LiquidityRiskManagementDay1Ses4.ppt

Day 1. Session 4. Credit risk and
liquidity modeling
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Copyright © 2011 Global Risk Consult

Modelling methodologies:
(source:causal capital)
• The individual assessments of rating
agencies should:
1. Include the definition of default
2. Include the time horizon and the
meaning of each rating
3. Include the actual default rates
experienced in each assessment category
4. Include the transitions of the
assessments such as the likelihood of ‘AA’
rating becoming an ‘A’ overtime

Conceptual Approaches to Credit
Risk Modeling
• Over the last decade, a number of the world's major banks have developed sophisticated
systems to quantify and aggregate credit risk across geographical and product lines.
• The initial interest in credit risk models stemmed from the desire to develop more
rigorous quantitative estimates of the amount of economic capital needed to support a
bank's risk-taking activities.
• From a regulatory perspective, the flexibility of models in responding to changes in the
economic environment and innovations in financial products may reduce the incentive
for banks to engage in regulatory capital arbitrage.
• Furthermore, a models-based approach may also bring capital requirements into closer
alignment with the perceived riskiness of underlying assets, and may produce estimates
of credit risk that better reflect the composition of each bank's portfolio.

Credit Loss

Applications of credit risk models
• Credit risk modelling methodologies allow a tailored and flexible approach to price measurement and
risk management.
• The internal applications of model output span a wide range, from the simple to the complex. Current
applications include:
– Setting of concentration and exposure limits;
– Setting of hold targets on syndicated loans;
– Risk-based pricing;
• Improving the risk/return profiles of the portfolio;
• Evaluation of risk-adjusted performance of business lines or managers using risk-adjusted return on
capital ("RAROC");
• Economic capital allocation; and
• Estimation for setting or loan loss reserves, either for direct calculations or for validation purposes.

Some characteristics of an ideal
model for measuring credit risk
• Ability to consider credit risk on a portfolio basis, taking account of default
correlation between each counterparty.
• Ability to take account of market movements and potential implications for credit
risk. This would be relevant for assets where the value depends on market
movements e.g. derivatives.
• Ability to consider mark-to-market values. That is, to allow for possibility of
upgrades and downgrades, not just default. Note that lack of data may make
this impractical.
• Ability to take account of varying recovery rates which would depend on the
seniority of the claim, the security held, and the legal system.

Credit Loss Distribution

Measuring credit losses
• Credit losses can be calculated as the sum of expected loss and unexpected loss
in a portfolio of assets.
• It can also be viewed as the difference between the portfolio's current value and
future value at the end of a time horizon.
• There are two fundamental approaches of evaluating credit losses at portfolio
level:
• Default mode (DM)
• Mark-to-market (MTM) paradigm

Default mode paradigm
• Within the DM paradigm, a credit loss arises only if a borrower defaults within the planning
horizon.
• Consider a standard term loan. In the absence of a default event, no credit loss would be
incurred.
• In the event that a borrower defaults, the credit loss would reflect the difference between
the bank's credit exposure and the present value of future net recoveries.
• For a term loan, the current value would typically be measured as the bank's credit
exposure (e.g., book value). The (uncertain) future value of the loan, however, would
depend on whether or not the borrower defaults during the planning horizon.
• If the borrower does not default, the loan's future value would normally be measured as the
bank's credit exposure at the end of the planning horizon, adjusted so as to add back any
principal payments made over the period.
• On the other hand, if the borrower were to default, the loan's future value (per dollar of
current value at the beginning of the horizon) would be measured as one minus its loss rate
given default (LGD). The lower the LGD, the higher the recovery rate following default

Market to market paradigm
• The mark-to-market (MTM) paradigm recognizes any gains or losses in
the value of a debt security caused by changes in the credit quality of
the obligor over the measured time horizon.
• If the credit quality of the obligors in a portfolio deteriorates as a result
of recession, the portfolio value will be lower, even without any
defaults. A market price for each debt security is obtained by
discounting cash flows on the obligor's credit curve.
• Most MTM-type credit models employ either of the following
approaches for the purpose of modeling the current and future (mark-
to-market) values of credit instruments.
– Discounted contractual cash flow (DCCF) approach
– Risk-neutral valuation (RNV) approach

Types of models
• Unconditional models are those models that reflect relatively limited
borrower or facility- specific information.
• Conditional models are those that also attempt to incorporate
information on the state of the economy, such as levels and trends in
domestic and international employment, inflation, stock prices and
interest rates and even indicators of the financial health of particular
sectors.

