Worst-Case Value-At-Risk and Robust Portfolio Optimization: A Conic Programming Approach

  title={Worst-Case Value-At-Risk and Robust Portfolio Optimization: A Conic Programming Approach},
  author={Laurent El Ghaoui and Maksim Oks and François Oustry},
  journal={Oper. Res.},
Classical formulations of the portfolio optimization problem, such as mean-variance or Value-at-Risk (VaR) approaches, can result in a portfolio extremely sensitive to errors in the data, such as mean and covariance matrix of the returns. In this paper we propose a way to alleviate this problem in a tractable manner. We assume that the distribution of returns is partially known, in the sense that onlybounds on the mean and covariance matrix are available. We define the worst-case Value-at-Risk… 

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