Credit Portfolio Management: Developments & Model Implications

Credit Portfolio Management:

Developments & Model Implications

BMBF Workshop on Credit Risk Management

Christian Bluhm

Credit Portfolio Management

Credit Suisse, Zurich

Agenda

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Introduction and motivation

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Developments and challenges in credit portfolio management

and implications for credit risk modeling

Agenda

ƒ

Introduction and motivation

ƒ

Developments and challenges in credit portfolio management

and implications for credit risk modeling

Why do banks care about credit risk models?

Application of credit risk models appear in three key areas

Credit risk management Credit portfolio management Front office needs

• Measurement of the credit risk of clients, segments, and the total credit portfolio of the bank

• Limit setting, e.g., on single obligors, segments, businesses, etc. • Economic and regulatory capital measurement and management • Measurement and transparency regarding performance on risk capital • Capital allocation, etc.

• Active management and steering of the credit portfolio (includes RAP*)

• Active engagement in long and short positions in credit risks, hereby optimizing the risk/return profile of the credit portfolio (includes: hedging, securitizations, etc.) • Improvement of capital velocity and capital efficiency

• Reduction of P&L volatility, etc.

• Sophistication credit risk pricing evaluations and related issues • Structuring of credit transactions (e.g., in the bank's CDO group) • Modeling of credit risky instruments

Credit risk modeling in a nutshell

Single-name risk parameters • EAD exposure at default • PD probability of default • LGD loss given default

Portfolio risk parametrization • Linear correlations

• More general: copula function • Plus: time dynamics!

• Analytical approaches • Semi-analytic approa. • Monte Carlo simulation

• Expected and unexpected losses • Economic capital (quantile-/shortfall-based) • Risk contributions • Tranching/structuring

Best practice industry model: Bernoulli mixture model (cp. MKMV, CreditMetrics, Internal Models)

• Based on not always optimal data, parameters and calibrations of credit risk models are subject to certain uncertainties

• Therefore, strict point estimatorsas an outcome of a a credit risk model require a good amount of faith ... • Responsible credit risk modelers balancebetween the

communication of (sometimes required) exact results

and a transparent indication of the potential range of outcomes

• In critical decision making processes, decision makers

should be comfortable with a range of likely outcomes -otherwise they should keep their hands off the deal or should initiate risk limiting measuresvia syndication or

hedges or other risk transfer tools Transparent communication of the range of outcomes/solutions to senior management

Credit risk modelers attitude: transparency vs faith

illustrative

-Overall "space of outcomes"

Likely range of solutions

Example: the striking risk of copula choices

Source: http://db.riskwaters.com/public/showPage.html?page=risk_story_NewAngles_53

"Banks often use standard methodologies such as base correlation and the Gaussian copula model to price standard CDO tranches. The problem is that some people are also using them for non-standard tranches, such as bespoke portfolios" ...

The curse of "pure models" and "pure business"

• The purpose of credit risk models is the quantification of "true world" credit phenomena. Therefore, models need to be "business-challenged" and tested for their applicability and market conformity.

Models too heavily relying on pure math/statistics have rather limited chances to be successful.

• Business intuition and experience are valuable assets. But decisions based on judgements purely from a business point of view bear the inherent danger of overlooking evidence from data and models. The ideal "banker of the future" combines business intuition and experience with solid quantitative skills. Methodology and conceptual strength are essential for the success of banks in today's markets.

• Based on the two bullets above, banks have to maintain or, if not already established, implement -joint workstreams between people from the business side and people from the quant teams as an essential part of the bank's working culture.

Agenda

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Introduction and motivation

ƒ

Developments and challenges in credit portfolio management

and implications for credit risk modeling

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Conclusions

What are we going to discuss in this section?

