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IDENTIFYING NONLINEAR SERIAL DEPENDENCE IN VOLATILE, HIGH-FREQUENCY TIME SERIES AND ITS IMPLICATIONS FOR VOLATILITY MODELING

Published online by Cambridge University Press:  07 April 2010

Phillip Wild ,
John Foster  and
Melvin J. Hinich
Phillip Wild*
Affiliation:
University of Queensland
John Foster
Affiliation:
University of Queensland
Melvin J. Hinich
Affiliation:
University of Texas at Austin
*
Address correspondence to: Phillip Wild, School of Economics, University of Queensland, St. Lucia, QLD 4072, Australia; e-mail: [email protected].
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Abstract

In this article, we show how tests of nonlinear serial dependence can be applied to high-frequency time series data that exhibit high volatility, strong mean reversion, and leptokurtotis. Portmanteau correlation, bicorrelation, and tricorrelation tests are used to detect nonlinear serial dependence in the data. Trimming is used to control for the presence of outliers in the data. The data that are employed are 161,786 half-hourly spot electricity price observations recorded over nearly a decade in the wholesale electricity market in New South Wales, Australia. Strong evidence of nonlinear serial dependence is found and its implications for time series modeling are discussed.

Keywords

NonlinearityPortmanteau TestsBicorrelationTricorrelationTrimmingDeep Structure
Type
Articles
Information
Macroeconomic Dynamics , Volume 14 , Supplement S1: Nonlinear Time Series , May 2010 , pp. 88 - 110
Copyright
Copyright © Cambridge University Press 2010

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