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Risk factor contributions in portfolio credit risk models

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  • Rosen, Dan
  • Saunders, David

Abstract

Determining contributions to overall portfolio risk is an important topic in risk management. For positions (instruments and sub-portfolios), this problem has been well studied, and a significant theory built, around the calculation of marginal contributions. We consider the problem of determining the contributions to portfolio risk of risk factors. This cannot be addressed through an immediate extension of techniques for position contributions, since the portfolio loss is a nonlinear function of the risk factors. We employ the Hoeffding decomposition of the portfolio loss into a sum of terms depending on the factors. This decomposition restores linearity, but includes terms arising from joint effects of groups of factors. These cross-factor terms provide information to risk managers, since they can be viewed as best hedges of the portfolio loss involving instruments of increasing complexity. We illustrate the technique on multi-factor portfolio credit risk models, where systematic factors represent industries, geographical sectors, etc.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:jbfina:v:34:y:2010:i:2:p:336-349
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Kao, Lie-Jane, 2015. "A portfolio-invariant capital allocation scheme penalizing concentration risk," Economic Modelling, Elsevier, vol. 51(C), pages 560-570.
    3. Lee, Yongwoong & Poon, Ser-Huang, 2014. "Forecasting and decomposition of portfolio credit risk using macroeconomic and frailty factors," Journal of Economic Dynamics and Control, Elsevier, vol. 41(C), pages 69-92.
    4. Arvid Raknerud & Bjørn Helge Vatne, 2013. "The relations between bank-funding costs, retail rates, and loan volumes. Evidence form Norwegian microdata," Discussion Papers 742, Statistics Norway, Research Department.
    5. Peter Miu & Bogie Ozdemir & Evren Cubukgil & Michael Giesinger, 2016. "Determining Hurdle Rate and Capital Allocation in Credit Portfolio Management," Journal of Financial Services Research, Springer;Western Finance Association, vol. 50(2), pages 243-273, October.
    6. Aussenegg, Wolfgang & Resch, Florian & Winkler, Gerhard, 2011. "Pitfalls and remedies in testing the calibration quality of rating systems," Journal of Banking & Finance, Elsevier, vol. 35(3), pages 698-708, March.
    7. Arvid Raknerud & Bjørn Helge Vatne, 2012. "The relation between banks' funding costs, retail rates and loan volumes: An analysis of Norwegian bank micro data," Working Paper 2012/17, Norges Bank.
    8. 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.
    9. Buch, Arne & Dorfleitner, Gregor & Wimmer, Maximilian, 2011. "Risk capital allocation for RORAC optimization," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 3001-3009, November.
    10. repec:kap:rqfnac:v:49:y:2017:i:4:d:10.1007_s11156-016-0613-x is not listed on IDEAS
    11. Lu, Meng-Jou & Chen, Cathy Yi-Hsuan & Härdle, Karl Wolfgang & Härdle, 2015. "Copula-Based Factor Model for Credit Risk Analysis," SFB 649 Discussion Papers SFB649DP2015-042, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    12. 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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