Abstract:  In this talk we survey some of our recent work on using robust optimization techniques in the context of portfolio selection. In particular, we present robust models for active portfolio management. The intent of the robust optimization framework is to systematically correct for parameter uncertainties in optimization problems. The proposed models allow one to impose additional side constraints such as transaction costs, bounds on the portfolio holdings, constraints on the portfolio beta, limits on cash exposure, etc.

We report on the performance of our robust strategies in outperforming the S&P 500 index over 2000-2003. Our computational results suggest that the robust active portfolio management strategy significantly outperforms the S&P 500 index without a significant increase in volatility.

This is a joint work with Donald Goldfarb and Garud Iyengar from Columbia University IEOR Department.