Abstract
Evolutionary computation techniques are closely connected with Monte Carlo simulations via statistical mechanics. Most practical realizations of such a connection are based on Markov chain Monte Carlo procedures and Markov chain approximation methodologies. However, such realizations face challenges when we have to deal with multivariate situations. In this contribution, we consider the development of evolutionary type Monte Carlo based algorithms for dealing with jump-diffusion stochastic processes. In particular, we focus on the first passage time problems for multivariate correlated jump-diffusion processes in the context of credit risk and the analysis of default correlations. The developed technique can be useful in option pricing as well as in other areas of complex systems analysis.
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Tsviliuk, O., Melnik, R., Zhang, D. (2010). Evolutionary Monte Carlo Based Techniques for First Passage Time Problems in Credit Risk and Other Applications in Finance. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2010. Lecture Notes in Computer Science, vol 6025. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12242-2_24
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DOI: https://doi.org/10.1007/978-3-642-12242-2_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12241-5
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