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Measuring marginal risk contributions in credit portfolios

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  • Thomas Siller

Abstract

The Fourier Transform Monte Carlo (FTMC) method, a powerful algorithm for robust computation of marginal risk contributions and capital allocations for credit portfolios in the framework of mixture models, is presented. The method outperforms results obtained from simple Monte Carlo simulations which are flawed by high variances if expected values conditional on rare events are calculated. The FTMC method exploits the conditional independence property of the underlying latent variable model and, in addition, makes use of the Fast Fourier Transform technique for risk aggregation. Marginal risk contributions for expected shortfall, value at risk and capital at risk are presented for a synthetic but realistic credit portfolio.

Suggested Citation

  • Thomas Siller, 2013. "Measuring marginal risk contributions in credit portfolios," Quantitative Finance, Taylor & Francis Journals, vol. 13(12), pages 1915-1923, December.
    • Handle: RePEc:taf:quantf:v:13:y:2013:i:12:p:1915-1923
    DOI: 10.1080/14697688.2012.742203
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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.
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    3. Dirk Tasche, 2009. "Capital allocation for credit portfolios with kernel estimators," Quantitative Finance, Taylor & Francis Journals, vol. 9(5), pages 581-595.
    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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    Cited by:

    1. Matthias Fischer & Thorsten Moser & Marius Pfeuffer, 2018. "A Discussion on Recent Risk Measures with Application to Credit Risk: Calculating Risk Contributions and Identifying Risk Concentrations," Risks, MDPI, vol. 6(4), pages 1-28, December.
    2. Takaaki Koike & Marius Hofert, 2019. "Markov Chain Monte Carlo Methods for Estimating Systemic Risk Allocations," Papers 1909.11794, arXiv.org, revised May 2020.
    3. Paulusch, Joachim & Schlütter, Sebastian, 2022. "Sensitivity-implied tail-correlation matrices," Journal of Banking & Finance, Elsevier, vol. 134(C).
    4. Koike, Takaaki & Saporito, Yuri & Targino, Rodrigo, 2022. "Avoiding zero probability events when computing Value at Risk contributions," Insurance: Mathematics and Economics, Elsevier, vol. 106(C), pages 173-192.
    5. Ji, Liuyan & Tan, Ken Seng & Yang, Fan, 2021. "Tail dependence and heavy tailedness in extreme risks," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 282-293.
    6. Targino, Rodrigo S. & Peters, Gareth W. & Shevchenko, Pavel V., 2015. "Sequential Monte Carlo Samplers for capital allocation under copula-dependent risk models," Insurance: Mathematics and Economics, Elsevier, vol. 61(C), pages 206-226.
    7. Takaaki Koike & Marius Hofert, 2020. "Markov Chain Monte Carlo Methods for Estimating Systemic Risk Allocations," Risks, MDPI, vol. 8(1), pages 1-33, January.
    8. Guoli Mo & Chunzhi Tan & Weiguo Zhang & Xuezeng Yu, 2023. "Dynamic spatiotemporal correlation coefficient based on adaptive weight," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-43, December.

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