Modified weighted power variations of the Hermite process and applications to integrated volatility

TL;DR

This paper investigates the asymptotic behavior of modified weighted power variations of the Hermite process, establishing a central limit theorem and applying it to develop Gaussian estimators for integrated volatility in non-Gaussian models.

Contribution

It extends previous results on quadratic variations to general powers and weighted variations for Hermite processes, with explicit bounds and practical applications.

Findings

Established a CLT for weighted p-variations of Hermite processes.

Constructed asymptotically Gaussian estimators for integrated volatility.

Numerical simulations demonstrate the estimators' practical effectiveness.

Abstract

We study the asymptotic behaviour of modified weighted power variations of the Hermite process of arbitrary order. By selecting suitable "good" increments and exploiting their decomposition into dominant independent components, we establish a central limit theorem for weighted $p$-variations using tools from Stein-Malliavin calculus. Our results extend previous works on modified quadratic and wavelet-based variations to general powers and to weighted settings, with explicit bounds in Wasserstein distance. We further apply these limit theorems to construct asymptotically Gaussian estimators of integrated volatility in Hermite-driven models, thereby extending fBm-based methods to non-Gaussian settings. The last part of our work contains numerical simulations which illustrate the practical performance of the proposed estimators.