Estimating Persistence in the Volatility of Asset Returns with Signal Plus Noise Models

Guglielmo Maria Caporale and Luis Gil-Alana

No 1006, Discussion Papers of DIW Berlin from DIW Berlin, German Institute for Economic Research

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.

Keywords: Fractional integration; long memory; stochastic volatility; asset returns (search for similar items in EconPapers)
JEL-codes: C13 C22 (search for similar items in EconPapers)
Pages: 15 p.
Date: 2010
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-mst
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Related works:
Journal Article: Estimating persistence in the volatility of asset returns with signal plus noise models (2012)
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