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Active extension portfolio optimization with non-convex risk measures using metaheuristics

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  • Ronald Hochreiter
  • Christoph Waldhauser

Abstract

We consider the optimization of active extension portfolios. For this purpose, the optimization problem is rewritten as a stochastic programming model and solved using a clever multi-start local search heuristic, which turns out to provide stable solutions. The heuristic solutions are compared to optimization results of convex optimization solvers where applicable. Furthermore, the approach is applied to solve problems with non-convex risk measures, most notably to minimize Value-at-Risk. Numerical results using data from both the Dow Jones Industrial Average as well as the DAX 30 are shown.

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  • Ronald Hochreiter & Christoph Waldhauser, 2014. "Active extension portfolio optimization with non-convex risk measures using metaheuristics," Papers 1406.7723, arXiv.org.
    • Handle: RePEc:arx:papers:1406.7723
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    References listed on IDEAS

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    3. Stein W. Wallace & Stein-Erik Fleten, 2002. "Stochastic programming in energy," GE, Growth, Math methods 0201001, University Library of Munich, Germany, revised 13 Nov 2003.
    4. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
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