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Title:Estimating persistence in the volatility of asset returns with signal plus noise models PDF Logo
Authors:Caporale, Guglielmo Maria
Gil-Alana, Luis A.
Issue Date:2010
Series/Report no.:Discussion papers // German Institute for Economic Research 1006
Abstract:This paper examines the degree of persistence in the volatility of financial time series using a Long Memory Stochastic Volatility (LMSV) model. Specifically, it employs a Gaussian semiparametric (or local Whittle) estimator of the memory parameter, based on the frequency domain, proposed by Robinson (1995a), and shown by Arteche (2004) to be consistent and asymptotically normal in the context of signal plus noise models. Daily data on the NASDAQ index are analysed. The results suggest that volatility has a component of long- memory behaviour, the order of integration ranging between 0.3 and 0.5, the series being therefore stationary and mean-reverting.
Subjects:Fractional integration
long memory
stochastic volatility
asset returns
JEL:C13
C22
Document Type:Working Paper
Appears in Collections:DIW-Diskussionspapiere
Publikationen von Forscherinnen und Forschern des DIW

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