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

Peter M. Robinson and Marc Henry

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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 heteroscedasticity. We show that a leading semiparametric estimate, the Gaussian or local Whittle one, can be consistent and have the same limiting distribution under conditional heteroscedasticity as under conditional homoscedasticity assumed by Robinson (1995a). Indeed, noting that long memory has been observed in the squares of financial time series, we allow, under regularity conditions, for conditional heteroscedasticity of the general form introduced by Robinson (1991) which may include long memory behaviour for the squares, such as the fractional noise and autoregressive fractionally integrated moving average form, as well as standard short memory ARCH and GARCH specifications.

Keywords: long memory; dynamic conditional heteroscedasticity; semiparametric estimation. (search for similar items in EconPapers)
JEL-codes: C13 (search for similar items in EconPapers)
Pages: 35 pages
Date: 1998-08
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

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Related works:
Journal Article: LONG AND SHORT MEMORY CONDITIONAL HETEROSKEDASTICITY IN ESTIMATING THE MEMORY PARAMETER OF LEVELS (1999)
Working Paper: Long and short memory conditional heteroskedasticity in estimating the memory parameter of levels (1999)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:2022

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