Nonlinear GARCH Models for Highly Persistent Volatility

43 Pages Posted: 20 Feb 2002

See all articles by Markku Lanne

Markku Lanne

University of Helsinki - Department of Political and Economic Studies

Pentti Saikkonen

University of Helsinki - Department of Statistics

Abstract

In this paper we study new nonlinear GARCH models mainly designed for time series with highly persistent volatility. For such series, conventional GARCH models have often proved unsatisfactory because they tend to exaggerate volatility persistence and exhibit poor forecasting ability. Our main emphasis is on models that are similar to previously introduced smooth transition GARCH models except for the novel feature that a lagged value of conditional variance is used as the transition variable. This choice of the transition variable corresponds to the idea that high persistence in conditional variance is related to relatively infrequent changes in regime. Using the theory of Markov chains we provide sufficient conditions for the stationarity and existence of moments of the considered smooth transition GARCH models and even some more general nonlinear GARCH models. Empirical applications to two exchange rate return series show that the new models can be superior to conventional GARCH models especially in longer term forecasting.

Keywords: nonlinear GARCH models, volatility persistence, exchange rates

JEL Classification: C32, C51, C53, F31

Suggested Citation

Lanne, Markku and Saikkonen, Pentti, Nonlinear GARCH Models for Highly Persistent Volatility. Available at SSRN: https://ssrn.com/abstract=300784 or http://dx.doi.org/10.2139/ssrn.300784

Markku Lanne (Contact Author)

University of Helsinki - Department of Political and Economic Studies ( email )

P.O. Box 54
FIN-00014 Helsinki
Finland
+358-9-191 24626 (Phone)
+358-9-191 24780 (Fax)

Pentti Saikkonen

University of Helsinki - Department of Statistics ( email )

Finland
+09 191 24867 (Phone)

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