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Estimating Default and Recovery Rate Correlations

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Abstract

The paper analyzes a two-factor credit risk model allowing to capture default and recovery rate variation, their mutual correlation, and dependence on various explanatory variables. At the same time, it allows computing analytically the unexpected credit loss. We propose and empirically implement estimation of the model based on aggregate and exposure level Moody’s default and recovery data. The results confirm existence of significantly positive default and recovery rate correlation. We empirically compare the unexpected loss estimates based on the reduced two-factor model with Monte Carlo simulation results, and with the current regulatory formula outputs. The results show a very good performance of the proposed analytical formula which could feasibly replace the current regulatory formula.

Suggested Citation

  • Jiri Witzany, 2013. "Estimating Default and Recovery Rate Correlations," Working Papers IES 2013/03, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Apr 2013.
    • Handle: RePEc:fau:wpaper:wp2013_03
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    File URL: http://ies.fsv.cuni.cz/sci/publication/show/id/4830/lang/cs
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    References listed on IDEAS

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    1. Acharya, Viral V. & Bharath, Sreedhar T. & Srinivasan, Anand, 2007. "Does industry-wide distress affect defaulted firms? Evidence from creditor recoveries," Journal of Financial Economics, Elsevier, vol. 85(3), pages 787-821, September.
    2. repec:uts:ppaper:v:18:y:2009:i:1:p:1-26 is not listed on IDEAS
    3. Daniel Rösch & Harald Scheule, 2009. "Credit Portfolio Loss Forecasts for Economic Downturns," Financial Markets, Institutions & Instruments, John Wiley & Sons, vol. 18(1), pages 1-26, February.
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    5. Jon Frye, 2000. "Depressing recoveries," Emerging Issues, Federal Reserve Bank of Chicago, issue Oct.
    6. Stefano Caselli & Stefano Gatti & Francesca Querci, 2008. "The Sensitivity of the Loss Given Default Rate to Systematic Risk: New Empirical Evidence on Bank Loans," Journal of Financial Services Research, Springer;Western Finance Association, vol. 34(1), pages 1-34, August.
    7. Benjamin Bade & Daniel Rösch & Harald Scheule, 2011. "Default and Recovery Risk Dependencies in a Simple Credit Risk Model," European Financial Management, European Financial Management Association, vol. 17(1), pages 120-144, January.
    8. Seidler, Jakub & Horvath, Roman & Jakubík, Petr, 2009. "Estimating expected loss given default in an emerging market: the case of Czech Republic," Journal of Financial Transformation, Capco Institute, vol. 27, pages 103-107.
    9. De Graeve, F. & Kick, T. & Koetter, M., 2008. "Monetary policy and financial (in)stability: An integrated micro-macro approach," Journal of Financial Stability, Elsevier, vol. 4(3), pages 205-231, September.
    10. Dirk Tasche, 2004. "The single risk factor approach to capital charges in case of correlated loss given default rates," Papers cond-mat/0402390, arXiv.org, revised Feb 2004.
    11. Konstantin Belyaev & Aelita Belyaeva & Tomas Konecny & Jakub Seidler & Martin Vojtek, 2012. "Macroeconomic Factors as Drivers of LGD Prediction: Empirical Evidence from the Czech Republic," Working Papers 2012/12, Czech National Bank.
    12. Jiří Witzany, 2017. "Credit Risk Management," Springer Books, Springer, number 978-3-319-49800-3, July.
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    Cited by:

    1. Franco Varetto, 2017. "La correlazione tra PD ed LGD nell’analisi del rischio di credito/The correlation between probability of default and loss given default in the credit risk analysis," IRCrES Working Paper 201714, CNR-IRCrES Research Institute on Sustainable Economic Growth - Moncalieri (TO) ITALY - former Institute for Economic Research on Firms and Growth - Torino (TO) ITALY.
    2. Andrey Itkin & Fazlollah Soleymani, 2019. "Four-factor model of Quanto CDS with jumps-at-default and stochastic recovery," Papers 1912.08713, arXiv.org.

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    More about this item

    Keywords

    credit risk; Basel II regulation; default rates; recovery rates; correlation;
    All these keywords.

    JEL classification:

    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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