A Computational Study of Modern Approaches to Risk-Averse Stochastic Optimization Using Financial Portfolio Allocation Model

Abstract

We study some novel approaches to risk-averse stochastic optimization. Our goal is to numerically evaluate whether these methods result in an improved decision making under conditions of uncertainty. The methodology used relies on the nancial portfolio optimization model used as a testing framework. We track the behavior of trading strategies made based on Conditional-Value-at-Risk (CVaR), Higher Moment Coherent Risk (HMCR) measure and Log-Exponential Convex Risk (LogExpCR) measures, three of the approaches recently proposed to deal with risk in stochastic operations research problems. We use historical data from S&P 100 assets during the period from 2006 to 2015, which includes the Global Financial Crisis. In our analysis we have observed that more advanced HMCR and LogExpCR measures result in better performance compared to CVaR portfolios, especially in the case of heavytailed distributions of the uncertainties. While this is in accordance with most of the previous ndings presented in the literature this work represents an attempt at a more comprehensive comparative study of risk measures. We have also observed some behaviors that go against general expectations, and hence require additional attention in the future research.