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Estimating default barriers from market information

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  • Hoi Ying Wong
  • Tsz Wang Choi

Abstract

Brockman and Turtle [J. Finan. Econ., 2003, 67, 511-529] develop a barrier option framework to show that default barriers are significantly positive. Most implied barriers are typically larger than the book value of corporate liabilities. We show theoretically and empirically that this result is biased due to the approximation of the market value of corporate assets by the sum of the market value of equity and the book value of liabilities. This approximation leads to a significant overestimation of the default barrier. To eliminate this bias, we propose a maximum likelihood (ML) estimation approach to estimate the asset values, asset volatilities, and default barriers. The proposed framework is applied to empirically examine the default barriers of a large sample of industrial firms. This paper documents that default barriers are positive, but not very significant. In our sample, most of the estimated barriers are lower than the book values of corporate liabilities. In addition to the problem with the default barriers, we find significant biases on the estimation of the asset value and the asset volatility of Brockman and Turtle.

Suggested Citation

  • Hoi Ying Wong & Tsz Wang Choi, 2009. "Estimating default barriers from market information," Quantitative Finance, Taylor & Francis Journals, vol. 9(2), pages 187-196.
  • Handle: RePEc:taf:quantf:v:9:y:2009:i:2:p:187-196
    DOI: 10.1080/14697680802047041
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    Cited by:

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    2. Dionne, Georges & Laajimi, Sadok, 2012. "On the determinants of the implied default barrier," Journal of Empirical Finance, Elsevier, vol. 19(3), pages 395-408.
    3. Forte, Santiago & Lovreta, Lidija, 2012. "Endogenizing exogenous default barrier models: The MM algorithm," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1639-1652.
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    5. Jessen, Cathrine & Lando, David, 2015. "Robustness of distance-to-default," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 493-505.
    6. Engelen, Peter-Jan & Kool, Clemens & Li, Ye, 2016. "A barrier options approach to modeling project failure: The case of hydrogen fuel infrastructure," Resource and Energy Economics, Elsevier, vol. 43(C), pages 33-56.
    7. Petra Andrlikova, 2014. "Is Barrier version of Merton model more realistic? Evidence from Europe," Proceedings of International Academic Conferences 0801868, International Institute of Social and Economic Sciences.
    8. Chuang-Chang Chang & Ruey-Jenn Ho, 2017. "Risk-Shifting Behavior At Commercial Banks With Different Deposit Insurance Assessments: Further Evidence From U.S. Markets," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 40(1), pages 55-80, March.
    9. Amaya, Diego & Boudreault, Mathieu & McLeish, Don L., 2019. "Maximum likelihood estimation of first-passage structural credit risk models correcting for the survivorship bias," Journal of Economic Dynamics and Control, Elsevier, vol. 100(C), pages 297-313.
    10. Arianna Agosto & Enrico Moretto, 2012. "Exploiting default probabilities in a structural model with nonconstant barrier," Applied Financial Economics, Taylor & Francis Journals, vol. 22(8), pages 667-679, April.
    11. Chuang-Chang Chang & Jun-Biao Lin & Chun-Chieh Yang, 2015. "The effect of stochastic interest rates on a firm’s capital structure under a generalized model," Review of Quantitative Finance and Accounting, Springer, vol. 45(4), pages 695-719, November.
    12. Brian Kantor & Christopher Holdsworth, 2010. "Lessons from the Global Financial Crisis (Or Why Capital Structure Is Too Important to Be Left to Regulation)," Journal of Applied Corporate Finance, Morgan Stanley, vol. 22(3), pages 112-122, June.

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