The University of Southampton
University of Southampton Institutional Repository

A nonlinear threshold model for the dependence of extremes of stationary sequences

A nonlinear threshold model for the dependence of extremes of stationary sequences
A nonlinear threshold model for the dependence of extremes of stationary sequences
We propose a TAR(3,1)-GARCH(1,1) model able to describe two different types of extreme events: a first type generated by large uncertainty regimes and a second type where extremes come from isolated dread/joy events. The novelty of this model resides on the definition of the regimes, motivated by the occurrence of extreme values, and of the threshold variable, defined by the shock affecting the process one period lagged. The model is able to uncover dependence and clustering of extremes in high and low volatility periods. A Wald type test to detect nonlinearities on the conditional mean process defined by an unobservable threshold variable is introduced. In the empirical application, we find evidence of predictability for extreme returns on SPDR S&P500 fund during the recent crisis period, July 2008 to March 2011. This finding seems to support the presence of some persistence and mean reversion in the dynamics of returns after the occurrence of extreme shocks.
1558-3708
1-3
Martinez, Oscar
a2a6f05d-bd54-44be-97e0-8b73bbb63964
Olmo, Jose
706f68c8-f991-4959-8245-6657a591056e
Martinez, Oscar
a2a6f05d-bd54-44be-97e0-8b73bbb63964
Olmo, Jose
706f68c8-f991-4959-8245-6657a591056e

Martinez, Oscar and Olmo, Jose (2012) A nonlinear threshold model for the dependence of extremes of stationary sequences. Studies in Nonlinear Dynamics & Econometrics, 16 (3), 1-3. (doi:10.1515/1558-3708.1881).

Record type: Article

Abstract

We propose a TAR(3,1)-GARCH(1,1) model able to describe two different types of extreme events: a first type generated by large uncertainty regimes and a second type where extremes come from isolated dread/joy events. The novelty of this model resides on the definition of the regimes, motivated by the occurrence of extreme values, and of the threshold variable, defined by the shock affecting the process one period lagged. The model is able to uncover dependence and clustering of extremes in high and low volatility periods. A Wald type test to detect nonlinearities on the conditional mean process defined by an unobservable threshold variable is introduced. In the empirical application, we find evidence of predictability for extreme returns on SPDR S&P500 fund during the recent crisis period, July 2008 to March 2011. This finding seems to support the presence of some persistence and mean reversion in the dynamics of returns after the occurrence of extreme shocks.

Text
j$002fsnde.2012.16.issue-3$002f1558-3708.1881$002f1558-3708.1881.xml - Version of Record
Restricted to Registered users only
Download (51kB)
Request a copy

More information

e-pub ahead of print date: 18 September 2012
Published date: September 2012
Organisations: Economics

Identifiers

Local EPrints ID: 348612
URI: http://eprints.soton.ac.uk/id/eprint/348612
ISSN: 1558-3708
PURE UUID: 54a6c96e-298c-4f57-8dd2-17e126eb3eae
ORCID for Jose Olmo: ORCID iD orcid.org/0000-0002-0437-7812

Catalogue record

Date deposited: 15 Feb 2013 15:19
Last modified: 15 Mar 2024 03:46

Export record

Altmetrics

Contributors

Author: Oscar Martinez
Author: Jose Olmo ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×