Estimating persistence in the volatility of asset returns with signal plus noise models

Caporale, Guglielmo Maria and Gil-Alana, Luis A. (2010) Estimating persistence in the volatility of asset returns with signal plus noise models. Centre for International Capital Markets discussion papers, 2010 (09). pp. 1-19. ISSN 1749-3412

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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.

Item Type:	Article
Uncontrolled Keywords:	Centre for International Capital Markets discussion papers; CICM discussion papers; fractional integration; long memory; stochastic volatility; asset returns
Subjects:	300 Social sciences > 330 Economics
Department:	Guildhall School of Business and Law
Depositing User:	Mary Burslem
Date Deposited:	20 Apr 2015 11:01
Last Modified:	08 May 2018 10:37
URI:	http://repository.londonmet.ac.uk/id/eprint/421

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