A Broader View of the Mean-Variance Optimization Framework

Abstract

In theory, mean-variance optimization provides a rich and elegant framework for asset allocation. Given a practical representation of the possible distribution of returns for a set of assets, mean-variance optimization defines the optimal asset weights to maximize a reasonable objective of balancing an investor’s desire for high returns with their aversion to risk. In practice, the assumptions that underlie the mean-variance framework are overly restrictive and almost certainly violated. Further, mean-variance optimization commonly produces asset allocations that are extreme, counter-intuitive and highly sensitive to small changes to input parameters. In this paper, we explore the relative mismatch between the coarseness of the inputs to mean-variance optimization and the precision of the output, and demonstrate that greater insight may be gained from mean-variance optimization by decreasing the precision of the optimization.