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Portfolio credit-risk optimization

Author

Listed:
  • Iscoe, Ian
  • Kreinin, Alexander
  • Mausser, Helmut
  • Romanko, Oleksandr

Abstract

This paper evaluates several alternative formulations for minimizing the credit risk of a portfolio of financial contracts with different counterparties. Credit risk optimization is challenging because the portfolio loss distribution is typically unavailable in closed form. This makes it difficult to accurately compute Value-at-Risk (VaR) and expected shortfall (ES) at the extreme quantiles that are of practical interest to financial institutions. Our formulations all exploit the conditional independence of counterparties under a structural credit risk model. We consider various approximations to the conditional portfolio loss distribution and formulate VaR and ES minimization problems for each case. We use two realistic credit portfolios to assess the in- and out-of-sample performance for the resulting VaR- and ES-optimized portfolios, as well as for those which we obtain by minimizing the variance or the second moment of the portfolio losses. We find that a Normal approximation to the conditional loss distribution performs best from a practical standpoint.

Suggested Citation

  • Iscoe, Ian & Kreinin, Alexander & Mausser, Helmut & Romanko, Oleksandr, 2012. "Portfolio credit-risk optimization," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1604-1615.
  • Handle: RePEc:eee:jbfina:v:36:y:2012:i:6:p:1604-1615
    DOI: 10.1016/j.jbankfin.2012.01.013
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    References listed on IDEAS

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    1. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    2. Alexander, S. & Coleman, T.F. & Li, Y., 2006. "Minimizing CVaR and VaR for a portfolio of derivatives," Journal of Banking & Finance, Elsevier, vol. 30(2), pages 583-605, February.
    3. Norbert Jobst & Stavros A. Zenios, 2001. "The Tail that Wags the Dog: Integrating Credit Risk in Asset Portfolios," Center for Financial Institutions Working Papers 01-24, Wharton School Center for Financial Institutions, University of Pennsylvania.
    4. Tilke, Stephan, 2006. "Reducing Asset Weights' Volatility by Importance Sampling in Stochastic Credit Portfolio Optimization," University of Regensburg Working Papers in Business, Economics and Management Information Systems 417, University of Regensburg, Department of Economics.
    5. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
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    Citations

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    Cited by:

    1. Nielsen, Caren Yinxia, 2016. "Banks' Credit-Portfolio Choices and Risk-Based Capital Regulation," Working Papers 2016:9, Lund University, Department of Economics.
    2. Yinxia Nielsen, Caren, 2015. "Banks’ credit-portfolio choices and riskbased capital regulation," Knut Wicksell Working Paper Series 2015/8, Lund University, Knut Wicksell Centre for Financial Studies.
    3. Changqing Luo & Mengzhen Li & Zisheng Ouyang, 2016. "An Empirical Study on the Correlation Structure of Credit Spreads based on the Dynamic and Pair Copula Functions," China Finance Review International, Emerald Group Publishing, vol. 6(3), pages 284-303, August.
    4. Vladimir Rankovic & Mikica Drenovak & Branko Uroševic & Ranko Jelic, 2016. "Mean Univariate-GARCH VaR Portfolio Optimization: Actual Portfolio Approach," CESifo Working Paper Series 5731, CESifo Group Munich.

    More about this item

    Keywords

    Credit risk; Optimization; Portfolio optimization; Risk modeling; Value-at-Risk; Expected shortfall;

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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