Non-Central Limit Theorem for Quadratic Functionals of Hermite-Driven Long Memory Moving Average Processes
T. T. Diu Tran

TL;DR
This paper proves a non-central limit theorem for quadratic functionals of Hermite-driven long memory processes, showing convergence to the Rosenblatt process under certain conditions, extending previous results and employing advanced spectral analysis techniques.
Contribution
It establishes a non-central limit theorem for quadratic functionals of Hermite-driven processes for q ≥ 2, generalizing prior work and introducing a spectral measure approach.
Findings
Convergence to Rosenblatt process for q ≥ 2
Extension of spectral integral techniques
Complements existing limit theorems for Gaussian case
Abstract
Let denote a Hermite process of order and self-similarity parameter . Consider the Hermite-driven moving average process In the special case of , is the non-stationary Hermite Ornstein-Uhlenbeck process of order . Under suitable integrability conditions on the kernel , we prove that as , the normalized quadratic functional where , converges in the sense of finite-dimensional distribution to the Rosenblatt process of parameter , up to a multiplicative constant, irrespective of self-similarity parameter whenever $q \geq…
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Taxonomy
TopicsFinancial Risk and Volatility Modeling · Stochastic processes and financial applications · Complex Systems and Time Series Analysis
