Estimation of the long memory parameter in stochastic volatility models by quadratic variations

We consider a stochastic volatility model where the volatility process is a fractional Brownian motion. We estimate the memory parameter of the volatility from discrete observations of the price process. We use criteria based on Malliavin calculus in order to characterize the asymptotic normality of...

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Bibliographic Details
Published inRandom operators and stochastic equations Vol. 19; no. 2; pp. 197 - 216
Main Authors Florescu, Ionuţ, Tudor, Ciprian A.
Format Journal Article
LanguageEnglish
Published Walter de Gruyter GmbH & Co. KG 01.06.2011
Subjects
fractional Brownian motion
Hurst parameter
Malliavin calculus
multiple stochastic integral
quadratic variation
self-similarity
statistical estimation
Stochastic volatility model
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Summary:We consider a stochastic volatility model where the volatility process is a fractional Brownian motion. We estimate the memory parameter of the volatility from discrete observations of the price process. We use criteria based on Malliavin calculus in order to characterize the asymptotic normality of the estimators.
Bibliography:istex:6CDDC658E17EF237CE61EE241F8A8311403E56E2
ArticleID:ROSE.19.2.197
rose.2011.012.pdf
ark:/67375/QT4-BTV4ZM23-J
ISSN:0926-6364
1569-397X
DOI:10.1515/ROSE.2011.012