## Portfolio Credit Risk and Macroeconomic Shocks: Applications to Stress Testing Under Data-Restricted Environments

_IMF Working Papers, December 1, 2006_

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## Bibliographic details
- Authors: Miguel A. Segoviano
- Published: December 1, 2006
- Series: IMF Working Papers
- DOI: https://doi.org/10.5089/9781451865431.001

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### Summary and purpose
- Portfolio credit risk measurement is greatly affected by data constraints, especially for loans to unlisted firms.
- Standard methodologies often adopt convenient, but not necessarily properly specified parametric distributions or ignore the effects of macroeconomic shocks on credit risk.
- The paper proposes the joint implementation of two methodologies designed to improve portfolio credit risk measurement under data restrictions: the conditional probability of default (CoPoD) methodology and the consistent information multivariate density optimizing (CIMDO) methodology.
- Implementation is presented as straightforward and particularly useful in stress testing exercises (STEs), illustrated by an STE carried out within the Danish Financial Sector Assessment Program.

### Methodologies proposed
- Conditional Probability of Default (CoPoD)
  - Incorporates the effects of macroeconomic shocks into credit risk.
  - Recovers robust estimators when only short time series of loans exist.
- Consistent Information Multivariate Density Optimizing (CIMDO)
  - Recovers portfolio multivariate distributions on which portfolio credit risk measurement relies.
  - Provides improved specifications when only partial information about borrowers is available.

### Key findings and analytical contributions
- Data constraints materially affect measurement of portfolio credit risk; commonly used parametric assumptions may be misspecified.
- Incorporating macroeconomic shocks via CoPoD leads to more robust default probability estimation in short time-series environments.
- Recovering multivariate portfolio distributions with CIMDO improves the specification of loss distributions when borrower-level information is incomplete.
- The joint use of CoPoD and CIMDO enhances the reliability of stress testing under data-restricted environments.

### Applications and examples
- The methodologies are illustrated through their application in a stress testing exercise within the Danish Financial Sector Assessment Program (Denmark and the IMF context noted).
- Implementation is described as straightforward for STE practitioners facing partial borrower information and short loan time series.

### Subject coverage and keywords
- Subjects: Asset and liability management; Asset valuation; Banking; Credit; Credit risk; Financial institutions; Financial regulation and supervision; Financial sector policy and analysis; Loans; Money; Stress testing.
- Keywords: Asset valuation; bank portfolio UL; capital adequacy ratio; concentration effect; Credit; credit portfolio; Credit risk; credit risk modeling; credit risk quality; economic theory; entropy distribution; Europe; importance of portfolio credit risk; loan default; loan portfolio; Loans; loss distribution; macroeconomic shock measurement; multivariate density estimation; multivariate distribution; portfolio credit risk; Portfolio credit risk measurement; stress testing; time series; WP.

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_Source: https://www.imf.org/en/publications/wp/issues/2016/12/31/portfolio-credit-risk-and-macroeconomic-shocks-applications-to-stress-testing-under-data-20064_