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Structural Credit Risk Model with Stochastic Volatility: A Particle-filter Approach

Author Info

  • Di Bu
  • Yin Liao

    ()

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Abstract

This paper extends Merton's structural credit risk model to account for the fact that the firm's asset volatility follows a stochastic process. With the presence of stochastic volatility, the transformed-data maximum likelihood estimation (MLE) method of Duan (1994, 2000) can no longer be applied to estimate the model. We devise a particle filtering algorithm to solve this problem. This algorithm is based on the general non-linear and non-Gaussian filtering with sequential parameter learning, and a simulation study is conducted to ascertain its finite sample performance. Meanwhile, we implement this model on the real data of companies in Dow Jones industrial average and find that incorporating stochastic volatility into the structural model can largely improve the model performance.

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File URL: http://www.ncer.edu.au/papers/documents/WP98.pdf
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Paper provided by National Centre for Econometric Research in its series NCER Working Paper Series with number 98.

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Length: 33 pages
Date of creation: 28 Oct 2013
Date of revision:
Handle: RePEc:qut:auncer:2013_91
Contact details of provider: Phone: 07 3138 5066
Fax: 07 3138 1500
Web page: http://www.ncer.edu.au

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Keywords: Credit risk; Merton model; Stochastic volatility; Particle Filtter; Default probability; CDS;

Find related papers by JEL classification:

  • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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  1. Charles Quanwei Cao & Gurdip S. Bakshi & Zhiwu Chen, 1999. "Do Call Prices and the Underlying Stock Always Move in the Same Direction?," Yale School of Management Working Papers ysm125, Yale School of Management.
  2. Huang, Shirley J. & Yu, Jun, 2010. "Bayesian analysis of structural credit risk models with microstructure noises," Journal of Economic Dynamics and Control, Elsevier, vol. 34(11), pages 2259-2272, November.
  3. Jin-Chuan Duan, 1994. "Maximum Likelihood Estimation Using Price Data Of The Derivative Contract," Mathematical Finance, Wiley Blackwell, vol. 4(2), pages 155-167.
  4. Merton, Robert C., 1973. "On the pricing of corporate debt: the risk structure of interest rates," Working papers 684-73., Massachusetts Institute of Technology (MIT), Sloan School of Management.
  5. Stein, Jeremy, 1989. " Overreactions in the Options Market," Journal of Finance, American Finance Association, vol. 44(4), pages 1011-23, September.
  6. Bjørn Eraker & Michael Johannes & Nicholas Polson, 2003. "The Impact of Jumps in Volatility and Returns," Journal of Finance, American Finance Association, vol. 58(3), pages 1269-1300, 06.
  7. Duan, Jin-Chuan & Fulop, Andras, 2009. "Estimating the structural credit risk model when equity prices are contaminated by trading noises," Journal of Econometrics, Elsevier, vol. 150(2), pages 288-296, June.
  8. Jin-Chuan Duan, 2000. "Correction: Maximum Likelihood Estimation Using Price Data of the Derivative Contract (Mathematical Finance 1994, 4/2, 155-167)," Mathematical Finance, Wiley Blackwell, vol. 10(4), pages 461-462.
  9. Jones, E Philip & Mason, Scott P & Rosenfeld, Eric, 1984. " Contingent Claims Analysis of Corporate Capital Structures: An Empirical Investigation," Journal of Finance, American Finance Association, vol. 39(3), pages 611-25, July.
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