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Capital allocation for credit portfolios with kernel estimators

Author

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  • Dirk Tasche

Abstract

Determining the contributions of sub-portfolios or single exposures to portfolio-wide economic capital for credit risk is an important risk measurement task. Often, economic capital is measured as the Value-at-Risk (VaR) of the portfolio loss distribution. For many of the credit portfolio risk models used in practice, the VaR contributions then have to be estimated from Monte Carlo samples. In the context of a partly continuous loss distribution (i.e. continuous except for a positive point mass on zero), we investigate how to combine kernel estimation methods with importance sampling to achieve more efficient (i.e. less volatile) estimation of VaR contributions.

Suggested Citation

  • Dirk Tasche, 2009. "Capital allocation for credit portfolios with kernel estimators," Quantitative Finance, Taylor & Francis Journals, vol. 9(5), pages 581-595.
    • Handle: RePEc:taf:quantf:v:9:y:2009:i:5:p:581-595
    DOI: 10.1080/14697680802620599
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    References listed on IDEAS

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    1. Acerbi, Carlo & Tasche, Dirk, 2002. "On the coherence of expected shortfall," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1487-1503, July.
    2. 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.
    3. Paul Glasserman & Jingyi Li, 2005. "Importance Sampling for Portfolio Credit Risk," Management Science, INFORMS, vol. 51(11), pages 1643-1656, November.
    4. Sandro Merino & Mark Nyfeler, 2004. "Applying importance sampling for estimating coherent credit risk contributions," Quantitative Finance, Taylor & Francis Journals, vol. 4(2), pages 199-207.
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    Citations

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    Cited by:

    1. M. Dietsch & C. Welter-Nicol, 2014. "Do LTV and DSTI caps make banks more resilient?," Débats économiques et financiers 13, Banque de France.
    2. Guangwu Liu, 2015. "Simulating Risk Contributions of Credit Portfolios," Operations Research, INFORMS, vol. 63(1), pages 104-121, February.
    3. So Yeon Chun & Miguel A. Lejeune, 2020. "Risk-Based Loan Pricing: Portfolio Optimization Approach with Marginal Risk Contribution," Management Science, INFORMS, vol. 66(8), pages 3735-3753, August.
    4. Thomas Siller, 2013. "Measuring marginal risk contributions in credit portfolios," Quantitative Finance, Taylor & Francis Journals, vol. 13(12), pages 1915-1923, December.
    5. Leitao, Álvaro & Ortiz-Gracia, Luis, 2020. "Model-free computation of risk contributions in credit portfolios," Applied Mathematics and Computation, Elsevier, vol. 382(C).
    6. Karabey, Uǧur & Kleinow, Torsten & Cairns, Andrew J.G., 2014. "Factor risk quantification in annuity models," Insurance: Mathematics and Economics, Elsevier, vol. 58(C), pages 34-45.
    7. Dietsch, Michel & Petey, Joël, 2015. "The credit-risk implications of home ownership promotion: The effects of public subsidies and adjustable-rate loans," Journal of Housing Economics, Elsevier, vol. 28(C), pages 103-120.
    8. Takaaki Koike & Mihoko Minami, 2017. "Estimation of Risk Contributions with MCMC," Papers 1702.03098, arXiv.org, revised Jan 2019.
    9. Laurent, Jean-Paul & Sestier, Michael & Thomas, Stéphane, 2016. "Trading book and credit risk: How fundamental is the Basel review?," Journal of Banking & Finance, Elsevier, vol. 73(C), pages 211-223.

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