detail at any time.


Source: BIS Liquidity coverage ratio disclosure standards, January 2014

Suggestions for portfolio optimisation

Investment process and investment universe

The basis of a risk-return optimised liquidity portfolio is a structured investment

process that takes into account both qualitative and quantitative factors.

Regulatory guidelines Internal risk management

guidelines

Optimised LCR portfolio

Investment universe

Qualitative/quantitative optimisation

1 2 3

4 Rebalancing

Figure 2: Investment process LCR portfolio

In presenting the process, only HQLA Level 1 assets with a zero risk weighting

are considered, since these constitute the bulk of the liquidity reserve (Figure 3)

and there is as yet no exhaustive definition for all HQLAs.

The investment universe is defined by the intersecting set between the

regulatory provisions and the applicable internal banking risk limits, and – as a

result of new issues, changes in risk limits and other factors – is subject to

continual change.

Level 2A covered bonds (AA- or better), 1.8%

Level 2B RMBS, 0.3%

Figure 3: Distribution of HQLAs – Global banks

Level 1 (0% RW), 53.5%

Level 1 (cash & withdrawable CBKR), 31%

Level 1 (>0% RW), 3.9%

Level 2A (20% RW sovereigns, CBKs & PSEs), 6%

Level 2A non-financial corporate bonds (AA- or better), 1.9%

Level 2B non-financial corporate bonds (BBB- to A+), 0.5%

Level 2B non-financial common equity shares, 1.1%

Source: Basel III Monitoring Report 2013


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Qualitative optimisation – Credit scores

Once the investment universe has been defined, quality controls are applied in

which the individual countries are allocated credit scores of between -2 and +2.

These credit scores are the result of quantitative and qualitative analysis, and

determine each country’s weighting in the subsequent optimisation process.

The quantitative analysis is based on a fair value spread model, which indicates

whether the sovereign credits are fundamentally overvalued or undervalued.

The qualitative analysis includes event risk, policy and rating analysis. It ensures

that a wider range of risk and bottom-up factors are reflected in the credit

scores.

0.4

0.8

1.2

1.6

1.4 1.9 2.4 2.9 3.4 3.9 4.4 4.9

Po

rtfo

lio R

etu

rn

Duration

Figure 4: Efficiency curve – Optimised liquidity portfolios

Quantitative optimisation with credit score overlay

The next step is a quantitative optimisation process in which an efficiency curve

is calculated on the basis of the relevant regulatory guidelines and internal risk

limits. The three efficiency curves in Figure 4 differ due to the different weights

applied to the credit scores is each optimisation scenario.

The efficiency curve “Scenario 1 (max.)” shows all the portfolios with a

maximum return for the respective duration. The composition of the portfolio

in Scenario 2 (5 bps) optimises the yield as well as the credit score. This

represents a compromise between a reduction in yield of a maximum of 5 bps

and a portfolio with an improved score. The interpretation for Scenario 3

(20 bps) is similar, with a yield give-up of maximum 20bps.

The change in the credit scores relative to duration is shown in Figure 5.

Scenario 1 (max.) represents the portfolio with the worst credit score, because

it is maximised for returns only. In Scenario 2 (5 bps) there is a significant

improvement in credit scores, with a minimal loss of yield of 5 bps. Portfolios

with a reduction of 20 bps as against the yield-maximised Scenario 1 (max.)

show the highest credit scores.


Source: Ithuba Capital

Scenario 1 (max.) Scenario 2 (5bps) Scenario 3 (20bps)

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

1.4 1.9 2.4 2.9 3.4 3.9 4.4 4.9

Cre

dit

Sco

re

Duration

Although the yield give-up between Scenario 1 and Scenario 2 is only 5 bps, the

actual portfolio allocation is very different (Figure 6). With a duration of

2.9 years, Scenario 2 has a significantly higher credit score of +0.08, as

compared with -0.55 in Scenario 1.