Examples of unconditional models
• Examples of unconditional credit risk models are the Unexpected loss approach,
CreditMetricsTM and CreditRisk+TM.
• All three modeling frameworks base expected default frequency (EDFs) and derived
correlation effects on relationships between historical defaults and borrower-specific
information such as internal risk ratings.
• The data is estimated (ideally) over many credit cycles. Whatever the point in the credit
cycle, these approaches will predict similar values for the standard deviation of losses
arising from a portfolio of obligors having similar internal risk ratings.
• Such models are currently not designed to capture business cycle effects, such as the
tendency for internal ratings to improve (deteriorate) during cyclical upturns (downturns).

Examples of conditional models
• An example of a conditional credit risk model is McKinsey's CreditPortfolioView TM.
• Within its modelling framework, rating transition matrices are functionally related to the
state of the economy, as the matrices are modified to give an increased likelihood of an
upgrade (and decreased likelihood of a downgrade) during an upswing (downswing) in a
credit cycle.
• This qualitative phenomenon accords with intuition and is borne out by some research.

Portfolio credit risk models can be
classified according to their approaches to
credit risk aggregation:
• Top-down models
• Top-down models group credit risks using single statistics.
• They aggregate many sources of risk viewed as homogeneous into an overall portfolio risk,
without going into the detail of individual transactions.
• This approach is appropriate for retail portfolios with large number of credits, but less so for
corporate or sovereign loans. Even within retail portfolios, top-down models may hide
specific risks, by industry or geographic location.
Bottom-up models
• Bottom-up models account for features of each asset/credit.
• This approach attempts to measure credit risk at the level of each loan based on an explicit
evaluation of the creditworthiness of the portfolio's constituent debtors.
• Each specific position in the portfolio is associated with a particular risk rating, which is
typically treated as a proxy for its EDF and/or probability of rating migration.
• These models could also utilize a micro approach in estimating each instrument's LGD. The
data is then aggregated to the portfolio level taking into account diversification effects.
• It is appropriate for corporate and capital market portfolios.

Correlation between credit events
• In measuring credit risk, the calculation of a measure of the dispersion of credit risk
requires consideration of the dependencies between the factors determining credit-related
losses, such as correlations among defaults or rating migrations, LGDs and exposures, both
for the same borrower and among different borrowers.
• These correlations, and the methodology to calculate them in a given credit model, can
have a major effect on the loss distribution of a portfolio and is a key difference between
the credit models.

Approaches for handling
default/rating migration correlations
• Structural approach
• Structural approach explains correlations by the joint movements of assets.
• For instance, A customer may be assumed to default if the underlying value of its assets falls below some
threshold, such as the level of the customer's liabilities; or
• A change in a customer's risk rating can be determined by the change in the value of his assets in relations
to various thresholds.
• In both these cases some random variables have been used to determine the change in the customer's risk
rating including defaults. These variables are called migration risk factors.
• This approach is used by models like CreditMetrics and PortfolioManager.
• Reduced-form approach
• Reduced-form models explain correlations by assuming a particular functional relationship between default
and 'background factors'.
• These background factors may represent either:
• Observable variables, such as indicators of macroeconomic activity, or unobservable random risk factors.
• Within reduced-form models, it is the dependence of the financial condition of individual customers on
common or correlated background factors that gives rise to correlations among customers' default rates
and rating migrations.
• This approach is used by models like CreditRisk+ and CreditPortfolioView

Risk aggregation and its
stakeholders

Factoring macroeconomic impact on
Credit and Valuation Models
• Calculate macroeconomic correlations to fundamental data
• Understand the correlations between fundamental financial data and a
set of macroeconomic variables, commodity prices, foreign exchange
rates and interest rates
• Monitor those correlations for convergence/divergence
• Look for warning signs such as increasing correlations
• Use the correlations to forecast fundamentals and feed models
• Use the correlations to forecast financials to feed a range of macro and
fundamentally based credit models, equity valuation models and so
forth
• Then, perform economic shocks and stress testing
– Shock the economic, commodity, and rate variables to provide a
foundational stress testing capability across those models

Risk Aggregation Methodologies
Risk aggregation involves the aggregation of individual risk measurements
using a model for aggregation. The model for aggregation can be based
on a simple linear aggregation or using a copula model.
• The linear aggregation model is based on aggregating risk, such as
value at risk (VaR) or expected shortfall (ES), using correlations and
the individual VaR or ES risk measures.
• The copula model aggregates risk using a copula for the co-
dependence, such as the normal or t-copula, and the individual risk’s
profit and loss simulations. The copula model allows greater flexibility
in defining the dependence model than the linear risk aggregation.

