Long and short memory conditional heteroskedasticity in estimating the memory parameter of levels

Robinson, Peter M. and Henry, M. (1999) Long and short memory conditional heteroskedasticity in estimating the memory parameter of levels. Econometric Theory, 15 (3). pp. 299-336. ISSN 1469-4360

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Abstract

Semiparametric estimates of long memory seem useful in the analysis of long financial time series because they are consistent under much broader conditions than parametric estimates. However, recent large sample theory for semiparametric estimates forbids conditional heteroskedasticity. We show that a leading semiparametric estimate, the Gaussian or local Whittle one, can be consistent and have the same limiting distribution under conditional heteroskedasticity as under the conditional homoskedasticity assumed by Robinson (1995, Annals of Statistics 23, 1630–61). Indeed, noting that long memory has been observed in the squares of financial time series, we allow, under regularity conditions, for conditional heteroskedasticity of the general form introduced by Robinson (1991, Journal of Econometrics 47, 67–84), which may include long memory behavior for the squares, such as the fractional noise and autoregressive fractionally integrated moving average form, and also standard short memory ARCH and GARCH specifications.

Item Type:	Article
Official URL:	http://uk.cambridge.org/journals/ect/
Additional Information:	Copyright © 1999 Cambridge University Press. LSE has developed LSE Research Online so that users may access research output of the School. Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Users may download and/or print one copy of any article(s) in LSE Research Online to facilitate their private study or for non-commercial research. You may not engage in further distribution of the material or use it for any profit-making activities or any commercial gain. You may freely distribute the URL (http://eprints.lse.ac.uk) of the LSE Research Online website.
Divisions:	Economics
Subjects:	H Social Sciences > HA Statistics
Date Deposited:	15 Feb 2008
Last Modified:	17 Nov 2024 18:24
URI:	http://eprints.lse.ac.uk/id/eprint/304

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