Figure 5: Credit scores along the efficency curve

CS (2) = 0.08

CS (1) = -0.55

Figure 6: Portfolio allocation with a duration of 2.9 years

Portfolio: Scenario 1 (max.) Portfolio: Scenario 2 (5bps)




Optimisation including duration signal

Similar to the evaluation of credit scores, the duration signal is expressed using

a scale between -2 (e.g. long duration) and +2 (e.g. short duration) in order

define the amount of interest rate risk.

The measure is based on a fair value yield model (i.e. risk premia in 5y5y

forwards) and an analysis of the factors influencing deviation from fair values.

The combination of these two analyses is the basis for the duration signal.

The next step is to use scenario analysis to determine the events that would be

responsible for a stepwise change in the duration signal (Figure 7). In Scenario 4

(1pct GDP positive growth shock), the duration indicator changes to short

duration (+2), which is a signal to reduce portfolio interest rate risk.


5%

5%

5%

9%

10%

10%

16%

20%

20%

0% 5% 10% 15% 20%

NIB

Sweden

Netherlands

EFSF

Italy

Spain

France

EIB

KFW

3%

5%

5%

9%

10%

10%

18%

20%

20%

0% 5% 10% 15% 20%

Sweden

Finland

France

EIB

Italy

Spain

Germany

KFW

Netherlands

Figure 7: Duration signal and scenario analysis

Influencing factors

Fair-Value Model

Duration-signal (DS)

Scenario DS

1pct revision lower in 5y ahead inflation expectations

-2

ECB QE1 -1

ECB QE2 1

1pct GDP positive growth shock

2

Duration corridor (DC)

To adapt the duration signal for an individual risk budget, the extreme scenarios

are converted into expected values, and the potential gains or losses are

quantified. These parameters then define the duration corridor (DC) within

which – on the basis of the duration signal – the portfolio with the maximum

yield is selected (Figure 8).



0.4

0.8

1.2

1.6

1.4 1.9 2.4 2.9 3.4 3.9 4.4 4.9

Po

rtfo

lio R

etu

rn

Duration

Figure 8: Efficiency curve – Optimised liquidity portfolio

-2 -1 0 1 2

Duration corridor

Optimised portfolio


Transparent and structured investment process

Compared with a discretionary approach, structured optimisation offers a

systematic and transparent approach to the investment process. The result

(Figure 8) is a yield-maximised portfolio that takes account of both regulatory

provisions and internal bank guidelines, and also reflects other fundamental

factors such as interest rate and spread risks.


The result is a very high degree of flexibility: investors decide for themselves

what weighting to give the credit score analysis and which credit score input to

choose. It is also up to investors to decide to what extent market sentiment and

the selected interest rate risk should be reflected. To sum up, this procedure

contributes to a very stable, structured and transparent investment process.

This article has focused on explaining the basic concepts underlying LCR HQLA

optimisation. HQLA Level 1 (RW>0%), Level 2 assets, as well as accounting and

reporting issues such as allocation between HTM, AFS and AFV portfolios, have

not been discussed. We are happy to discuss our suggested solutions and other

topics in more detail at any time.

Figure 9: Further thoughts with respect to the optimisation

• Broadening of the investment universe including regional bonds and HQLA

Level 1 (RW>0%) and HQLA Level 2 Assets

• Further parameters for optimisation

• Further risk- and volatility measures (e.g. VaR, scenarios, …)

• Haircuts (Level 2 assets)

• RWA and ROE

• Financing costs

• Internal and external reporting (risk and regulatory)


Q1 2014

thuba Capital is an independent partnership, specialised in Asset Management, Risk- and Portfolio Advisory and Corporate Finance.

For further information please contact us í[email protected] Peter Bichl, CAIA Managing Director Ithuba Capital AG +43-1-5123883-430 Stallburggasse 4, 1010 Wien

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LCR Portfoliooptimisation

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