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Estimating Persistence in the Volatility of Asset Returns with Signal Plus Noise ModelsGuglielmo Maria CaporaleLondon South Bank University; Brunel University - Brunel Business School; CESifo (Center for Economic Studies and Ifo Institute for Economic Research) Luis A. Gil-AlanaUniversity of Navarra - Department of Economics May 1, 2010 DIW Berlin Discussion Paper No. 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.
Number of Pages in PDF File: 18 Keywords: Fractional Integration, Long Memory, Stochastic Volatility, Asset Returns JEL Classification: C13, C22 working papers seriesDate posted: July 14, 2010Suggested CitationContact Information
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