The detection and estimation of long memory in stochastic volatility

We propose a new time series representation of persistence in conditional variance called a long memory stochastic volatility (LMSV) model. The LMSV model is constructed by incorporating an ARFIMA process in a standard stochastic volatility scheme. Strongly consistent estimators of the parameters of...

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Bibliographic Details
Published inJournal of econometrics Vol. 83; no. 1; pp. 325 - 348
Main Authors Breidt, F.Jay, Crato, Nuno, de Lima, Pedro
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 01.03.1998
Elsevier
Elsevier Sequoia S.A
SeriesJournal of Econometrics
Subjects
Econometrics
Economic models
Economic theory
EGARCH
Estimating techniques
Estimation
Fractional ARMA
Monte Carlo simulation
Spectral likelihood estimators
Stochastic models
Stochastic processes
Stock prices
Studies
Time series
Variance
Volatility
United States > US
Spectral likelihood estimators
C22
Fractional ARMA
EGARCH
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Summary:We propose a new time series representation of persistence in conditional variance called a long memory stochastic volatility (LMSV) model. The LMSV model is constructed by incorporating an ARFIMA process in a standard stochastic volatility scheme. Strongly consistent estimators of the parameters of the model are obtained by maximizing the spectral approximation to the Gaussian likelihood. The finite sample properties of the spectral likelihood estimator are analyzed by means of a Monte Carlo study. An empirical example with a long time series of stock prices demonstrates the superiority of the LMSV model over existing (short-memory) volatility models.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0304-4076
1872-6895
DOI:10.1016/S0304-4076(97)00072-9