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An approximate multi-period Vasicek credit risk model

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  • García-Céspedes, Rubén
  • Moreno, Manuel

Abstract

Financial institutions and regulators usually measure credit risk only over a one-year time horizon. Hence, current statistical models can generate closed-form expressions for the one-year loss distribution. Losses over longer horizons are considered using scenario analysis or Monte Carlo simulation. This paper proposes a simple multi-period credit risk model and uses Taylor expansion approximations to estimate the multi-period loss distribution. In this paper we extend the currently available second-order Taylor expansion approximations to credit risk with a third-order term and we use this new approximation to obtain the loss distribution in the multi-period framework. Our results show that the approximation is more accurate under recessions or for portfolios with high probability of default. We also show that, in general, the effect of this third-order adjustment is quite small.

Suggested Citation

  • García-Céspedes, Rubén & Moreno, Manuel, 2017. "An approximate multi-period Vasicek credit risk model," Journal of Banking & Finance, Elsevier, vol. 81(C), pages 105-113.
    • Handle: RePEc:eee:jbfina:v:81:y:2017:i:c:p:105-113
    DOI: 10.1016/j.jbankfin.2017.05.002
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    References listed on IDEAS

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    1. Gourieroux, C. & Laurent, J. P. & Scaillet, O., 2000. "Sensitivity analysis of Values at Risk," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 225-245, November.
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    3. Gordy, Michael B. & Marrone, James, 2012. "Granularity adjustment for mark-to-market credit risk models," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 1896-1910.
    4. Repullo, Rafael, 2013. "Cyclical adjustment of capital requirements: A simple framework," Journal of Financial Intermediation, Elsevier, vol. 22(4), pages 608-626.
    5. Mikhail Voropaev, 2009. "Analytical Framework for Credit Portfolios. Part I: Systematic Risk," Papers 0911.0223, arXiv.org, revised Jul 2011.
    6. Paul Glasserman & Jingyi Li, 2005. "Importance Sampling for Portfolio Credit Risk," Management Science, INFORMS, vol. 51(11), pages 1643-1656, November.
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    Cited by:

    1. 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.
    2. Rubén García-Céspedes & Manuel Moreno, 2020. "Random LGD adjustments in the Vasicek credit risk model," The European Journal of Finance, Taylor & Francis Journals, vol. 26(18), pages 1856-1875, December.

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    More about this item

    Keywords

    Finance; Credit risk; Approximate methods; Multi-period models;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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