Bayesian Inference for Credit Risk with Serially Dependent Factor Model

10.6186/IJIMS.2011.22.2.3

Yi-Ping Chang；Chih-Tun Yu；Hui-Mei Liu

Default probability ； asset correlation ； serially dependent factor model ； Bayesian inference

International Journal of Information and Management Sciences

22：2（2011 / 06 / 01）

135 - 156

英文

Default probability and asset correlation are key factors in determining credit default risk in loan portfolios. Therefore, many articles have been devoted to the study in quantifying default probability and asset correlation. However, the classical estimation methods (e.g. MLE) usually use only historical data and often underestimate the default probability in special situations, such as the occurrence of a financial crisis. By contrast, the Bayesian method is seen to be a more viable alternative to solving such estimation problems. In this paper, we consider the Bayesian approach by applying Markov chain Monte Carlo (MCMC) techniques in estimating default probability and asset correlation under serially dependent factor model. The empirical results and out-of-sample forecasting for S&P default data provide strong evidence to support that the serially dependent factor model is reliable in determining credit default risk.

1. Chang, Y. P.,Hung, M. C.,Wang, S. F.,Yu, C. T.(2010).An EM algorithm for multivariate NIG distribution and its application to Value-at-Risk.International Journal of Information and Management Sciences,21(3),265-283.
   連結：
2. Wu, P. C.,Kao, L. J.,Lee, C. W.(2011).How issuer default risk affects basket credit linked note coupon rate.International Journal of Information and Management Sciences,22(1),59-71.
   連結：
3. G¨ossl, M., Predictions based on certain uncertainties – a Bayesian credit portfolio approach, Disscuss Paper, HypoVereinsbank, 2005.
4. Gordy, M. and Heitfield, E., Estimating default correlation from short panels of credit rating,Working Paper, Federal Reserve Board, 2002.
5. Gupton, G., Finger, C. and Bhatia, M., CreditMetricsTM, technical document, CreditMetrics, 1997..
6. Amman, H. M.(ed.),Kendrick, D. A.(ed.),Rust, J.(ed.)(1996).Handbook of Computational Economics.Amsterdam:North-Holland.
7. Basel Committee on Banking Supervision(2006).International convergence of capital measurement and capital standards: A revised framework-comprehensive version.Bank for International Settlements.
8. Booth, J. G.,Hobert, J. P.(1999).Maximizing generalized linear mixed models likelihoods with an automated Monte Carlo EM algorithm.Journal of the Royal Statistical Society Series B,61(1),265-285.
9. Crouhy, M. G.,Jarrow, R. A.,Turnbull, S. M.(2008).The subprime credit crisis of 2007.The Journal of Derivatives,16(1),81-110.
10. Czado, C.,Pflüger, C.(2008).Modeling dependencies between rating categories and their effects on prediction in a credit risk portfolio.Applied Stochastic Models in Business and Industry,24(3),237-259.
11. Dagpunar, J. S.(2007).Simulation and Monte Carlo-with Applications in Finance and MCMC.New York:Wiley.
12. Diamond, D. W.,Rajan, R. G.(2009).The credit crisis: Conjectures about causes and remedies.American Economic Review,99(2),606-610.
13. Dwyer, D. W.(2007).The distribution of defaults and Bayesian model validation.Journal of Risk Model Validation,1(1),23-53.
14. Ebnother, S.,Vanini, P.(2007).Credit portfolios: What defines risk horizons and risk measurement?.Journal of Banking & Finance,31(12),3663-3679.
15. Gilks, W.(ed.),Richardson, S.(ed.),Spiegelhalter, D.(ed.)(1996).Markov Chain Monte Carlo in Practice.London:Chapman & Hall.
16. Gordy, M. B.(2003).A risk-factor model foundation for ratings-based capital rules.Journal of Finanial Intermediation,12(3),199-232.
17. Greenberg, E.(2007).Introduction to Bayesian Econometrics.New York:Cambridge University Press.
18. Hanson, S.,Schuermann, T.(2006).Confidence intervals for probabilities of default.Journal of Banking & Finance,30(8),2281-2301.
19. Kiefer, N. M.(2009).Default estimation for low-default portfolios.Journal of Empirical Finance,16(1),164-173.
20. Kiefer, N. M.(2007).The probability approach to default probabilities.Risk,20(7),146-150.
21. Kiefer, N. M.(2011).Default estimation, correlated defaults, and expert information.Journal of Applied Econometrics,26(2),173-192.
22. Kiefer, N. M.(2010).Default estimation and expert information.Journal of Business and Economic Statistics,28(2),320-328.
23. Liao, S. L.,Chen M. S.,Li, F. C.(2009).A factor-copula based valuation of synthetic CDO-squared under a stochastic intensity.International Journal of Information and Management Sciences,20(1),103-120.
24. McNeil, A. J.,Wendin, J. P.(2007).Bayesian inferences for generalized linear mixed models of portfolio credit risk.Journal of Empirical Finance,14(2),131-149.
25. Rachev, S. T.,Hsu, J. S. J.,Bagasheva, B. S.,Fabozzi, F. J.(2008).Bayesian Methods in Finance.New York:Wiley.
26. Robert, C.,Casella, G.(2004).Monte Carlo Statistical Models.New York:Springer.
27. Schönbucher, P.(2001).Factor models: Portfolio credit risks when defaults are correlated.Journal of Risk Finance,3(1),45-56.
28. Soros, G.(2008).The New Paradigm for Financial Markets: The Credit Crisis of 2008 and What it Means.New York:PublicAffairs.
29. Standard & Poor''s.(2009).,Global Fixed Income Research.
30. Vasicek, O.(2002).Loan portfolio value.Risk,15(12),160-162.
31. Wu, L.(2004).Non-linear mixed-effect models with non-ignorably missing covariates.The Canadian Journal of Statistics,32(1),27-37.

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