An estimation-free, robust conditional value-at-risk portfolio allocation model

We propose a novel optimization model for risk-averse investors to obtain robust solutions for portfolio allocation problems. Unlike related models in the literature, no historical data or statistical estimation techniques are used to compute the parameters of tire model. Instead, the parameters are directly obtained from current prices of options on the assets being considered. Furthermore, the model only requires the,solution of a linear program. To find a robust portfolio, the minimize the portfolio's worst case conditional value-at-risk over all asset return distributions that replicate the current option prices. The model addresses the main practical imitations associated with classical portfolio allocation techniques, namely, the high sensitivity to model parameters and the difficult, to obtain accurate parameters' estimates. These characteristics, together with their linear programming formulation and the use of a coherent downside measure of risk, should he appealing to practitioners. We provide numerical experiments to illustrate tire characteristics of the model.