@inbook{b59400cf02684ca1beb5d59c7868db46,

title = "Evolutionary estimation of a coupled Markov Chain credit risk model",

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 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.",

author = "Ronald Hochreiter and David Wozabal",

year = "2010",

doi = "10.1007/978-3-642-13950-5_3",

language = "English",

isbn = "9783642139499",

series = "Studies in Computational Intelligence",

publisher = "Springer Verlag",

pages = "31--44",

booktitle = "Natural Computing in Computational Finance",

}