Algorithms for Optimization of Value-at-Risk
This paper suggests two new heuristic algorithms for optimization of Value-at-Risk (VaR). By definition, VaR is an estimate of the maximum portfolio loss during a standardized period with some confidence level. The optimization algo- rithms are based on the minimization of the closely related risk measure Conditional Value-at-Risk (CVaR). For continuous distributions, CVaR is the expected loss exceeding VaR, and is also known as Mean Excess Loss or Expected Shortfall. For discrete distributions, CVaR is the weighted average of VaR and losses exceeding VaR. CVaR is an upper bound for VaR, therefore, minimization of CVaR also reduces VaR. The algorithms are tested by minimizing the credit risk of a portfolio of emerging market bonds. Numerical experiments showed that the algorithms are efficient and can handle a large number of instruments and scenarios. However, calculations identified a deficiency of VaR risk measure, compared to CVaR. Minimization of VaR leads to an undesirable stretch of the tail of the distribution exceeding VaR. For portfolios with skewed distributions, such as credit risk, minimization of VaR may result in a significant increase of high losses exceeding VaR. For the credit risk problem studied in this paper, VaR minimization leads to about 16% increase of the average loss for the worst 1% scenarios (compared to the worst 1% scenarios in CVaR minimum solution). 1% includes 200 of 20000 scenarios, which were used for estimating credit risk in this case study.
KeywordsSupply Chain Credit Risk Active Scenario Expect Shortfall Financial Engineer
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