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.
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Acknowledgement
The author would like to thank Roberto Baragona, Francesco Battaglia, Manfred Gilli, Thiemo Krink, Dietmar Maringer, Tommaso Minerva, Irene Poli and Peter Winker for many interesting and inspiring discussions on EAs and stochastic search heuristics. Financial support from MIUR PRIN 20077P5AWA005 and from Fondazione Cassa di Risparmio di Modena for ASBE Project is gratefully acknowledged.
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Paterlini, S. (2010). Evolutionary Computation for Modelling and Optimization in Finance. In: Lechevallier, Y., Saporta, G. (eds) Proceedings of COMPSTAT'2010. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2604-3_24
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DOI: https://doi.org/10.1007/978-3-7908-2604-3_24
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