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Evolutionary Computation for Modelling and Optimization in Finance

In: Proceedings of COMPSTAT'2010

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  • Sandra Paterlini

    (University of Modena and Reggio E., Department of Economics, CEFIN and RECent
    CEQURA, Center for Quantitative Risk Analysis)

Abstract

In the last decades, there has been a tendency to move away from mathematically tractable, but simplistic models towards more sophisticated and real-world models in finance. However, the consequence of the improved sophistication is that the model specification and analysis is no longer mathematically tractable. Instead solutions need to be numerically approximated. For this task, evolutionary computation heuristics are the appropriate means, because they do not require any rigid mathematical properties of the model. Evolutionary algorithms are search heuristics, usually inspired by Darwinian evolution and Mendelian inheritance, which aim to determine the optimal solution to a given problem by competition and alteration of candidate solutions of a population. In this work, we focus on credit risk modelling and financial portfolio optimization to point out how evolutionary algorithms can easily provide reliable and accurate solutions to challenging financial problems.

Suggested Citation

  • Sandra Paterlini, 2010. "Evolutionary Computation for Modelling and Optimization in Finance," Springer Books, in: Yves Lechevallier & Gilbert Saporta (ed.), Proceedings of COMPSTAT'2010, pages 265-274, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2604-3_24
    DOI: 10.1007/978-3-7908-2604-3_24
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