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Estimation of the long memory parameter in stochastic volatility models by quadratic variations
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Ionuţ Florescu
and
Ciprian A. Tudor
Published/Copyright:
April 20, 2011
Abstract
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.
Keywords.: Stochastic volatility model; multiple stochastic integral; fractional Brownian motion; Malliavin calculus; quadratic variation; Hurst parameter; self-similarity; statistical estimation
Received: 2010-10-02
Accepted: 2011-03-02
Published Online: 2011-04-20
Published in Print: 2011-June
© de Gruyter 2011
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Articles in the same Issue
- To Anatolii Volodymyrovych Skorokhod's memory
- Preface
- Almost sure asymptotic stability and convergence of stochastic Theta methods applied to systems of linear SDEs in
- Strong uniform consistency of a nonparametric estimator of a conditional quantile for censored dependent data and functional regressors
- Central limit theorem associated with bilinear random fields
- Almost sure exponential stability of the Euler–Maruyama approximations for stochastic functional differential equations
- An improvement of subword complexity
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