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Empirical Studies Of Structural Credit Risk Models And The Application In Default Prediction: Review And New Evidence

Author

Listed:
  • HAN-HSING LEE

    (Graduate Institution of Finance, National Chiao Tung University, Hsunchu, Taiwan)

  • REN-RAW CHEN

    (Finance and Economics, Fordham University, New York, NY 10023, USA)

  • CHENG-FEW LEE

    (Department of Finance and Economics, Rutgers Business School, Rutgers University, Piscataway, NJ 08854, USA)

Abstract
This paper first reviews empirical evidence and estimation methods of structural credit risk models. Next, an empirical investigation of the performance of default prediction under the down-and-out barrier option framework is provided. In the literature review, a brief overview of the structural credit risk models is provided. Empirical investigations in extant literature papers are described in some detail, and their results are summarized in terms of subject and estimation method adopted in each paper. Current estimation methods and their drawbacks are discussed in detail. In our empirical investigation, we adopt the Maximum Likelihood Estimation method proposed by Duan [Mathematical Finance10(1994) 461–462]. This method has been shown by Ericsson and Reneby [Journal of Business78(2005) 707–735] through simulation experiments to be superior to the volatility restriction approach commonly adopted in the literature. Our empirical results surprisingly show that the simple Merton model outperforms the Brockman and Turtle [Journal of Financial Economics67(2003) 511–529] model in default prediction. The inferior performance of the Brockman and Turtle model may be the result of its unreasonable assumption of the flat barrier.

Suggested Citation

  • Han-Hsing Lee & Ren-Raw Chen & Cheng-Few Lee, 2009. "Empirical Studies Of Structural Credit Risk Models And The Application In Default Prediction: Review And New Evidence," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 8(04), pages 629-675.
    • Handle: RePEc:wsi:ijitdm:v:08:y:2009:i:04:n:s0219622009003703
    DOI: 10.1142/S0219622009003703
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    References listed on IDEAS

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    1. Bruche, Max, 2005. "Estimating structural bond pricing models via simulated maximum likelihood," LSE Research Online Documents on Economics 24647, London School of Economics and Political Science, LSE Library.
    2. Delianedis, Gordon & Geske, Robert, 2001. "The Components of Corporate Credit Spreads: Default, Recovery, Tax, Jumps, Liquidity, and Market Factors," University of California at Los Angeles, Anderson Graduate School of Management qt32x284q3, Anderson Graduate School of Management, UCLA.
    3. Saa-Requejo, Jesus & Santa-Clara, Pedro, 1997. "Bond Pricing with Default Risk," University of California at Los Angeles, Anderson Graduate School of Management qt3w71g2ch, Anderson Graduate School of Management, UCLA.
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    Cited by:

    1. 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.
    2. Şaban Çelik, 2013. "Micro Credit Risk Metrics: A Comprehensive Review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(4), pages 233-272, October.

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    More about this item

    Keywords

    Structural credit risk model; estimation approach; default prediction; Maximum Likelihood Estimation (MLE);
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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