Long and short memory conditional heteroskedasticity 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 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.

JEL-codes: C1 (search for similar items in EconPapers)
Date: 1999-06
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Citations: View citations in EconPapers (77)

Published in Econometric Theory, June, 1999, 15(3), pp. 299-336. ISSN: 1469-4360

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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 heteroscedasticity in estimating the memory parameter of levels (1998)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:304

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