Multiperiod Corporate Default Prediction with the Partially-Conditioned Forward Intensity

37 Pages Posted: 24 Sep 2012 Last revised: 18 Oct 2013

See all articles by Jin-Chuan Duan

Jin-Chuan Duan

National University of Singapore (NUS) - Business School and Risk Management Institute

Andras Fulop

ESSEC Business School

Date Written: August 21, 2013

Abstract

The forward-intensity model of Duan, {et al} (2012) is a parsimonious and practical way for predicting corporate defaults over multiple horizons. However, it has a noticeable shortcoming because default correlations through intensities are conspicuously absent when the prediction horizon is more than one data period. We propose a new forward-intensity approach that builds in correlations among intensities of individual obligors by conditioning all forward intensities on the future values of some common variables, such as the observed interest rate and/or a latent frailty factor. The new model is implemented on a large sample of US industrial and financial firms spanning the period 1991-2011 on the monthly frequency. Our findings suggest that the new model is able to correct the structural biases at longer prediction horizons reported in Duan et al (2012). Not surprisingly, default correlations are also found to be important in describing the joint default behavior.

Keywords: default, forward default intensity, pseudo-bayesian inference, sequential monte carlo, self-normalized asymptotics

Suggested Citation

Duan, Jin-Chuan and Fulop, Andras, Multiperiod Corporate Default Prediction with the Partially-Conditioned Forward Intensity (August 21, 2013). Available at SSRN: https://ssrn.com/abstract=2151174 or http://dx.doi.org/10.2139/ssrn.2151174

Jin-Chuan Duan

National University of Singapore (NUS) - Business School and Risk Management Institute ( email )

1 Business Link
Singapore, 117592
Singapore

Andras Fulop (Contact Author)

ESSEC Business School ( email )

3 Avenue Bernard Hirsch
CS 50105 CERGY
CERGY, CERGY PONTOISE CEDEX 95021
France

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