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Robust Portfolio Optimization with Derivative Insurance Guarantees

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  • Steve Zymler
  • Berc Rustem
  • Daniel Kuhn
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    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.

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    File URL: http://comisef.eu/files/wps018.pdf
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    Bibliographic Info

    Paper provided by COMISEF in its series Working Papers with number 018.

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    Length: 31 pages
    Date of creation: 14 Aug 2009
    Date of revision:
    Handle: RePEc:com:wpaper:018

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    Web page: http://www.comisef.eu

    Related research

    Keywords: robust optimization; portfolio optimization; portfolio insurance; second order cone programming;

    This paper has been announced in the following NEP Reports:

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