Heuristic Optimisation in Financial Modelling

Manfred Gilli (manfred.gilli@unige.ch) and Enrico Schumann

No 7, Working Papers from COMISEF

Abstract: There is a large number of optimisation problems in theoretical and applied finance that are difficult to solve as they exhibit multiple local optima or are not ‘well- behaved’ in other ways (eg, discontinuities in the objective function). One way to deal with such problems is to adjust and to simplify them, for instance by dropping constraints, until they can be solved with standard numerical methods. This paper argues that an alternative approach is the application of optimisation heuristics like Simulated Annealing or Genetic Algorithms. These methods have been shown to be capable to handle non-convex optimisation problems with all kinds of constraints. To motivate the use of such techniques in finance, the paper presents several actual problems where classical methods fail. Next, several well-known heuristic techniques that may be deployed in such cases are described. Since such presentations are quite general, the paper describes in some detail how a particular problem, portfolio selection, can be tackled by a particular heuristic method, Threshold Accepting. Finally, the stochastics of the solutions obtained from heuristics are discussed. It is shown, again for the example from portfolio selection, how this random character of the solutions can be exploited to inform the distribution of computations.

Keywords: Optimisation heuristics; Financial Optimisation; Portfolio Optimisation (search for similar items in EconPapers)
JEL-codes: C61 C63 G11 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2009-02-09
New Economics Papers: this item is included in nep-cmp and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Related works:
Journal Article: Heuristic optimisation in financial modelling (2012)
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