• The simplest method in aggregating different risks and the
corresponding loss distributions is to ignore possible diversification and
netting effects. In other words, this corresponds to making the
assumption that random variables, representing the different risks, are
perfectly correlated. Within this approach, the risk measures are
summed up to obtain the corresponding risk measure for the total loss
distribution.

• A more sophisticated and widely used method is the correlation
approach. Within this approach the assumption is that the different
risks and corresponding random variables are multivariate normal; that
is, assume that the dependence structure can be described by the
dependence between the margins of a multivariate Gaussian
distribution. Furthermore, the individual distributions are assumed to
be normally distributed—an assumption that, depending on the actual
distribution, can introduce a questionable distortion.
• The problem of assuming normally distributed risk components is
circumvented if one uses the copula approach. While the individual
risks can be modeled independently of each other, one also has the
freedom to model the dependence structure separately.

Linear Risk Aggregation
• The linear model for risk aggregation takes the individual VaR or ES risks as
inputs and aggregates the risks using the standard formula for covariance.

Risk aggregation and Economic
Capital

Capital
• The independent aggregate risk is obtained using zero correlations and
the additive risk is obtained using correlations equal to unity.
• Finally, we also calculate the actual contribution risk as a percentage of
total risk.
• The contribution is the actual risk contribution from the sub-risks
obtained in the context of the portfolio of total risks1. This risk
contribution is often compared with standalone risk to obtain a
measure of the diversification level.
• For example, the market risk sub-risk has a standalone risk share of
16,3 percent whereas the risk contribution share is 11,89 percent. This
represents a diversification level of about 73 percent compared to the
simple summation approach.

Capital

Pros and Cons of the linear model
• The linear model of risk aggregation is a very convenient model
to work with. The only data required is the estimates of the
sub-risks’ economic capital and the correlation between the sub-
risks.
• However, the model also has some serious drawbacks. For
example, the model has the assumption that quantiles of
portfolio returns are the same as quantiles of the individual
return – a condition that is satisfied in case the total risks and
the sub-risks come from the same elliptic density family.
• The linear risk aggregation model is the aggregation model used
in the capital regulations for the standard approach

Copula-Based Risk Aggregation
While linear risk aggregation only requires
measurement of the sub-risks, copula methods of
aggregation depend on the whole distribution of the
sub-risks.
• Benefit 1: allows the original shape of the sub-risk
distributions to be retained.
• Benefit 2: allows for the specification of more general
dependence models than the normal dependence
model.

Copula-Based Risk Aggregation

Scenarios generation

Capital is not 100% fungible
• Legal Entity Structure
• Joint Ventures
• Ring fenced funds related to certain
products and structures
• Taxes
• Caps on Transfers
• Intra Company Reinsurance

Risk aggregation and estimation of overall risk is
key in banks’ approaches to economic capital and
capital allocation.
• The resulting capital also forms the basis for banks’ value-based management of
the balance sheet.
• When estimating aggregate capital, one typically uses a combination of bottom-
up and top-down risk aggregation approaches.

Considerations in designing Liquidity Risk Stress
Scenarios
 Utilize integrated approach when developing scenario specifications
̶ Leverage subject matter experts across various disciplines, including but
not limited to Economics, Credit, Market and Operational Risk, and Finance
̶ Assess cash flow implications related to key liquidity risk drivers
 Consider different degrees of severity by varying scenarios’ duration and impact
levels
 Consider relationships between risk drivers
̶ Leverage combination of subject matter expert judgement and observed
quantitative relationships
 Ensure management reviews results
̶ Assess results against established risk appetite and take actions to
remediate accordingly

Stress Testing best Practices
 Know your business and inherent liquidity risk drivers
̶ Institution complexity and level of exposures should drive stress
testing framework and cash flow modeling assumptions
 Use a variety of stress scenarios, time horizons and severity
levels
̶ Stress testing should be run on a regular basis
̶ As the operating stage becomes more severe, frequency of testing
should increase
 Conduct regular review of stress testing outcomes to ensure
alignment with the established liquidity risk appetite
̶ Any liquidity gaps exceeding risk tolerance merit remedial action
̶ Results should be integrated with the firm’s contingency funding
planning

