Robust Portfolio Optimization with Derivative Insurance Guarantees

Steve Zymler, Berc Rustem and Daniel Kuhn

No 18, Working Papers from COMISEF

Abstract: Robust portfolio optimization aims to maximize the worst-case portfolio return given that the asset returns are allowed to vary within a prescribed uncertainty set. If the uncertainty set is not too large, the resulting portfolio performs well under normal market conditions. However, its performance may substantially degrade in the presence of market crashes, that is, if the asset returns materialize far outside of the uncertainty set. We propose a novel robust portfolio optimization model that provides additional strong performance guarantees for all possible realizations of the asset returns. This insurance is provided via optimally chosen derivatives on the assets in the portfolio. The resulting model constitutes a convex second- order cone program, which is amenable to efficient numerical solution. We evaluate the model using simulated and empirical backtests and conclude that it can out- perform standard robust portfolio optimization as well as classical mean-variance optimization.

Keywords: robust optimization; portfolio optimization; portfolio insurance; second order cone programming (search for similar items in EconPapers)
Pages: 31 pages
Date: 2009-08-14
New Economics Papers: this item is included in nep-ias
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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