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Measurement and Calibration of Regulatory Credit Risk Asset Correlations

Author

Listed:
  • Anton van Dyk

    (Department of Mathematics and Applied Mathematics, University of Pretoria, Pretoria 0028, South Africa
    RiskWorx, Johannesburg 2031, South Africa)

  • Gary van Vuuren

    (Centre for Business Mathematics and Informatics, Potchefstroom Campus, North-West University, Potchefstroom 2520, South Africa)

Abstract

Vasicek’s asymptotic single risk factor (ASRF) model is employed by the Basel Committee on Banking Supervision (BCBS) in its internal ratings-based (IRB) approach for estimating credit losses and regulatory credit risk capital. This methodology requires estimates of asset correlations; these are prescribed by the BCBS. Practitioners are interested to know market-implied asset correlations since these influence economic capital and lending behavior. These may be backed out from ASRF loan loss distributions using ex post loan losses. Prescribed asset correlations have been neither updated nor recalibrated since their introduction in 2008 with the implementation of the Basel II accord. The market milieu has undergone significant alterations and adaptations since then; it is unlikely that these remain relevant. Loan loss data from a developed (US) and developing (South Africa) economy spanning at least two business cycles for each region were used to explore the relevance of the BCBS calibration. Results obtained from three alternative methodologies are compared with prescribed BCBS values, and the latter were found to be countercyclical to empirical loan loss experience, resulting in less punitive credit risk capital requirements than required in market crises and more punitive requirements than required in calm conditions.

Suggested Citation

  • Anton van Dyk & Gary van Vuuren, 2023. "Measurement and Calibration of Regulatory Credit Risk Asset Correlations," JRFM, MDPI, vol. 16(9), pages 1-19, September.
  • Handle: RePEc:gam:jjrfmx:v:16:y:2023:i:9:p:402-:d:1235319
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    References listed on IDEAS

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