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Mathematical Programming

, Volume 89, Issue 2, pp 273–291 | Cite as

Credit risk optimization with Conditional Value-at-Risk criterion

  • Fredrik Andersson
  • Helmut Mausser
  • Dan Rosen
  • Stanislav Uryasev

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.

Mathematics Subject Classification (1991): 20E28, 20G40, 20C20 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Fredrik Andersson
    • 1
  • Helmut Mausser
    • 2
  • Dan Rosen
    • 2
  • Stanislav Uryasev
    • 3
  1. 1.Ementor, Stortorget 1, 111 29 Stockholm, Sweden, e-mail: fredrik.andersson@ementor.se, web: http://www.ementor.seSE
  2. 2.Algorithmics, Inc., 185 Spadina Avenue, Toronto, Ontario M5T 2C6, Canada, web: http://www.algorithmics.comCA
  3. 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/uryasevUS

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