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A Data-Driven Optimization Heuristic for Downside Risk Minimization

Manfred Gilli (), Evis Këllezi and Hilda Hysi
Additional contact information
Evis Këllezi: Mirabaud & cie
Hilda Hysi: University of Geneva - Department of Econometrics

No 06-02, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: In practical portfolio choice models risk is often defined as VaR, expected short-fall, maximum loss, Omega function, etc. and is computed from simulated future scenarios of the portfolio value. It is well known that the minimization of these functions can not, in general, be performed with standard methods. We present a multi-purpose data-driven optimization heuristic capable to deal efficiently with a variety of risk functions and practical constraints on the positions in the portfolio. The efficiency and robustness of the heuristic is illustrated by solving a collection of real world portfolio optimization problems using different risk functions such as VaR, expected shortfall, maximum loss and Omega function with the same algorithm.

Keywords: Portfolio optimization; Heuristic optimization; Threshold accepting; Downside risk (search for similar items in EconPapers)
JEL-codes: C61 C63 G11 G32 (search for similar items in EconPapers)
Date: 2006-06
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Working Paper: A Data-Driven Optimization Heuristic for Downside Risk Minimization (2006) Downloads
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