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Industry specific defaults

Author

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  • Kwon, Tae Yeon
  • Lee, Yoonjung

Abstract

In this paper, the hidden common factor for a default correlation model is expanded to industry. By introducing industry-specific hidden factors as random effects, a comparison is made of the relative scale of within- and between-industries correlations. Empirical analysis is based on 14,249 U.S. public firms between 1990 and 2014. A comparison study among the without-hidden-factor model, the common-hidden-factor model, and our industry-specific common-factor model show that an industry-specific common factor is necessary for adjusting time and industry specific over- or under-estimation of default probabilities. The Monte Carlo EM algorithm is adopted for model estimation.

Suggested Citation

  • Kwon, Tae Yeon & Lee, Yoonjung, 2018. "Industry specific defaults," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 45-58.
    • Handle: RePEc:eee:empfin:v:45:y:2018:i:c:p:45-58
    DOI: 10.1016/j.jempfin.2017.10.002
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    References listed on IDEAS

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    More about this item

    Keywords

    Intensity credit risk model; Within industry default correlation; Between industries default correlation; Frailty; MCEM;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access

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