An exact solution to a robust portfolio choice problem with multiple risk measures under ambiguous distribution

Zhilin Kang () and Zhongfei Li ()
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Zhilin Kang: Sun Yat-sen University
Zhongfei Li: Sun Yat-sen University

Mathematical Methods of Operations Research, 2018, vol. 87, issue 2, No 1, 169-195

Abstract: Abstract This paper proposes a unified framework to solve distributionally robust mean-risk optimization problem that simultaneously uses variance, value-at-risk (VaR) and conditional value-at-risk (CVaR) as a triple-risk measure. It provides investors with more flexibility to find portfolios in the sense that it allows investors to optimize a return-risk profile in the presence of estimation error. We derive a closed-form expression for the optimal portfolio strategy to the robust mean-multiple risk portfolio selection model under distribution and mean return ambiguity (RMP). Specially, the robust mean-variance, robust maximum return, robust minimum VaR and robust minimum CVaR efficient portfolios are all special instances of RMP portfolios. We analytically and numerically show that the resulting portfolio weight converges to the minimum variance portfolio when the level of ambiguity aversion is in a high value. Using numerical experiment with simulated data, we demonstrate that our robust portfolios under ambiguity are more stable over time than the non-robust portfolios.

Keywords: Portfolio selection; Multiple-risk measures; Distribution ambiguity; Minimum variance portfolio; Robustness; 91G10; 91B30; 90C29; 62G35 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00186-017-0614-0

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