一类耦合Markov链信用风险模型的演化估计
Evolutionary estimation of a Coupled Markov Chain credit risk model
摘要：
存在一系列不同的模型，用于估计和模拟信用风险转移，以优化管理信用风险组合和产品。在本章中，我们提出了一个耦合马尔可夫链的方法来模拟评级转换，从而违约概率的公司。由于模型的似然性是待估计参数的非凸函数，因此我们应用概率论来寻找ML估计量。为此，我们概述了模型及其似然函数，并提出了粒子群优化算法，以及进化优化算法，以最大限度地提高似然函数。数值结果表明，进一步应用进化优化技术的信用风险管理。
英文摘要：
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.
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