Forecasting Forward Defaults with the Discrete‐Time Hazard Model

Abstract

For predicting forward default probabilities of firms, the discrete‐time forward hazard model (DFHM) is proposed. We derive maximum likelihood estimates for the parameters in DFHM. To improve its predictive power in practice, we also consider an extension of DFHM by replacing its constant coefficients of firm‐specific predictors with smooth functions of macroeconomic variables. The resulting model is called the discrete‐time varying‐coefficient forward hazard model (DVFHM). Through local maximum likelihood analysis, DVFHM is shown to be a reliable and flexible model for forward default prediction. We use real panel datasets to illustrate these two models. Using an expanding rolling window approach, our empirical results confirm that DVFHM has better and more robust out‐of‐sample performance on forward default prediction than DFHM, in the sense of yielding more accurate predicted numbers of defaults and predicted survival times. Thus DVFHM is a useful alternative for studying forward default losses in portfolios. Copyright © 2013 John Wiley & Sons, Ltd.

Study of the classification accuracy measures for predicting corporate bankruptcy taking into account changes in the economic environment

“…In the database, selected macroeconomic indicators were included, such as (e.g. Hwang and Chu, 2014):…”

“…De Leonardis and Rocci, 2008), hazard models (e.g. Hwang and Chu, 2014;Shumway, 2001;Trabelsi et al, 2015) and, naturally the literature on this subject is much more extensive. Among these proposals there are fairly rare comparative studies on the effectiveness of the proposed approaches.…”

On assessing the relative performance of default predictions

“…The present note is focused on the predictive performance as such. Whereas there is a rich literature on how probability forecasts are best produced (see, e.g., Hwang & Chu, , for a recent contribution in the present journal), much less is known on how competing probability forecasters can be ranked in terms of predictive accuracy, that is, on how the ESMA demands can best be met. This note shows in Section 2 that the concept of calibration (DeGroot & Fienberg, ) is a rather tough requirement which prevents most calibrated probability forecasters from being unequivocally comparable in terms of conditional default or survival distributions.…”