Drawdown-at-Risk Monte Carlo Optimization

Abstract

We present a portfolio construction approach with two interesting non-standard features: First, the risk measure used is “drawdown-at-risk”, an interesting concept combining attractive features of drawdown and value-at-risk measures. Second, the efficient frontier is calculated from “random portfolios”, i.e. portfolios containing random constituent weights. We call this “Monte Carlo optimization”. Both features would deserve a detailed analysis. The goal of this note is to provide an overview and illustrate the potential of the approach with two examples: Asset allocation in a small universe consisting of three assets (Indian stocks and bonds as well as gold in INR) and drawdown optimization on single-stock level across the full S&P 500 universe.