Contemporaneous-Threshold Smooth Transition GARCH Models

Michael Dueker, Zacharias Psaradakis, Martin Sola and Fabio Spagnolo

Studies in Nonlinear Dynamics & Econometrics, 2011, vol. 15, issue 2, 25

Abstract: This paper proposes a contemporaneous-threshold smooth transition GARCH (or C-STGARCH) model for dynamic conditional heteroskedasticity. The C-STGARCH model is a generalization to second conditional moments of the contemporaneous smooth transition threshold autoregressive model of Dueker et al. (2007) in which the regime weights depend on the ex ante probability that a contemporaneous latent regime-specific variable exceeds a threshold value. A key feature of the C-STGARCH model is that its transition function depends on all the parameters of the model as well as on the data. The structural properties of the model are investigated, in addition to the finite-sample properties of the maximum likelihood estimator of its parameters. An application to U.S. stock returns illustrates the practical usefulness of the C-STGARCH model.

Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://doi.org/10.2202/1558-3708.1755 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
Working Paper: Contemporaneous-Threshold Smooth Transition GARCH Models (2009)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sndecm:v:15:y:2011:i:2:n:1

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/snde/html

DOI: 10.2202/1558-3708.1755

Access Statistics for this article

Studies in Nonlinear Dynamics & Econometrics is currently edited by Bruce Mizrach

More articles in Studies in Nonlinear Dynamics & Econometrics from De Gruyter
Bibliographic data for series maintained by Peter Golla ().