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A varying-coefficient default model

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  • Hwang, Ruey-Ching

Abstract

In this paper, a default prediction method based on the discrete-time varying-coefficient hazard model (DVHM) is proposed. The new model is constructed by replacing the constant coefficients of firm-specific predictors in the discrete-time hazard model (DHM; see Shumway, 2001; and Chava & Jarrow, 2004) with the smooth functions of macroeconomic variables. Thus, it allows the effects of those firm-specific predictors on the default prediction to change with the macroeconomic dynamics (Pesaran, Schuermann, Treutler, & Weiner, 2006). The coefficient functions in the new model are estimated by a local likelihood approach. One real panel dataset is used to illustrate the proposed methodology. Using an expanding rolling window approach, the empirical results confirm that DVHM has a better and more robust performance than the usual DHM, in the sense that it yields more accurate predicted numbers of defaults and predictive intervals through out-of-sample analysis. Thus, the proposed model is a useful alternative for studying default losses on portfolios.

Suggested Citation

  • Hwang, Ruey-Ching, 2012. "A varying-coefficient default model," International Journal of Forecasting, Elsevier, vol. 28(3), pages 675-688.
  • Handle: RePEc:eee:intfor:v:28:y:2012:i:3:p:675-688
    DOI: 10.1016/j.ijforecast.2011.11.006
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    2. Ruey-Ching Hwang & Huimin Chung & C. K. Chu, 2016. "A Two-Stage Probit Model for Predicting Recovery Rates," Journal of Financial Services Research, Springer;Western Finance Association, vol. 50(3), pages 311-339, December.
    3. Ruey-Ching Hwang & Chih-Kang Chu & Kaizhi Yu, 2021. "Predicting the Loss Given Default Distribution with the Zero-Inflated Censored Beta-Mixture Regression that Allows Probability Masses and Bimodality," Journal of Financial Services Research, Springer;Western Finance Association, vol. 59(3), pages 143-172, June.
    4. Jairaj Gupta & Andros Gregoriou & Jerome Healy, 2015. "Forecasting bankruptcy for SMEs using hazard function: To what extent does size matter?," Review of Quantitative Finance and Accounting, Springer, vol. 45(4), pages 845-869, November.
    5. Kalak, Izidin El & Azevedo, Alcino & Hudson, Robert & Karim, Mohamad Abd, 2017. "Stock liquidity and SMEs’ likelihood of bankruptcy: Evidence from the US market," Research in International Business and Finance, Elsevier, vol. 42(C), pages 1383-1393.
    6. Ruey-Ching Hwang & Chih-Kang Chu, 2013. "Forecasting forward defaults: a simple hazard model with competing risks," Quantitative Finance, Taylor & Francis Journals, vol. 14(8), pages 1467-1477, August.
    7. Djeundje, Viani Biatat & Crook, Jonathan, 2019. "Dynamic survival models with varying coefficients for credit risks," European Journal of Operational Research, Elsevier, vol. 275(1), pages 319-333.
    8. El Kalak, Izidin & Hudson, Robert, 2016. "The effect of size on the failure probabilities of SMEs: An empirical study on the US market using discrete hazard model," International Review of Financial Analysis, Elsevier, vol. 43(C), pages 135-145.
    9. Dendramis, Y. & Tzavalis, E. & Varthalitis, P. & Athanasiou, E., 2020. "Predicting default risk under asymmetric binary link functions," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1039-1056.
    10. Calabrese, Raffaella & Crook, Jonathan, 2020. "Spatial contagion in mortgage defaults: A spatial dynamic survival model with time and space varying coefficients," European Journal of Operational Research, Elsevier, vol. 287(2), pages 749-761.
    11. Dendramis, Y. & Tzavalis, E. & Adraktas, G., 2018. "Credit risk modelling under recessionary and financially distressed conditions," Journal of Banking & Finance, Elsevier, vol. 91(C), pages 160-175.

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