IDEAS home Printed from
   My bibliography  Save this article

Firm-specific credit risk estimation in the presence of regimes and noisy prices


  • Bégin, Jean-François
  • Boudreault, Mathieu
  • Gauthier, Geneviève


Security prices are important inputs for estimating credit risk. Yet, to obtain an accurate firm-specific credit risk assessment, one needs a reliable model and a methodology that filters the elements unrelated to the firm’s fundamentals from market prices.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:finlet:v:23:y:2017:i:c:p:306-313
    DOI: 10.1016/

    Download full text from publisher

    File URL:
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL:
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    1. Peter Carr & Liuren Wu, 2010. "Stock Options and Credit Default Swaps: A Joint Framework for Valuation and Estimation," Journal of Financial Econometrics, Oxford University Press, vol. 8(4), pages 409-449, Fall.
    2. Black, Fischer & Cox, John C, 1976. "Valuing Corporate Securities: Some Effects of Bond Indenture Provisions," Journal of Finance, American Finance Association, vol. 31(2), pages 351-367, May.
    3. Gurdip Bakshi & Dilip Madan & Frank Xiaoling Zhang, 2006. "Investigating the Role of Systematic and Firm-Specific Factors in Default Risk: Lessons from Empirically Evaluating Credit Risk Models," The Journal of Business, University of Chicago Press, vol. 79(4), pages 1955-1988, July.
    4. 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.
    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. Chun, Olfa Maalaoui & Dionne, Georges & François, Pascal, 2014. "Detecting Regime Shifts in Credit Spreads," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 49(5-6), pages 1339-1364, December.
    7. Hitesh Doshi & Jan Ericsson & Kris Jacobs & Stuart M. Turnbull, 2013. "Pricing Credit Default Swaps with Observable Covariates," The Review of Financial Studies, Society for Financial Studies, vol. 26(8), pages 2049-2094.
    8. 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.
    9. Benjamin Yibin Zhang & Hao Zhou & Haibin Zhu, 2009. "Explaining Credit Default Swap Spreads with the Equity Volatility and Jump Risks of Individual Firms," The Review of Financial Studies, Society for Financial Studies, vol. 22(12), pages 5099-5131, December.
    10. Huang, Alex YiHou & Hu, Wen-Cheng, 2012. "Regime switching dynamics in credit default swaps: Evidence from smooth transition autoregressive model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1497-1508.
    11. 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.
    Full references (including those not matched with items on IDEAS)


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

    Cited by:

    1. Kim, Sung Ik, 2023. "A comparative study of firm value models: Default risk of corporate bonds," Finance Research Letters, Elsevier, vol. 56(C).

    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. Augustin, Patrick & Subrahmanyam, Marti G. & Tang, Dragon Yongjun & Wang, Sarah Qian, 2014. "Credit Default Swaps: A Survey," Foundations and Trends(R) in Finance, now publishers, vol. 9(1-2), pages 1-196, December.
    2. Da Fonseca, José & Gottschalk, Katrin, 2014. "Cross-hedging strategies between CDS spreads and option volatility during crises," Journal of International Money and Finance, Elsevier, vol. 49(PB), pages 386-400.
    3. repec:wyi:journl:002109 is not listed on IDEAS
    4. Masaaki Kijima & Chi Chung Siu, 2014. "Credit-Equity Modeling Under A Latent Lévy Firm Process," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 17(03), pages 1-41.
    5. Koutmos, Dimitrios, 2019. "Asset pricing factors and bank CDS spreads," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 58(C), pages 19-41.
    6. Murphy, Austin & Headley, Adrian, 2022. "An empirical evaluation of alternative fundamental models of credit spreads," International Review of Financial Analysis, Elsevier, vol. 81(C).
    7. Wang, Hao & Zhou, Hao & Zhou, Yi, 2013. "Credit default swap spreads and variance risk premia," Journal of Banking & Finance, Elsevier, vol. 37(10), pages 3733-3746.
    8. Shi, Yukun & Stasinakis, Charalampos & Xu, Yaofei & Yan, Cheng, 2022. "Market co-movement between credit default swap curves and option volatility surfaces," International Review of Financial Analysis, Elsevier, vol. 82(C).
    9. Jumbe, George, 2023. "Credit Risk Assessment Using Default Models: A Review," OSF Preprints ksb8n, Center for Open Science.
    10. Avino, Davide & Nneji, Ogonna, 2014. "Are CDS spreads predictable? An analysis of linear and non-linear forecasting models," International Review of Financial Analysis, Elsevier, vol. 34(C), pages 262-274.
    11. Landschoot, Astrid Van, 2008. "Determinants of yield spread dynamics: Euro versus US dollar corporate bonds," Journal of Banking & Finance, Elsevier, vol. 32(12), pages 2597-2605, December.
    12. Gatfaoui, Hayette, 2017. "Equity market information and credit risk signaling: A quantile cointegrating regression approach," Economic Modelling, Elsevier, vol. 64(C), pages 48-59.
    13. Eliana Angelini & Elisa Di Febo, 2014. "CDS Spreads: an Empirical Analysis on the Determinants," Journal of Empirical Economics, Research Academy of Social Sciences, vol. 2(2), pages 70-87.
    14. Da Fonseca, José & Ignatieva, Katja & Ziveyi, Jonathan, 2016. "Explaining credit default swap spreads by means of realized jumps and volatilities in the energy market," Energy Economics, Elsevier, vol. 56(C), pages 215-228.
    15. Elisa Di Febo & Eliana Angelini, 2018. "The Relevance of Market Variables in the CDS Spread Volatility: An Empirical Post-crisis Analysis," Global Business Review, International Management Institute, vol. 19(6), pages 1462-1477, December.
    16. Flavia Barsotti & Simona Sanfelici, 2016. "Market Microstructure Effects on Firm Default Risk Evaluation," Econometrics, MDPI, vol. 4(3), pages 1-31, July.
    17. Xiaoqing Fu & Matthew C. Li & Philip Molyneux, 2021. "Credit default swap spreads: market conditions, firm performance, and the impact of the 2007–2009 financial crisis," Empirical Economics, Springer, vol. 60(5), pages 2203-2225, May.
    18. Jang, Woon Wook & Eom, Young Ho & Kang, Yong Joo, 2016. "Corporate bond pricing model with stochastically volatile firm value process," Economics Letters, Elsevier, vol. 148(C), pages 41-44.
    19. 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.
    20. 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.
    21. Gemmill, Gordon & Marra, Miriam, 2019. "Explaining CDS prices with Merton’s model before and after the Lehman default," Journal of Banking & Finance, Elsevier, vol. 106(C), pages 93-109.

    More about this item


    Credit risk; Maximum likelihood estimation; Regime-switching; Filtering; Noisy prices;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G01 - Financial Economics - - General - - - Financial Crises


    Access and download statistics


    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:eee:finlet:v:23:y:2017:i:c:p:306-313. 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: Catherine Liu (email available below). General contact details of provider: .

    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.