IDEAS home Printed from https://ideas.repec.org/p/qut/auncer/2013_91.html
   My bibliography  Save this paper

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

Author

Listed:
  • Di Bu
  • Yin Liao

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.

Suggested Citation

  • 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.
    • Handle: RePEc:qut:auncer:2013_91
    as

    Download full text from publisher

    File URL: http://www.ncer.edu.au/papers/documents/WP98.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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 611-625, July.
    2. Bakshi, Gurdip & Cao, Charles & Chen, Zhiwu, 2000. "Do Call Prices and the Underlying Stock Always Move in the Same Direction?," The Review of Financial Studies, Society for Financial Studies, vol. 13(3), pages 549-584.
    3. Joseph P. Ogden, 1987. "Determinants Of The Ratings And Yields On Corporate Bonds: Tests Of The Contingent Claims Model," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 10(4), pages 329-340, December.
    4. 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, October.
    5. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    6. Young Ho Eom, 2004. "Structural Models of Corporate Bond Pricing: An Empirical Analysis," The Review of Financial Studies, Society for Financial Studies, vol. 17(2), pages 499-544.
    7. 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.
    8. repec:bla:jfinan:v:44:y:1989:i:4:p:1011-23 is not listed on IDEAS
    9. 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, June.
    10. 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.
    11. Jin‐Chuan Duan, 1994. "Maximum Likelihood Estimation Using Price Data Of The Derivative Contract," Mathematical Finance, Wiley Blackwell, vol. 4(2), pages 155-167, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jessen, Cathrine & Lando, David, 2015. "Robustness of distance-to-default," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 493-505.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Forte, Santiago & Lovreta, Lidija, 2012. "Endogenizing exogenous default barrier models: The MM algorithm," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1639-1652.
    2. 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.
    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. Li, Ka Leung & Wong, Hoi Ying, 2008. "Structural models of corporate bond pricing with maximum likelihood estimation," Journal of Empirical Finance, Elsevier, vol. 15(4), pages 751-777, September.
    5. Zhou, Xinghua & Reesor, R. Mark, 2015. "Misrepresentation and capital structure: Quantifying the impact on corporate debt value," Journal of Corporate Finance, Elsevier, vol. 34(C), pages 293-310.
    6. Bougias, Alexandros & Episcopos, Athanasios & Leledakis, George N., 2022. "The role of asset payouts in the estimation of default barriers," International Review of Financial Analysis, Elsevier, vol. 81(C).
    7. Han-Hsing Lee & Kuanyu Shih & Kehluh Wang, 2016. "Measuring sovereign credit risk using a structural model approach," Review of Quantitative Finance and Accounting, Springer, vol. 47(4), pages 1097-1128, November.
    8. Bartram, Sohnke M. & Brown, Gregory W. & Hund, John E., 2007. "Estimating systemic risk in the international financial system," Journal of Financial Economics, Elsevier, vol. 86(3), pages 835-869, December.
    9. Jan Ericsson & Joel Reneby & Hao Wang, 2015. "Can Structural Models Price Default Risk? Evidence from Bond and Credit Derivative Markets," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 5(03), pages 1-32, September.
    10. Correia, Ricardo & Población, Javier, 2015. "A structural model with Explicit Distress," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 112-130.
    11. Eberhart, Allan C., 2005. "A comparison of Merton's option pricing model of corporate debt valuation to the use of book values," Journal of Corporate Finance, Elsevier, vol. 11(1-2), pages 401-426, March.
    12. Paul Kupiec, 2007. "Financial stability and Basel II," Annals of Finance, Springer, vol. 3(1), pages 107-130, January.
    13. Lim, Terence & Lo, Andrew W. & Merton, Robert C. & Scholes, Myron S., 2006. "The Derivatives Sourcebook," Foundations and Trends(R) in Finance, now publishers, vol. 1(5–6), pages 365-572, April.
    14. Reisz, Alexander S. & Perlich, Claudia, 2007. "A market-based framework for bankruptcy prediction," Journal of Financial Stability, Elsevier, vol. 3(2), pages 85-131, July.
    15. Leroy, Aurélien & Lucotte, Yannick, 2017. "Is there a competition-stability trade-off in European banking?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 46(C), pages 199-215.
    16. Qi, Howard & Liu, Sheen & Wu, Chunchi, 2010. "Structural models of corporate bond pricing with personal taxes," Journal of Banking & Finance, Elsevier, vol. 34(7), pages 1700-1718, July.
    17. Flavia Barsotti & Simona Sanfelici, 2016. "Market Microstructure Effects on Firm Default Risk Evaluation," Econometrics, MDPI, vol. 4(3), pages 1-31, July.
    18. Jing-Zhi Huang & Zhan Shi & Hao Zhou, 2020. "Specification Analysis of Structural Credit Risk Models [Corporate bond valuation and hedging with stochastic interest rates and endogenous bankruptcy]," Review of Finance, European Finance Association, vol. 24(1), pages 45-98.
    19. 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.
    20. Ericsson, Jan & Reneby, Joel, 2003. "Valuing Corporate Liabilities," SIFR Research Report Series 15, Institute for Financial Research.

    More about this item

    Keywords

    Credit risk; Merton model; Stochastic volatility; Particle Filtter; Default probability; CDS;
    All these keywords.

    JEL classification:

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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:qut:auncer:2013_91. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: School of Economics and Finance (email available below). General contact details of provider: https://edirc.repec.org/data/ncerrau.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.