Variations and Hurst index estimation for a Rosenblatt process using longer filters

Alexandra Chronopoulou (1) , Ciprian A. Tudor (2) , Frederi Viens (1)

1 Department of Statistics [West Lafayette]
2 LPP - Laboratoire Paul Painlevé - UMR 8524

The Rosenblatt process is a self-similar non-Gaussian process which lives in second Wiener chaos, and occurs as the limit of correlated random sequences in so-called \textquotedblleft non-central limit theorems\textquotedblright. It shares the same covariance as fractional Brownian motion. We study the asymptotic distribution of the quadratic variations of the Rosenblatt process based on long filters, including filters based on high-order finite-difference and wavelet-based schemes. We find exact formulas for the limiting distributions, which we then use to devise strongly consistent estimators of the self-similarity parameter $H$. Unlike the case of fractional Brownian motion, no matter now high the filter orders are, the estimators are never asymptotically normal, converging instead in the mean square to the observed value of the Rosenblatt process at time $1$.

Domaines: Probabilités [math.PR]; Statistiques [math.ST]; Théorie [stat.TH]

Alexandra Chronopoulou, Ciprian A. Tudor, Frederi Viens. Variations and Hurst index estimation for a Rosenblatt process using longer filters. Electronic Journal of Statistics , 2009, 3, pp.1393-1435. ⟨hal-00440149⟩