Bayesian confidence intervals for probability of default and asset correlation of portfolio credit risk

被引：0
作者：
Yi-Ping Chang
Chih-Tun Yu
机构：
[1] Soochow University,Department of Financial Engineering and Actuarial Mathematics
[2] National Chengchi University,Department of Statistics
来源：
Computational Statistics | 2014年 / 29卷
关键词：
Asset correlation; Bayesian confidence intervals; Portfolio credit risk; Probability of default; MCMC; Serial dependence;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
We derive Bayesian confidence intervals for the probability of default (PD), asset correlation (Rho), and serial dependence (Theta) for low default portfolios (LDPs). The goal is to reduce the probability of underestimating credit risk in LDPs. We adopt a generalized method of moments with continuous updating to estimate prior distributions for PD and Rho from historical default data. The method is based on a Bayesian approach without expert opinions. A Markov chain Monte Carlo technique, namely, the Gibbs sampler, is also applied. The performance of the estimation results for LDPs validated by Monte Carlo simulations. Empirical studies on Standard & Poor’s historical default data are also conducted.
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页码：331 / 361
页数：30
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