Qingshuo Song : Outperformance Portfolio Optimization: Hypothesis Testing Approach

Posted by Jie Yang , part of the Statistics and Data Science Seminar.

At

June 27, 2012, 3 p.m.

In

SEO 636

Abstract

We study the portfolio problem of maximizing the out-performance probability over a random benchmark through dynamic trading with a fixed initial capital. Under a general incomplete market framework, this stochastic control problem can be formulated as a composite pure hypothesis testing problem. We analyze the connection between this pure testing problem and its randomized counterpart, and from latter we derive a dual representation for the maximal outperformance probability. Moreover, in a complete market setting, we provide a closed-form solution to the problem of beating a leveraged exchange traded fund. For a general benchmark under an incomplete stochastic factor model, we provide the Hamilton-Jacobi-Bellman PDE characterization for the maximal out-performance probability. It's a joint work with Tim Leung and Jie Yang.