Deriving the Dependence Structure of Portfolio Credit Derivatives using Evolutionary Algorithms

Hager, Svenja; Schoebel, Rainer

doi:10.2139/ssrn.893049

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“…Evolutionary techniques have not yet been applied extensively in the area of credit risk management -see e.g. [5] for credit portfolio dependence structure derivations or [15] for optimization of transition probability matrices. In this chapter, we apply the Coupled Markov Chain approach introduced by [9] and provide extensions to the methods presented in [8].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Estimation of a Coupled Markov Chain Credit Risk Model

Hochreiter

Wozabal

2010

Natural Computing in Computational Finance

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Summary. 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.

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