Algorithm Engineering in Robust Optimization

Robust optimization is a young and emerging field of research having received a considerable increase of interest over the last decade. In this paper, we argue that the the algorithm engineering methodology fits very well to the field of robust optimization and yields a rewarding new perspective on both the current state of research and open research directions. To this end we go through the algorithm engineering cycle of design and analysis of concepts, development and implementation of algorithms, and theoretical and experimental evaluation. We show that many ideas of algorithm engineering have already been applied in publications on robust optimization. Most work on robust optimization is devoted to analysis of the concepts and the development of algorithms, some papers deal with the evaluation of a particular concept in case studies, and work on comparison of concepts just starts. What is still a drawback in many papers on robustness is the missing link to include the results of the experiments again in the design

Can switching between risk measures lead to better portfolio optimization?

This article proposes a technique that involves switching between risk measures in different market environments, to capture the well-documented dynamic nature of risk within a portfolio optimization setting. In-sample results show categorically that switching between various measures, such as CVaR, time-varying (GARCH) variances and simple standard deviations, can lead to a better performance than using any single measure. Using a logistic probability model to determine when to switch between alternatives, out-of -sample results also show positive results. Given that this study only applies a basic switching system, it lends itself to easy application by practitioners through its simplicity, intuitive appeal and computational feasibility.Brianna Cain and Ralf Zurbrueg