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

Yi-Ping Chang () and Chih-Tun Yu ()

Computational Statistics, 2014, vol. 29, issue 1, 361 pages

Abstract: 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. Copyright Springer-Verlag Berlin Heidelberg 2014

Keywords: Asset correlation; Bayesian confidence intervals; Portfolio credit risk; Probability of default; MCMC; Serial dependence (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:29:y:2014:i:1:p:331-361

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DOI: 10.1007/s00180-013-0453-2

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