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Specification analysis of structural credit risk models

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  • Jing-zhi Huang
  • Hao Zhou

Abstract

In this paper we conduct a specification analysis of structural credit risk models, using term structure of credit default swap (CDS) spreads and equity volatility from high-frequency return data. Our study provides consistent econometric estimation of the pricing model parameters and specification tests based on the joint behavior of time-series asset dynamics and cross-sectional pricing errors. Our empirical tests reject strongly the standard Merton (1974) model, the Black and Cox (1976) barrier model, and the Longstaff and Schwartz (1995) model with stochastic interest rates. The double exponential jump-diffusion barrier model (Huang and Huang, 2003) improves significantly over the three models. The best model is the stationary leverage model of Collin-Dufresne and Goldstein (2001), which we cannot reject in more than half of our sample firms. However, our empirical results document the inability of the existing structural models to capture the dynamic behavior of CDS spreads and equity volatility, especially for investment grade names. This points to a potential role of time-varying asset volatility, a feature that is missing in the standard structural models.

Suggested Citation

  • Jing-zhi Huang & Hao Zhou, 2008. "Specification analysis of structural credit risk models," Finance and Economics Discussion Series 2008-55, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgfe:2008-55
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    Cited by:

    1. Scheicher, Martin & Raunig, Burkhard, 2008. "A value at risk analysis of cedit default swaps," Working Paper Series 968, European Central Bank.
    2. Zhijian (James) Huang & Yuchen Luo, 2016. "Revisiting Structural Modeling of Credit Risk—Evidence from the Credit Default Swap (CDS) Market," JRFM, MDPI, vol. 9(2), pages 1-20, May.
    3. Feldhütter, Peter & Schaefer, Stephen, 2023. "Debt dynamics and credit risk," Journal of Financial Economics, Elsevier, vol. 149(3), pages 497-535.
    4. Ming Xi Huang, 2010. "Modelling Default Correlations in a Two-Firm Model with Dynamic Leverage Ratios," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 15, July-Dece.
    5. 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.
    6. 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.
    7. 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.
    8. Song Han & Hao Zhou, 2016. "Effects of Liquidity on the Non-Default Component of Corporate Yield Spreads: Evidence from Intraday Transactions Data," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 6(03), pages 1-49, September.
    9. Ming Xi Huang, 2010. "Modelling Default Correlations in a Two-Firm Model with Dynamic Leverage Ratios," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 4-2010.
    10. Christian Gross & Pierre L. Siklos, 2020. "Analyzing credit risk transmission to the nonfinancial sector in Europe: A network approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(1), pages 61-81, January.
    11. NUCU, Anca Elena, 2011. "Managementul riscului de creditare: realizari actuale, analiza critica, sugestii [Credit risk management: current achievements, critical analysis, suggestions]," MPRA Paper 27932, University Library of Munich, Germany.
    12. Jing-Zhi Huang & Bibo Liu & Zhan Shi, 2023. "Determinants of Short-Term Corporate Yield Spreads: Evidence from the Commercial Paper Market," Review of Finance, European Finance Association, vol. 27(2), pages 539-579.
    13. Perrakis, Stylianos & Zhong, Rui, 2015. "Credit spreads and state-dependent volatility: Theory and empirical evidence," Journal of Banking & Finance, Elsevier, vol. 55(C), pages 215-231.
    14. Kucuk, Ugur N., 2010. "Non-default Component of Sovereign Emerging Market Yield Spreads and its Determinants: Evidence from Credit Default Swap Market," MPRA Paper 27428, University Library of Munich, Germany.
    15. Monica Dudian & Monica Balcan Maciuca, 2010. "Internal Ratings Systems: An Empirical Approach," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 5(1), pages 71-79, april.
    16. Corvino, Raffaele & Fusai, Gianluca, 2022. "Default risk premium and asset prices," Journal of Financial Stability, Elsevier, vol. 60(C).
    17. Kita, Arben & Tortorice, Daniel L., 2021. "Same firm, two volatilities: How variance risk is priced in credit and equity markets," Journal of Corporate Finance, Elsevier, vol. 69(C).
    18. Peter J. Zeitsch, 2017. "Capital Structure Arbitrage under a Risk-Neutral Calibration," JRFM, MDPI, vol. 10(1), pages 1-23, January.
    19. 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.
    20. 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.

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    More about this item

    Keywords

    Econometric models; Risk management;

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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