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Credit risk optimization with Conditional Value-at-Risk criterion

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Abstract.

This paper examines a new approach for credit risk optimization. The model is based on the Conditional Value-at-Risk (CVaR) risk measure, the expected loss exceeding Value-at-Risk. CVaR is also known as Mean Excess, Mean Shortfall, or Tail VaR. This model can simultaneously adjust all positions in a portfolio of financial instruments in order to minimize CVaR subject to trading and return constraints. The credit risk distribution is generated by Monte Carlo simulations and the optimization problem is solved effectively by linear programming. The algorithm is very efficient; it can handle hundreds of instruments and thousands of scenarios in reasonable computer time. The approach is demonstrated with a portfolio of emerging market bonds.

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Authors and Affiliations

  1. Ementor, Stortorget 1, 111 29 Stockholm, Sweden, e-mail: fredrik.andersson@ementor.se, web: http://www.ementor.se, , , , , , SE

    Fredrik Andersson

  2. Algorithmics, Inc., 185 Spadina Avenue, Toronto, Ontario M5T 2C6, Canada, web: http://www.algorithmics.com, , , , , , CA

    Helmut Mausser & Dan Rosen

  3. University of Florida, Dept. of Industrial and Systems Engineering, PO Box 116595, 303 Weil Hall, Gainesville, FL 32611-6595, e-mail: uryasev@ise.ufl.edu, web: http://www.ise.ufl.edu/uryasev, , , , , , US

    Stanislav Uryasev

Authors
  1. Fredrik Andersson
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  2. Helmut Mausser
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  3. Dan Rosen
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  4. Stanislav Uryasev
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Received: November 1, 1999 / Accepted: October 1, 2000¶Published online December 15, 2000

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Andersson, F., Mausser, H., Rosen, D. et al. Credit risk optimization with Conditional Value-at-Risk criterion. Math. Program. 89, 273–291 (2001). https://doi.org/10.1007/PL00011399

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  • DOI: https://doi.org/10.1007/PL00011399

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