On the Log Periodogram Regression Estimator of the Memory Parameter in Long Memory Stochastic Volatility Models

25 Pages Posted: 31 Oct 2008

See all articles by Rohit Deo

Rohit Deo

Stern School of Business, New York University

Clifford M. Hurvich

Stern School of Business, New York University; New York University (NYU) - Department of Information, Operations, and Management Sciences

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Date Written: 2000

Abstract

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

Suggested Citation

Deo, Rohit and Hurvich, Clifford M., On the Log Periodogram Regression Estimator of the Memory Parameter in Long Memory Stochastic Volatility Models (2000). Statistics Working Papers Series, Vol. , pp. -, 2000. Available at SSRN: https://ssrn.com/abstract=1290967

Rohit Deo

Stern School of Business, New York University ( email )

44 West Fourth Street
New York, NY 10012
United States

Clifford M. Hurvich

Stern School of Business, New York University ( email )

44 West 4th Street
New York, NY 10012-1126
United States

New York University (NYU) - Department of Information, Operations, and Management Sciences

44 West Fourth Street
New York, NY 10012
United States

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