CVAR proxies for minimizing scenario-based value-at-risk

Submitted on 25th June 2015

Abstract. Minimizing VaR, as estimated from a set of scenarios, is a di -cult integer programming problem. Solving the problem to optimality may demand using only a small number of scenarios, which leads to poor out-of- sample performance. A simple alternative is to minimize CVaR for several di erent quantile levels and then to select the optimized portfolio with the best out-of-sample VaR. We show that this approach is both practical and e ective, outperforming integer programming and an existing VaR minimization heuristic. The CVaR quantile level acts as a regularization parameter and, therefore, its ideal value depends on the number of scenarios and other problem characteristics.

Quantitative Research, Risk Analytics, Business Analytics, IBM 185 Spadina Avenue, Toronto, ON M5T2C6, Canada
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Helmut Mausser and Oleksandr Romanko
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