Gaussian and non-Gaussian processes of zero power variation

Russo, Francesco^1, 2 ; Viens, Frederi³ 

¹ ENSTA-ParisTech. Unité de Mathématiques appliquées, 828, bd des Maréchaux, 91120 Palaiseau, France
² INRIA Rocquencourt, Projet MathFi and Cermics, École des Ponts, Rocquencourt, France
³ Department of Statistics, Purdue University, 150 N. University St., West Lafayette, IN 47907-2067, USA

ESAIM: Probability and Statistics, Tome 19 (2015), pp. 414-439

Résumé

We consider a class of stochastic processes $X$ defined by $X\left(t\right)=\int {}_0^T$ $G(t,s)dM\left(s\right)$ for $t\in [0,T]$, where $M$ is a square-integrable continuous martingale and $G$ is a deterministic kernel. Let $m$ be an odd integer. Under the assumption that the quadratic variation $\left[M\right]$ of $M$ is differentiable with $𝐄\left[\right|d\left[M\right]\left(t\right)/dt{|}^m]$ finite, it is shown that the $m$th power variation

$$\begin{array}{c}\hfill \underset{\epsilon \to 0}{lim}{\epsilon }^{-1}{\int }_0^T\mathrm{d}s{\left(X\left(s+\epsilon \right)-X\left(s\right)\right)}^m\end{array}$$

exists and is zero when a quantity $\delta {}^2\left(r\right)$ related to the variance of an increment of $M$ over a small interval of length $r$ satisfies $\delta \left(r\right)=o\left(r^{1/\left(2m\right)}\right)$. When $M$ is the Wiener process, $X$ is Gaussian; the class then includes fractional Brownian motion and other Gaussian processes with or without stationary increments. When $X$ is Gaussian and has stationary increments, $\delta$ is $X$’s univariate canonical metric, and the condition on $\delta$ is proved to be necessary. In the non-stationary Gaussian case, when $m=3$, the symmetric (generalized Stratonovich) integral is defined, proved to exist, and its Itô’s formula is established for all functions of class $C^6$.

Détail

Reçu le : 2012-08-09
DOI : 10.1051/ps/2014031

Classification : 60G07, 60G15, 60G48, 60H05
Keywords: Power variation, martingale, calculusvia regularization, Gaussian processes, generalized Stratonovich integral, non-Gaussian processes