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Specification Analysis of Structural Credit Risk Models
[Corporate bond valuation and hedging with stochastic interest rates and endogenous bankruptcy]

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  • Jing-Zhi Huang
  • Zhan Shi
  • Hao Zhou

Abstract

Empirical studies of structural credit risk models so far are often based on calibration, rolling estimation, or regressions. This paper proposes a GMM-based method that allows us to estimate model parameters and test model-implied restrictions in a unified framework. We conduct a specification analysis of five representative structural models based on the proposed GMM procedure, using information from both equity volatility and the term structure of single-name credit default swap (CDS) spreads. Our test results strongly reject the Merton (1974) model and two diffusion-based models with a flat default boundary. The other two models, one with jumps and one with stationary leverage ratios, do improve the overall fit of CDS spreads and equity volatility. However, all five models have difficulty capturing the dynamic behavior of both equity volatility and CDS spreads, especially for investment-grade names. On the other hand, these models have a much better ability to explain the sensitivity of CDS spreads to equity returns.

Suggested Citation

  • 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.
  • Handle: RePEc:oup:revfin:v:24:y:2020:i:1:p:45-98.
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Peter J. Zeitsch, 2017. "Capital Structure Arbitrage under a Risk-Neutral Calibration," JRFM, MDPI, vol. 10(1), pages 1-23, January.
    7. 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.
    8. Feldhütter, Peter & Schaefer, Stephen, 2023. "Debt dynamics and credit risk," Journal of Financial Economics, Elsevier, vol. 149(3), pages 497-535.
    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 15, July-Dece.
    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. Scheicher, Martin & Raunig, Burkhard, 2008. "A value at risk analysis of credit default swaps," Discussion Paper Series 2: Banking and Financial Studies 2008,12, Deutsche Bundesbank.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. Corvino, Raffaele & Fusai, Gianluca, 2022. "Default risk premium and asset prices," Journal of Financial Stability, Elsevier, vol. 60(C).
    18. 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).
    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.

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    More about this item

    Keywords

    Structural credit risk models; GMM; Consistent specification analysis; Credit default swaps; CDS hedging; Jump-diffusion models; Stochastic asset volatility; Realized equity volatility;
    All these keywords.

    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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