The aim of this paper is to provide a new estimator of parameters for LARCH$(\infty)$ processes, and thus also for LARCH$(p)$ or GLARCH$(p,q)$ processes. This estimator results from minimising a contrast leading to a least squares estimator for the absolute values of the process. Strong consistency and asymptotic normality are shown, and convergence occurs at the rate $\sqrt n$ as well in short or long memory cases. Numerical experiments confirm the theoretical results and show that this new estimator significantly outperforms the smoothed quasi-maximum likelihood estimators or weighted least squares estimators commonly used for such processes.

翻译：本文旨在为LARCH$(\infty)$过程（进而也为LARCH$(p)$或GLARCH$(p,q)$过程）提供一种新的参数估计方法。该估计量通过最小化一个对比函数得到，该对比函数对应于过程绝对值的最小二乘估计。本文证明了该估计量的强一致性和渐近正态性，并且在短记忆或长记忆情形下，其收敛速度均达到$\sqrt n$。数值实验验证了理论结果，并表明这种新估计量显著优于此类过程中常用的平滑拟极大似然估计量或加权最小二乘估计量。