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Bayesian Analysis of Structural Credit Risk Models with Microstructure Noises

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  • Shirley J. Huang

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

  • Jun Yu

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

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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.

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Bibliographic Info

Paper provided by Sim Kee Boon Institute for Financial Economics in its series Working Papers with number CoFie-07-2008.

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Length: 31 Pages
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Publication status: Published in SMU-SKBI CoFie Working Paper
Handle: RePEc:skb:wpaper:cofie-07-2008

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Keywords: MCMC; Credit risk; Microstructure noise; Structural models; Deviance information criterion;

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References

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Citations

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Cited by:
  1. 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.
  2. 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.
  3. 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.
  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. 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.
  6. Yong Li & Tao Zeng & Jun Yu, 2012. "Robust Deviance Information Criterion for Latent Variable Models," Working Papers 30-2012, Singapore Management University, School of Economics.
  7. David Wozabal & Ronald Hochreiter, 2009. "A Coupled Markov Chain Approach to Credit Risk Modeling," Papers 0911.3802, arXiv.org, revised Jan 2014.
  8. Fulop, Andras & Li, Junye, 2013. "Efficient learning via simulation: A marginalized resample-move approach," Journal of Econometrics, Elsevier, vol. 176(2), pages 146-161.

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