Evolutionary estimation of a coupled Markov Chain credit risk model

Ronald Hochreiter, David Wozabal

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Scopus citations


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.

Original languageEnglish
Title of host publicationNatural Computing in Computational Finance
PublisherSpringer Verlag
Number of pages14
ISBN (Print)9783642139499
StatePublished - 2010
Externally publishedYes

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X


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