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Default prediction with dynamic sectoral and macroeconomic frailties

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  • Chen, Peimin
  • Wu, Chunchi

Abstract

This paper extends the macroeconomic frailty model to include sectoral frailty factors that capture default correlations among firms in a similar business. We estimate sectoral and macroeconomic frailty factors and their effects on default intensity using the data for Japanese firms from 1992 to 2010. We find strong evidence for the presence of sectoral frailty factors even after accounting for the effects of observable covariates and macroeconomic frailty on default intensity. The model with sectoral frailties performs better than that without. Results show that accounting for the sources of unobserved sectoral default risk covariations improves the accuracy of default probability estimation.

Suggested Citation

  • Chen, Peimin & Wu, Chunchi, 2014. "Default prediction with dynamic sectoral and macroeconomic frailties," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 211-226.
  • Handle: RePEc:eee:jbfina:v:40:y:2014:i:c:p:211-226
    DOI: 10.1016/j.jbankfin.2013.11.036
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    References listed on IDEAS

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    1. Duffie, Darrell & Saita, Leandro & Wang, Ke, 2007. "Multi-period corporate default prediction with stochastic covariates," Journal of Financial Economics, Elsevier, vol. 83(3), pages 635-665, March.
    2. Darrell Duffie & Andreas Eckner & Guillaume Horel & Leandro Saita, 2009. "Frailty Correlated Default," Journal of Finance, American Finance Association, vol. 64(5), pages 2089-2123, October.
    3. Lu, Yang-Cheng & Shen, Chung-Hua & Wei, Yu-Chen, 2013. "Revisiting early warning signals of corporate credit default using linguistic analysis," Pacific-Basin Finance Journal, Elsevier, vol. 24(C), pages 1-21.
    4. Naifar, Nader, 2011. "What explains default risk premium during the financial crisis? Evidence from Japan," Journal of Economics and Business, Elsevier, vol. 63(5), pages 412-430, September.
    5. Sanjiv R. Das & Darrell Duffie & Nikunj Kapadia & Leandro Saita, 2007. "Common Failings: How Corporate Defaults Are Correlated," Journal of Finance, American Finance Association, vol. 62(1), pages 93-117, February.
    6. Koopman, Siem Jan & Lucas, Andre & Monteiro, Andre, 2008. "The multi-state latent factor intensity model for credit rating transitions," Journal of Econometrics, Elsevier, vol. 142(1), pages 399-424, January.
    7. Hertzel, Michael G. & Officer, Micah S., 2012. "Industry contagion in loan spreads," Journal of Financial Economics, Elsevier, vol. 103(3), pages 493-506.
    8. Duffie, Darrell & Lando, David, 2001. "Term Structures of Credit Spreads with Incomplete Accounting Information," Econometrica, Econometric Society, vol. 69(3), pages 633-664, May.
    9. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    10. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    11. Jarrow, Robert & Li, Haitao & Liu, Sheen & Wu, Chunchi, 2010. "Reduced-form valuation of callable corporate bonds: Theory and evidence," Journal of Financial Economics, Elsevier, vol. 95(2), pages 227-248, February.
    12. Maria Vassalou & Yuhang Xing, 2004. "Default Risk in Equity Returns," Journal of Finance, American Finance Association, vol. 59(2), pages 831-868, April.
    13. Jorion, Philippe & Zhang, Gaiyan, 2007. "Good and bad credit contagion: Evidence from credit default swaps," Journal of Financial Economics, Elsevier, vol. 84(3), pages 860-883, June.
    14. Fukuda, Shin-ichi & Kasuya, Munehisa & Akashi, Kentaro, 2009. "Impaired bank health and default risk," Pacific-Basin Finance Journal, Elsevier, vol. 17(2), pages 145-162, April.
    15. Acharya, Viral V. & Bharath, Sreedhar T. & Srinivasan, Anand, 2007. "Does industry-wide distress affect defaulted firms? Evidence from creditor recoveries," Journal of Financial Economics, Elsevier, vol. 85(3), pages 787-821, September.
    16. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    17. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    18. Sreedhar T. Bharath & Tyler Shumway, 2008. "Forecasting Default with the Merton Distance to Default Model," Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1339-1369, May.
    19. Kimie Harada & Takatoshi Ito & Shuhei Takahashi, 2010. "Is the Distance to Default a Good Measure in Predicting Bank Failures? Case Studies," NBER Working Papers 16182, National Bureau of Economic Research, Inc.
    20. Philippe Jorion & Gaiyan Zhang, 2009. "Credit Contagion from Counterparty Risk," Journal of Finance, American Finance Association, vol. 64(5), pages 2053-2087, October.
    21. Kao, Chihwa & Wu, Chunchi, 1990. "Two-Step Estimation of Linear Models with Ordinal Unobserved Variables: The Case of Corporate Bonds," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(3), pages 317-325, July.
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    More about this item

    Keywords

    Default risk; Hazard rate function; Frailty; Distance to default; Tail loss; Monte Carlo expectations maximization (EM); Gibbs sampler;

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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