Evolutionary Estimation of a Coupled Markov Chian Credit Risk Model

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

Source
University of Vienna
Length of Resource
16
Resource File
Author
Ronald Hochreiter, David Wozabal
Date Published
Publication Type
paper
Resource Type
academic

ResourceID: 69800

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