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Confidence Levels for CVaR Risk Measures and Minimax Limits

Edward Anderson (edward.anderson***at***sydney.edu.au)
Huifu Xu (Huifu.Xu.1***at***city.ac.uk)
Dali Zhang ( dali.zhang.ustc***at***gmail.com)

Abstract: Conditional value at risk (CVaR) has been widely used as a risk measure in finance. When the confidence level of CVaR is set close to 1, the CVaR risk measure approximates the extreme (worst scenario) risk measure. In this paper, we present a quantitative analysis of the relationship between the two risk measures and its impact on optimal decision making when we wish to minimize the respective risks measures. We also investigate the difference between the optimal solutions to the two optimization problems with identical objective function but under constraints on the two risk measures. We discuss the benefits of a sample average approximation scheme for the CVaR constraints and investigate the convergence of the optimal solution obtained from this scheme as the sample size increases. We use some portfolio optimization problems to investigate the performance of the CVaR approximation approach. Our numerical results demonstrate how reducing the confidence level can lead to better overall performance.

Keywords: CVaR approximation; robust optimization; minimax; semi-infinite programming; distributional robust optimization; sample average approximation

Category 1: Robust Optimization

Category 2: Infinite Dimensional Optimization (Semi-infinite Programming )

Category 3: Stochastic Programming


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Entry Submitted: 05/16/2013
Entry Accepted: 05/16/2013
Entry Last Modified: 01/23/2014

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