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arXiv · 0911.3753

Evolutionary estimation of a Coupled Markov Chain credit risk model

Ronald Hochreiter·David Wozabal
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Abstract

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 extent, 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.

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Ronald Hochreiter, David Wozabal. 2009-11-19. Evolutionary estimation of a Coupled Markov Chain credit risk model. https://doi.org/10.1007/978-3-642-13950-5_3

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