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Bayesian Analysis of Structural Credit Risk Models with Microstructure Noises

Author

Listed:
  • Shirley J. Huang

    (Lee Kong Chian School of Business, Singapore Management University)

  • Jun Yu

    (Sim Kee Boon Institute for Financial Economics, Singapore Management University)

Abstract

In this paper a Markov chain Monte Carlo (MCMC) technique is developed for the Bayesian analysis of structural credit risk models with microstruc- ture noises. The technique is based on the general Bayesian approach with posterior computations performed by Gibbs sampling. Simulations from the Markov chain, whose stationary distribution converges to the posterior distri- bution, enable exact finite sample inferences of model parameters. The exact inferences can easily be extended to latent state variables and any nonlinear transformation of state variables and parameters, facilitating practical credit risk applications. In addition, the comparison of alternative models can be based on deviance information criterion (DIC) which is straightforwardly ob- tained from the MCMC output. The method is implemented on the basic structural credit risk model with pure microstructure noises and some more general specifications using daily equity data from US and emerging mar- kets. We find empirical evidence that microstructure noises are positively correlated with the firm values in emerging markets.

Suggested Citation

  • Shirley J. Huang & Jun Yu, "undated". "Bayesian Analysis of Structural Credit Risk Models with Microstructure Noises," Working Papers CoFie-07-2008, Singapore Management University, Sim Kee Boon Institute for Financial Economics.
    • Handle: RePEc:skb:wpaper:cofie-07-2008
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    Cited by:

    1. Bégin, Jean-François & Boudreault, Mathieu & Gauthier, Geneviève, 2017. "Firm-specific credit risk estimation in the presence of regimes and noisy prices," Finance Research Letters, Elsevier, vol. 23(C), pages 306-313.
    2. Wozabal, David & Hochreiter, Ronald, 2012. "A coupled Markov chain approach to credit risk modeling," Journal of Economic Dynamics and Control, Elsevier, vol. 36(3), pages 403-415.
    3. Xiao, Weilin & Zhang, Xili, 2016. "Pricing equity warrants with a promised lowest price in Merton’s jump–diffusion model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 219-238.
    4. Chung, Tsz-Kin & Hui, Cho-Hoi & Li, Ka-Fai, 2013. "Explaining share price disparity with parameter uncertainty: Evidence from Chinese A- and H-shares," Journal of Banking & Finance, Elsevier, vol. 37(3), pages 1073-1083.
    5. Kleppe, Tore Selland & Yu, Jun & Skaug, Hans J., 2014. "Maximum likelihood estimation of partially observed diffusion models," Journal of Econometrics, Elsevier, vol. 180(1), pages 73-80.
    6. Lindset, Snorre & Lund, Arne-Christian & Persson, Svein-Arne, 2014. "Credit risk and asymmetric information: A simplified approach," Journal of Economic Dynamics and Control, Elsevier, vol. 39(C), pages 98-112.
    7. Fulop, Andras & Li, Junye, 2013. "Efficient learning via simulation: A marginalized resample-move approach," Journal of Econometrics, Elsevier, vol. 176(2), pages 146-161.
    8. Guarin, Alexander & Liu, Xiaoquan & Ng, Wing Lon, 2014. "Recovering default risk from CDS spreads with a nonlinear filter," Journal of Economic Dynamics and Control, Elsevier, vol. 38(C), pages 87-104.
    9. Boudreault, Mathieu & Gauthier, Geneviève & Thomassin, Tommy, 2015. "Estimation of correlations in portfolio credit risk models based on noisy security prices," Journal of Economic Dynamics and Control, Elsevier, vol. 61(C), pages 334-349.
    10. Di Bu & Yin Liao, 2013. "Structural Credit Risk Model with Stochastic Volatility: A Particle-filter Approach," NCER Working Paper Series 98, National Centre for Econometric Research.
    11. Xiaoyi Han & Lung-Fei Lee, 2016. "Bayesian Analysis of Spatial Panel Autoregressive Models With Time-Varying Endogenous Spatial Weight Matrices, Common Factors, and Random Coefficients," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 642-660, October.
    12. Yong Li & Zeng Tao & Jun Yu, "undated". "Robust Deviance Information Criterion for Latent Variable Models," Working Papers CoFie-04-2012, Singapore Management University, Sim Kee Boon Institute for Financial Economics.
    13. Alina Sima (Grigore) & Alin Sima, 2011. "Distance to Default Estimates for Romanian Listed Companies," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 3(2), pages 091-106, December.
    14. Jieyan Fang-Klingler, 2019. "Impact of Readability on Corporate Bond Market," JRFM, MDPI, vol. 12(4), pages 1-18, December.
    15. Bu, Di & Liao, Yin, 2014. "Corporate credit risk prediction under stochastic volatility and jumps," Journal of Economic Dynamics and Control, Elsevier, vol. 47(C), pages 263-281.

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    Keywords

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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