The paper outlines an investment and portfolio optimisation process for the liquidity reserve in accordance with Basel III guidelines.

1. 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

2. 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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3. 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.
2Copyright © 2014 Ithuba Capital
Source: BIS Liquidity coverage ratio disclosure standards, January 2014

4. 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
3Copyright © 2014 Ithuba Capital
Quelle: Ithuba Capital
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.

5. 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
PortfolioReturn
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.
4Copyright © 2014 Ithuba Capital
Source: Ithuba Capital
Scenario 1 (max.)
Scenario 2 (5bps)
Scenario 3 (20bps)

6. -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
CreditScore
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)
5Copyright © 2014 Ithuba Capital
Source: Ithuba Capital
Source: Ithuba Capital
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.
Scenario 1 (max.)
Scenario 2 (5bps)
Scenario 3 (20bps)
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

7. 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).
6Copyright © 2014 Ithuba Capital
Source: Ithuba Capital
0.4
0.8
1.2
1.6
1.4 1.9 2.4 2.9 3.4 3.9 4.4 4.9
PortfolioReturn
Duration
Figure 8: Efficiency curve – Optimised liquidity portfolio
-2 -1 0 1 2
Duration corridor
Optimised portfolio
Source: Ithuba Capital
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.
Scenario 1 (max.)
Scenario 2 (5bps)
Scenario 3 (20bps)

8. 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)
7Copyright © 2014 Ithuba Capital

9. 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 ínfo@ithubacapital.com
Peter Bichl, CAIA
Managing Director
Ithuba Capital AG
+43-1-5123883-430
Stallburggasse 4, 1010 Wien
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