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

By Alexandra Chronopoulou, Ciprian A. Tudor, Frederi G. Viens. 2009

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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 “noncentral limit theorems”. 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-dierence and waveletbased 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.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/52f069076072615bfd54d540012be2345e58c12d

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Statistical inference for Vasicek-type model driven by Hermite processes (2017, background): “A limited number of attempts have been made to cover non-Gaussian noises, notably [6, 23].”