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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Evolutionary Estimation of a Coupled Markov Chain Credit Risk Model
verfasst von : Ronald Hochreiter, David Wozabal
Erschienen in: Natural Computing in Computational Finance
Verlag: Springer Berlin Heidelberg
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There exists a range of different models for estimating and simulating credit risk transitions to optimally manage credit risk portfolios and products. In this chapter we present a Coupled Markov Chain approach to model rating transitions and thereby default probabilities of companies. As the likelihood of the model turns out to be a non-convex function of the parameters to be estimated, we apply heuristics to find the ML estimators. To this end, we outline the model and its likelihood function, and present both a Particle Swarm Optimization algorithm, as well as an Evolutionary Optimization algorithm to maximize the likelihood function. Numerical results are shown which suggest a further application of evolutionary optimization techniques for credit risk management.