• Several randomly picked potential future developments of credit portfolio management activities are mentioned and discussed with a focus on model implications

• The selection made in this section is not complete/exhaustive; it reflects the personal opinion of the author and does not represent the opinion of Credit Suisse

Challenge 1: mark-to-model of illiquid assets

• Large international banks with an integrated corporate & retail bank have large buy & hold subportfolios consisting of illiquid assets. Examples are:

• residential mortgages

• loans to private individuals with small own business (e.g., physicians) • loans to SME and mid-market clients

• specialized lending exposures

• Currently, most large banks have a tendency/trend to mark-to-model these illiquid portfolios. Valuations include (beside others):

• cost to hedge/securitize

• comparison with alternative capital investments

• RAROC-based model prices, calibrated to public data as (proxy for missing info on private clients) • Challenges along the way to a full mark-to-model of the bank's corporate & retail portfolio

• discrepancies between deal prices and model prices

• different risk characteristics between large corporate clients (e.g., stock exchange listed companies or bond issuers) and private companies

• lack of external opinions (like agency ratings) and cross-references/benchmarks

Model example in the context of Challenge 1

Cost to hedge/securitize

i

-Challenge 2: replacement of first generation models

Model field First generation approach Model updates

• Ratings and PDs • Classical scoring systems, e.g., balance sheet ratios mapped to scores mapped to PDs via link fct. • Static view; expert

judgement-based conversion factors, e.g., for non-cash products; draw-down factors not empirically measured • Static view; no explicit modeling

of collateral value volatility; not always properly discounted

• Time-homogeneous Markov chains

• Quantile-based; non-dynamic • Var/covar-based UL contributions • EADs • LGDs • PD term structures • Economic Capital • Risk contributions

• Switch to causal models where possible; yearly recalibration and optimization of rating systems • Sound concept of time-dynamic

exposure; empirically calibrated exposure adjustment factors (CCFs, DDFs, CEEFs, etc.)

• Calibration of LGDs to empirical loss database; anticipation of collateral market value declines in model, etc. • Non-Markov processes or

time-inhomogenenous Markov chains • Shortfall-based; P&L-related, etc. • coherent; structured prod. coverage

Model example in the context of Challenge 2

Derivation of PD term structured based on S&P data (1/3)

Model example in the context of Challenge 2

Derivation of PD term structured based on S&P data (2/3)

Model example in the context of Challenge 2

Derivation of PD term structured based on S&P data (3/3)

Challenge 3: integrated modeling of all* credit risks

-Observed default history of a considered (sub)portfolio Drivers of default frequencies

Single-name risk parameters • EAD exposure at default • PD probability of default • LGD loss given default

Portfolio risk parametrization • Linear correlations

• More general: copula function • Plus: time dynamics!

Decoupling PD estimations from portfolio risk estimates is kind of artificial ...

Model example in the context of Challenge 3

View of the portfolio modeling team View of the rating/PD modeling team

Correlation structure and rating performance (e.g., AUROC) are interrelated

An integrated view is necessary in order to estimate parameters simultaneously(single-name as well as dependence structure) in the same way as in a multivariate MLE parameters have to be estimated altogether at once and not in separate/subsequent steps ...

Challenge 4: dependence modeling/stressing

• In recent years, copula models have swamped the market

• Many different copula functions have been investigated and suggested

• Sometimes the separation of marginals from their dependence function seems rather artificial • Enthusiasm regarding copulas is great, but caution is required in several directions*

• For credit risk problems, Gaussian and Student**-t copulas provide kind of base case dependencies • In addition, several copulas can be of use in stressing/studying the impact of tail dependencies

• For instance, the Clayton copula (Archimedean: ) with

is suitable to stress lower tails in portfolio loss distributions or default times

• Other copulas can be considered, e.g., as tools for defining the outer limits of a potential outcome/solution space of a certain credit risk modeling problem (cp. Slide 5)

Model example in the context of Challenge 4

Calibration of default times

with different copula functions

• Asset A: 100 bps PD • Asset B: 50 bps PD

• PD term structures based on NHCTMC approach (Slide 14)

Agenda

ƒ

Introduction and motivation

ƒ

Developments and challenges in credit portfolio management

and implications for credit risk modeling

Concluding remarks

Never forget that besides very fancy instruments like baskets, CSOs, EDOs, and all that the classical risk-adjusted pricing scheme still is the most efficient way to steer a credit portfolio!

Always take into account that business and quant tools belong together like bread and butter.

Allow the non-quantitative world to question our methodologies and tools. There is a lot we

can learn from the business people.

Enjoy your work as a quant in finance. There is a lot banks can do with good quant work. Banks and Insurers still offer many open interesting mathematical problems which wait for solutions.