Robust Optimization with Applications to Conditional Value-at-Risk-Based Portfolio Selection Problem

摘要

Based on the robust optimization techniques (D. Bertsimas and M. Sim, Tractable approximations to robust conic optimization problems, Mathematical Programming, 107, 5 (2006)), we propose a computationally tractable robust optimization method for minimizing the CVaR of a portfolio. A good characteristic of the new method is that the robust optimization model retains the complexity of original portfolio optimization problem, i.e., the robust counterpart problem is still a linear programming problem. Moreover, it is less conservative than the Quaranta and Zaffaroni's method which is under box uncertainty set. We present some numerical experiments with real market data to illustrate the behavior of robust optimization model.