Skip to main content
×
Home
    • Aa
    • Aa

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

  • Rohit S. Deo (a1) and Clifford M. Hurvich (a1)
    • Published online: 27 July 2001
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.

Copyright
Corresponding author
Address correspondence to: Rohit Deo, 8-57 KMEC, 44 West 4th Street, New York, NY 10012, USA; e-mail: rdeo@stern.nyu.edu.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Econometric Theory
  • ISSN: 0266-4666
  • EISSN: 1469-4360
  • URL: /core/journals/econometric-theory
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 5 *
Loading metrics...

Abstract views

Total abstract views: 67 *
Loading metrics...

* Views captured on Cambridge Core between September 2016 - 23rd April 2017. This data will be updated every 24 hours.

Errata