Evolutionary estimation of a Coupled Markov Chain credit risk model

TL;DR

This paper introduces a Coupled Markov Chain model for credit risk transition estimation, employing evolutionary algorithms like Particle Swarm Optimization to effectively find maximum likelihood estimators.

Contribution

It presents a novel application of evolutionary optimization techniques to estimate parameters in a non-convex credit risk model.

Findings

Evolutionary algorithms successfully optimize the model's likelihood function.

Numerical results demonstrate the effectiveness of the proposed methods.

The approach enhances credit risk management strategies.

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.