Multiperiod Corporate Default Prediction - A Forward Intensity Approach

48 Pages Posted: 26 Mar 2011 Last revised: 18 May 2012

See all articles by Jin-Chuan Duan

Jin-Chuan Duan

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

Jie Sun

Oversea-Chinese Banking Corporation Limited

Tao Wang

National University of Singapore (NUS) - Department of Finance

Date Written: May 16, 2012

Abstract

A forward intensity model for the prediction of corporate defaults over different future periods is proposed. Maximum pseudo-likelihood analysis is then conducted on a large sample of the US industrial and financial firms spanning the period 1991-2011 on a monthly basis. Several commonly used factors and firm-specific attributes are shown to be useful for prediction at both short and long horizons. Our implementation also factors in momentum in some variables and documents their importance in default prediction. The prediction is very accurate for shorter horizons. The accuracy deteriorates somewhat when the horizon is increased to two or three years, but its performance still remains reasonable. The forward intensity model is also amenable to aggregation, which allows for an analysis of default behavior at the portfolio and/or economy level.

Keywords: default, bankruptcy, forward intensity, maximum pseudo-likelihood, forward default probability, cumulative default probability, accuracy ratio

JEL Classification: C41, C53, G33

Suggested Citation

Duan, Jin-Chuan and Sun, Jie and Wang, Tao, Multiperiod Corporate Default Prediction - A Forward Intensity Approach (May 16, 2012). Available at SSRN: https://ssrn.com/abstract=1791222 or http://dx.doi.org/10.2139/ssrn.1791222

Jin-Chuan Duan (Contact Author)

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

1 Business Link
Singapore, 117592
Singapore

Jie Sun

Oversea-Chinese Banking Corporation Limited ( email )

65 Chulia Street #15-00 OCBC Center
049513
Singapore

Tao Wang

National University of Singapore (NUS) - Department of Finance ( email )

Mochtar Riady Building
15 Kent Ridge Drive
Singapore, 119245
Singapore

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