Contingency Funding Planning Best Practices
 Contingency funding plans provide clarity during deteriorating
liquidity conditions
̶ Robust CFPs clarify strategies, roles and responsibilities when total focus
should be given to mitigating the firm’s exposure to potential or realized
adverse liquidity conditions
̶ Management should assess the operating stage and execute the
applicable remediating actions
̶ Fully integrated with stress testing framework
 Know your business and available sources of liquidity
̶ Considers existing sources of liquidity and whether it is sufficient to fund
normal operating requirements under stress events
̶ Identifies potential alternative contingent liquidity sources
̶ Considers the firm’s specific legal, regulatory, and tax related
encumbrances to available sources of liquidity

Contingency Funding Best practices
 Robust CFPs ensure effective communication
̶ Internal and external stakeholders should be considered in the CFP
process
̶ Implementation and escalation procedures should be clearly defined
 CFPs should be reviewed on a regular basis to ensure
relevance to the risks to which the firm is exposed
̶ In addition, simulations of the plan should be enacted to test
coordination, decision-making, and operational execution ability

Day 1. Case study and exercise

Basel III shock scenario components
• The LCR goes much further than just reflecting the characteristics of funding. In
particular, it recognizes the liquidity effects that can stem from trading book and
derivative exposures in a stress period. As security prices and interest rates
become more volatile, so margin/collateral requirements can drain liquidity or
access to unencumbered high-quality liquid assets.

What the stress test captures
• Many contracts have triggers that require more collateral to be posted if the
institution is downgraded. This will put downward pressure on the available stock
of liquid high quality assets. The stress test also captures pressures from
balance sheet growth because, in a period of market stress, committed lines are
likely to be drawn down.
• The test does differentiate between different types of commitment. There is a
significant difference in drawdown assumptions for credit facilities and liquidity
facilities. Liquidity facilities are defined as any committed, undrawn back-up
facility put in place expressly to refinance the debt of a customer in situations
where the customer is unable to obtain its ordinary course of business funding
requirements (e.g., commercial paper program).

What the stress test captures
• Working capital facilities are classified as credit facilities, rather than liquidity
facilities. With the exception of retail and small business customers, the LCR
assumes 100% drawdown of liquidity facilities. Retail and small business
committed credit and liquidity lines are thought less likely to be drawn down
(e.g., credit card limits) than lines given to other customers and, therefore, a 5%
drawdown factor is applied.
• As discussed, for all other types of customers, committed liquidity lines are
assumed to be fully drawn. A drawdown factor of 10% is applied for credit
facilities for nonfinancial corporates, sovereigns and central banks, PSEs or
multilateral development banks. For financial institutions and other customers, a
100% drawdown factor for committed credit facilities must be used.
• The stress test also reflects the reality that in the face of potential reputational
damage, a bank could not walk away from some types of commitments even
though the contract may allow it to. One example is the mortgage pipeline for a
mortgage bank. Mortgages will have been approved out over a three-month
period.

Data and system challenges
• Although in some markets, the contractual terms may allow the bank to walk away from the
agreement and not provide the mortgage, the reputational damage sustained could make
this option unrealistic. This new liquidity stress testing will pose major data and systems
challenges.
• One difficult area is the tracking of triggers and other aspects of derivatives contracts that
could require more collateral to be posted. Another will be aggregating data across
international organizations; currently, many banks have established systems for managing
liquidity that do not combine all entities and also do not recognize all sources of liquidity
pressure.
• Over and above the regulatory minimum stress test, banks are also required to design and
carry out their own stress tests that should incorporate longer time horizons that are tailored
to their own business profiles. Banks are expected to share the results of these additional
stress tests with supervisors. This, too, will pose challenges.
• Clear policies and procedures will need to be developed to conduct the regulatory stress
tests, design appropriate internal stress tests and provide the necessary governance and
oversight. The output will also have to be embedded in the business.

Case studies in Liquidity Risk: Barclays
• Look at http://www.kamakuraco.com/Blog/tabid/231/EntryId/300/Case-Studies-
in-Liquidity-Risk-Barclays.aspx
• Discuss

LiquidityRiskManagementDay1Ses4.ppt

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