Credit risk optimization with Conditional Value-at-Risk criterion |
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| Authors: | Fredrik Andersson Helmut Mausser Dan Rosen Stanislav Uryasev |
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| Institution: | (1) Ementor, Stortorget 1, 111 29 Stockholm, Sweden, e-mail: fredrik.andersson@ementor.se, web: http://www.ementor.se, SE;(2) Algorithmics, Inc., 185 Spadina Avenue, Toronto, Ontario M5T 2C6, Canada, web: http://www.algorithmics.com, CA;(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 |
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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.
Received: November 1, 1999 / Accepted: October 1, 2000?Published online December 15, 2000 |
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| Keywords: | Mathematics Subject Classification (1991): 20E28 20G40 20C20 |
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