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Applying importance sampling for estimating coherent credit risk contributions

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  • Sandro Merino
  • Mark Nyfeler

Abstract

A Monte Carlo simulation method based on importance sampling is applied to the problem of determining individual risk contributions of the obligors in a credit portfolio. The effectiveness of the method is benchmarked against standard Monte Carlo techniques and the asymptotic optimality of the method is proved. The risk measure adopted is expected shortfall, a particualr coherent risk measure. The concept of a coherent risk spectrum is discussed on the basis of some numerical examples.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:quantf:v:4:y:2004:i:2:p:199-207
    DOI: 10.1080/14697680400000024
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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. Frey, Rudiger & McNeil, Alexander J., 2002. "VaR and expected shortfall in portfolios of dependent credit risks: Conceptual and practical insights," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1317-1334, July.
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    Cited by:

    1. Parrini, Alessandro, 2013. "Importance Sampling for Portfolio Credit Risk in Factor Copula Models," MPRA Paper 103745, University Library of Munich, Germany.
    2. Rosen, Dan & Saunders, David, 2010. "Risk factor contributions in portfolio credit risk models," Journal of Banking & Finance, Elsevier, vol. 34(2), pages 336-349, February.
    3. Guangwu Liu, 2015. "Simulating Risk Contributions of Credit Portfolios," Operations Research, INFORMS, vol. 63(1), pages 104-121, February.
    4. Sunggon Kim & Jisu Yu, 2023. "Stratified importance sampling for a Bernoulli mixture model of portfolio credit risk," Annals of Operations Research, Springer, vol. 322(2), pages 819-849, March.
    5. Thomas Siller, 2013. "Measuring marginal risk contributions in credit portfolios," Quantitative Finance, Taylor & Francis Journals, vol. 13(12), pages 1915-1923, December.
    6. Ferrer, Alex & Casals, José & Sotoca, Sonia, 2016. "Efficient estimation of unconditional capital by Monte Carlo simulation," Finance Research Letters, Elsevier, vol. 16(C), pages 75-84.
    7. Giuseppe Genovese & Ashkan Nikeghbali & Nicola Serra & Gabriele Visentin, 2022. "Universal approximation of credit portfolio losses using Restricted Boltzmann Machines," Papers 2202.11060, arXiv.org, revised Apr 2023.
    8. Dirk Tasche, 2009. "Capital allocation for credit portfolios with kernel estimators," Quantitative Finance, Taylor & Francis Journals, vol. 9(5), pages 581-595.
    9. Giannopoulos, Kostas & Tunaru, Radu, 2005. "Coherent risk measures under filtered historical simulation," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 979-996, April.
    10. Grundke, Peter, 2009. "Importance sampling for integrated market and credit portfolio models," European Journal of Operational Research, Elsevier, vol. 194(1), pages 206-226, April.
    11. Paul Glasserman & Wanmo Kang & Perwez Shahabuddin, 2008. "Fast Simulation of Multifactor Portfolio Credit Risk," Operations Research, INFORMS, vol. 56(5), pages 1200-1217, October.
    12. Alejandro Ferrer Pérez & José Casals Carro & Sonia Sotoca López, 2014. "Linking the problems of estimating and allocating unconditional capital," Documentos de Trabajo del ICAE 2014-13, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    13. Cheridito, Patrick & Stadje, Mitja, 2009. "Time-inconsistency of VaR and time-consistent alternatives," Finance Research Letters, Elsevier, vol. 6(1), pages 40-46, March.

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