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Structural models of corporate bond pricing with maximum likelihood estimation

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  • Li, Ka Leung
  • Wong, Hoi Ying

Abstract

This paper empirically examines the proxy, volatility-restriction (VR) and maximum likelihood (ML) approaches to implementing structural corporate bond pricing models, and documents that ML estimation is the best among the three implementation methods. Empirical studies using either the proxy approach or the VR method conclude that barrier-independent models significantly underestimate corporate bond yields. Although barrier-dependent models tend to overestimate the yield on average, they generate a sizable degree of underestimation. The present paper shows that the proxy approach is an upwardly biased estimator of the corporate assets and makes the empirical framework work systematically against structural models of corporate bond pricing. The VR approach may generate inconsistent corporate bond prices or may fail to give a positive corporate bond price for some structural models. When the Merton, LS, BD and LT models are implemented with ML estimation, we find substantial improvement in their performances. Our empirical analysis shows that the LT model is very accurate for predicting short-term bond yields, whereas the LS and BD models are good predictors for medium-term and long-term bonds. The Merton model however significantly overestimates short-term bond yields and underestimates long-term bond yields. Unlike empirical studies in the past, the Merton model implemented with ML estimation does not consistently underestimate corporate bond yields.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:empfin:v:15:y:2008:i:4:p:751-777
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Forte, Santiago & Lovreta, Lidija, 2012. "Endogenizing exogenous default barrier models: The MM algorithm," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1639-1652.
    5. 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.
    6. Chan, Ngai Hang & Wong, Hoi Ying & Zhao, Jing, 2012. "Structural model of credit migration," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3477-3490.
    7. Jarrow, Robert A., 2011. "Credit market equilibrium theory and evidence: Revisiting the structural versus reduced form credit risk model debate," Finance Research Letters, Elsevier, vol. 8(1), pages 2-7, March.

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