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Exploring the Sources of Default Clustering

by Shahriar Azizpour of Stanford University,
Kay Giesecke of Stanford University, and
Gustavo Schwenkler of Stanford University

January 10, 2012

Abstract: We develop and implement a filtering approach to maximum likelihood estimation, goodness-of-fit testing and prediction for event timing models in which the arrival intensity is influenced by past events and stochastic covariates, some of which cannot be measured. Applying these tools to default events of US firms between 1970 and 2010, we find that the response of the intensity to defaults is economically and statistically significant, after controlling for the influence of the macro-economic covariates that prior studies have identified as predictors of US defaults, and for the role of an unobservable frailty risk factor whose importance for US default timing was recently established. Both frailty and contagion, by which the default by one firm has a direct impact on the health of other firms, are significant sources of default clustering.

JEL Classification: C11, C15, C41, E44, G33.

Keywords: Correlated default, event feedback, contagion, frailty, self-exciting point process, intensity, point process filtering and smoothing, measure change, likelihood, goodness-of- t, time change.

Previously titled: Self-exciting Corporate Defaults: Contagion vs. frailty

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