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Specification and Calibration Errors in Measures of Portfolio Credit Risk: The Case of the ASRF Model

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  • Nikola Tarashev

    (Monetary and Economic Department, Bank for International Settlements)

  • Haibin Zhu

    (Monetary and Economic Department, Bank for International Settlements)

Abstract

This paper focuses on the asymptotic single-risk-factor (ASRF) model in order to analyze the impact of specification and calibration errors on popular measures of portfolio credit risk. Violations of key assumptions of this model are found to be virtually inconsequential, especially for large, welldiversified portfolios. By contrast, flaws in the calibrated interdependence of credit risk across exposures, caused by plausible small-sample estimation errors or rule-of-thumb values of asset return correlations, can lead to significant inaccuracies in measures of portfolio credit risk. Similar inaccuracies arise under standard assumptions regarding the tails of the distribution of asset returns.

Suggested Citation

  • Nikola Tarashev & Haibin Zhu, 2008. "Specification and Calibration Errors in Measures of Portfolio Credit Risk: The Case of the ASRF Model," International Journal of Central Banking, International Journal of Central Banking, vol. 4(2), pages 129-173, June.
    • Handle: RePEc:ijc:ijcjou:y:2008:q:2:a:4
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    3. Simone Varotto, 2008. "An Assessment of the Internal Rating Based Approach in Basel II," ICMA Centre Discussion Papers in Finance icma-dp2008-04, Henley Business School, University of Reading.
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    5. Metzler A., 2020. "State dependent correlations in the Vasicek default model," Dependence Modeling, De Gruyter, vol. 8(1), pages 298-329, January.
    6. Huang, Xin & Zhou, Hao & Zhu, Haibin, 2012. "Assessing the systemic risk of a heterogeneous portfolio of banks during the recent financial crisis," Journal of Financial Stability, Elsevier, vol. 8(3), pages 193-205.
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    10. Antonio Di Cesare & Anna Rogantini Picco, 2018. "A Survey of Systemic Risk Indicators," Questioni di Economia e Finanza (Occasional Papers) 458, Bank of Italy, Economic Research and International Relations Area.
    11. Yan, Meilan & Hall, Maximilian J.B. & Turner, Paul, 2012. "A cost–benefit analysis of Basel III: Some evidence from the UK," International Review of Financial Analysis, Elsevier, vol. 25(C), pages 73-82.
    12. Barbagli, Matteo & Vrins, Frédéric, 2023. "Accounting for PD-LGD dependency: A tractable extension to the Basel ASRF framework," Economic Modelling, Elsevier, vol. 125(C).
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    14. Marina Brogi & Valentina Lagasio & Luca Riccetti, 2021. "Systemic risk measurement: bucketing global systemically important banks," Annals of Finance, Springer, vol. 17(3), pages 319-351, September.
    15. M. B. Gordy & E. Lutkebohmert, 2013. "Granularity Adjustment for Regulatory Capital Assessment," International Journal of Central Banking, International Journal of Central Banking, vol. 9(3), pages 38-77, September.
    16. Drehmann, Mathias & Tarashev, Nikola, 2013. "Measuring the systemic importance of interconnected banks," Journal of Financial Intermediation, Elsevier, vol. 22(4), pages 586-607.
    17. Lee, Yongwoong & Rösch, Daniel & Scheule, Harald, 2016. "Accuracy of mortgage portfolio risk forecasts during financial crises," European Journal of Operational Research, Elsevier, vol. 249(2), pages 440-456.
    18. Kao, Lie-Jane, 2015. "A portfolio-invariant capital allocation scheme penalizing concentration risk," Economic Modelling, Elsevier, vol. 51(C), pages 560-570.
    19. Henry Penikas, 2023. "Unaccounted model risk for Basel IRB models deemed acceptable by conventional validation criteria," Risk Management, Palgrave Macmillan, vol. 25(4), pages 1-25, December.
    20. Gourieroux, C. & Jasiak, J., 2012. "Granularity adjustment for default risk factor model with cohorts," Journal of Banking & Finance, Elsevier, vol. 36(5), pages 1464-1477.
    21. Daniel Rösch & Harald Scheule, 2014. "Forecasting Mortgage Securitization Risk Under Systematic Risk and Parameter Uncertainty," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 81(3), pages 563-586, September.
    22. Henry Penikas, 2022. "Model Risk for Acceptable, but Imperfect, Discrimination and Calibration in Basel PD and LGD Models," Bank of Russia Working Paper Series wps92, Bank of Russia.
    23. Eva Lutkebohmert & Julian Sester, 2024. "Measuring Name Concentrations through Deep Learning," Papers 2403.16525, arXiv.org, revised Apr 2024.

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    More about this item

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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