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

18 Pages Posted: 14 Jul 2010

See all articles by Guglielmo Maria Caporale

Guglielmo Maria Caporale

Brunel University London - Department of Economics and Finance; London South Bank University; CESifo (Center for Economic Studies and Ifo Institute); German Institute for Economic Research (DIW Berlin)

Luis A. Gil-Alana

University of Navarra - Department of Economics

Date Written: May 1, 2010

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

JEL Classification: C13, C22

Suggested Citation

Caporale, Guglielmo Maria and Gil-Alana, Luis A., Estimating Persistence in the Volatility of Asset Returns with Signal Plus Noise Models (May 1, 2010). DIW Berlin Discussion Paper No. 1006, Available at SSRN: https://ssrn.com/abstract=1639471 or http://dx.doi.org/10.2139/ssrn.1639471

Guglielmo Maria Caporale (Contact Author)

Brunel University London - Department of Economics and Finance ( email )

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London South Bank University ( email )

Centre for Monetary and Financial Economics
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United Kingdom

CESifo (Center for Economic Studies and Ifo Institute)

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German Institute for Economic Research (DIW Berlin) ( email )

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Luis A. Gil-Alana

University of Navarra - Department of Economics ( email )

Campus de Arrosadia
Pamplona, 31006
Spain

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