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ON THE LOG PERIODOGRAM REGRESSION ESTIMATOR OF THE MEMORY PARAMETER IN LONG MEMORY STOCHASTIC VOLATILITY MODELS

Published online by Cambridge University Press:  27 July 2001

Rohit S. Deo  and
Clifford M. Hurvich
Rohit S. Deo
Affiliation:
New York University
Clifford M. Hurvich
Affiliation:
New York University
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Abstract

We consider semiparametric estimation of the memory parameter in a long memory stochastic volatility model. We study the estimator based on a log periodogram regression as originally proposed by Geweke and Porter-Hudak (1983, Journal of Time Series Analysis 4, 221–238). Expressions for the asymptotic bias and variance of the estimator are obtained, and the asymptotic distribution is shown to be the same as that obtained in recent literature for a Gaussian long memory series. The theoretical result does not require omission of a block of frequencies near the origin. We show that this ability to use the lowest frequencies is particularly desirable in the context of the long memory stochastic volatility model.

Type
Research Article
Information
Econometric Theory , Volume 17 , Issue 4 , August 2001 , pp. 686 - 710
Copyright
© 2001 Cambridge University Press

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