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State dependent correlations in the Vasicek default model

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  • Metzler A.

    (Department of Mathematics, Wilfrid Laurier University)

Abstract

This paper incorporates state dependent correlations (those that vary systematically with the state of the economy) into the Vasicek default model. Other approaches to randomizing correlation in the Vasicek model have either assumed that correlation is independent of the systematic risk factor (zero state dependence) or is an explicit function of the systematic risk factor (perfect state dependence). By contrast, our approach allows for an arbitrary degree of state dependence and includes both zero and perfect state dependence as special cases. This is accomplished by expressing the factor loading as a function of an auxiliary (Gaussian) variable that is correlated with the systematic risk factor. Using Federal Reserve data on delinquency rates we use maximum likelihood to estimate the parameters of the model, and find the empirical degree of state dependence to be quite high (but generally not perfect). We also find that randomizing correlation, without allowing for state dependence, does not improve the empirical performance of the Vasicek model.

Suggested Citation

  • Metzler A., 2020. "State dependent correlations in the Vasicek default model," Dependence Modeling, De Gruyter, vol. 8(1), pages 298-329, January.
  • Handle: RePEc:vrs:demode:v:8:y:2020:i:1:p:298-329:n:6
    DOI: 10.1515/demo-2020-0017
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    References listed on IDEAS

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    1. François Longin & Bruno Solnik, 2001. "Extreme Correlation of International Equity Markets," Journal of Finance, American Finance Association, vol. 56(2), pages 649-676, April.
    2. Marek Rutkowski & Silvio Tarca, 2015. "Regulatory Capital Modeling For Credit Risk," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 18(05), pages 1-44.
    3. Jiri Witzany, 2013. "A Note on the Vasicek’s Model with the Logistic Distribution," Working Papers IES 2013/01, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Jan 2013.
    4. Barry Arnold & Robert Beaver & Richard Groeneveld & William Meeker, 1993. "The nontruncated marginal of a truncated bivariate normal distribution," Psychometrika, Springer;The Psychometric Society, vol. 58(3), pages 471-488, September.
    5. Gordy, Michael B., 2003. "A risk-factor model foundation for ratings-based bank capital rules," Journal of Financial Intermediation, Elsevier, vol. 12(3), pages 199-232, July.
    6. Kofman, Paul & Koedijk, Kees & Campbell, Rachel, 2002. "Increased Correlation in Bear markets: A Downside Risk Perspective," CEPR Discussion Papers 3172, C.E.P.R. Discussion Papers.
    7. Scott, Alexandre & Metzler, Adam, 2015. "A general importance sampling algorithm for estimating portfolio loss probabilities in linear factor models," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 279-293.
    8. Puccetti Giovanni & Scherer Matthias, 2018. "Copulas, credit portfolios, and the broken heart syndrome," Dependence Modeling, De Gruyter, vol. 6(1), pages 114-130, June.
    9. 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.
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