Berentsen, Geir D. and Bulla, Jan and Maruotti, Antonello and Stove, Bard (2022) Modelling clusters of corporate defaults: Regime-switching models significantly reduce the contagion source. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, 71 (3). pp. 698-722. ISSN 0035-9254, 1467-9876
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In this paper, we report robust evidence that the process of corporate defaults is time-dependent and can be modelled by extending an autoregressive count time series model class via the introduction of regime-switching. That is, some of the parameters of the model depend on the regime of an unobserved Markov chain, capturing the model changes during clusters observed for count time series in corporate defaults. Thus, the process of corporate defaults is more dynamic than previously believed. Moreover, the contagion effect-that current defaults affect the probability of other firms defaulting in the future-is reduced compared to models without regime-switching, and is only present in one regime. A two-regime model drives the counts of monthly corporate defaults in the United States. To estimate the model, we introduce a novel quasi-maximum likelihood estimator by adapting the extended Hamilton-Gray algorithm for the Poisson autoregressive model.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | HIDDEN MARKOV-MODELS; TIME-SERIES; STATES; FRAILTY; NUMBER; extended Hamilton-Gray algorithm; integer valued time series; Poisson autoregression; RS-INGARCHX |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Psychiatrie und Psychotherapie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 16 Feb 2024 06:51 |
| Last Modified: | 16 Feb 2024 06:51 |
| URI: | https://pred.uni-regensburg.de/id/eprint/57891 |
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