We study asymptotic normality of the randomized periodogram estimator of quadratic variation in the mixed Brownian–fractional Brownian model. In the semimartingale case, that is, where the Hurst parameter H of the fractional part satisfies H∈(3/4,1), the central limit theorem holds. In the nonsemimartingale case, that is, where H∈(1/2,3/4], the convergence toward the normal distribution with a nonzero mean still holds if H=3/4, whereas for the other values, that is, H∈(1/2,3/4), the central convergence does not take place. We also provide Berry–Esseen estimates for the estimator.©2015 The Author(s). Published by VTeX. Open access article under the CC BY license.fi=vertaisarvioitu|en=peerReviewed
International audienceWe give a new proof and provide new bounds for the speed of convergence in the...
International audience\noindent We study the asymptotic behavior as $n\to \infty$ of the sequence $$...
AbstractWe observe (Yt) at times i/n, i=0,…,n, in the parametric stochastic volatility modeldYt=Φ(θ,...
We study asymptotic normality of the randomized periodogram estimator of quadratic variation in the ...
International audienceThis paper gives a central limit theorem for the generalized quadratic variati...
Dzhaparidze and Spreij (Stoch Process Appl, 54:165–174, 1994) showed that the quadratic variation of...
This article presents a weak law of large numbers and a central limit theorem for the scaled realise...
In this thesis we apply the Malliavin calculus to statistical estimation of parameters of stochastic...
AbstractIn this paper we give a central limit theorem for the weighted quadratic variation process o...
30 pages; minor changesInternational audienceIn this paper, we prove some central and non-central li...
AbstractIn this paper we present a central limit theorem for general functions of the increments of ...
In this paper we introduce the multivariate Brownian semistationary (BSS) process and study the join...
In recent years, there has been substantive empirical evidence that stochastic volatility is rough. ...
In this paper, we present the asymptotic theory for integrated functions of increments of Brownian l...
We establish the asymptotic normality of a quadratic form QnQn in martingale difference random varia...
International audienceWe give a new proof and provide new bounds for the speed of convergence in the...
International audience\noindent We study the asymptotic behavior as $n\to \infty$ of the sequence $$...
AbstractWe observe (Yt) at times i/n, i=0,…,n, in the parametric stochastic volatility modeldYt=Φ(θ,...
We study asymptotic normality of the randomized periodogram estimator of quadratic variation in the ...
International audienceThis paper gives a central limit theorem for the generalized quadratic variati...
Dzhaparidze and Spreij (Stoch Process Appl, 54:165–174, 1994) showed that the quadratic variation of...
This article presents a weak law of large numbers and a central limit theorem for the scaled realise...
In this thesis we apply the Malliavin calculus to statistical estimation of parameters of stochastic...
AbstractIn this paper we give a central limit theorem for the weighted quadratic variation process o...
30 pages; minor changesInternational audienceIn this paper, we prove some central and non-central li...
AbstractIn this paper we present a central limit theorem for general functions of the increments of ...
In this paper we introduce the multivariate Brownian semistationary (BSS) process and study the join...
In recent years, there has been substantive empirical evidence that stochastic volatility is rough. ...
In this paper, we present the asymptotic theory for integrated functions of increments of Brownian l...
We establish the asymptotic normality of a quadratic form QnQn in martingale difference random varia...
International audienceWe give a new proof and provide new bounds for the speed of convergence in the...
International audience\noindent We study the asymptotic behavior as $n\to \infty$ of the sequence $$...
AbstractWe observe (Yt) at times i/n, i=0,…,n, in the parametric stochastic volatility modeldYt=Φ(θ,...