Abstract
ABSTRACTThis 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 (Annals of Statistics 23: 1630–1661), and shown by Arteche (Journal of Econometrics 119: 131–154) 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. Copyright © 2011 John Wiley & Sons, Ltd.
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