Multi-period asset allocation with lower partial moments criteria and affine policies

This paper discusses a computational methodology for solving multi-period dynamic asset allocation problems using empirical asymmetric measures of risk. Three features distinguish the proposed approach from the mainstream ones. First, our approach is nonparametric, in the sense that it does not require explicit estimation of a statistical model for the returns distribution. Second, it employs affine decision policies, which make the multi-period formulation of the problem amenable to efficient convex optimization format. Third, it uses asymmetric, unilateral, measures of risk which, unlike standard symmetric measures such as variance, capture the fact that investors are usually not averse to return deviations from the expected target, if these deviations actually exceed the target.