Estimation of the linear fractional stable motion

TL;DR

This paper develops methods for estimating parameters of the symmetric linear fractional stable motion, a non-Gaussian process, using power variation and characteristic functions, with proven consistency and limit theorems.

Contribution

It introduces a new parametric inference approach for the symmetric linear fractional stable motion, combining power variation and empirical characteristic functions, with theoretical guarantees.

Findings

Consistent estimators for , , and H are proposed.

Law of large numbers and weak limit theorems are established.

Method performs well in high and low frequency settings.

Abstract

In this paper we investigate the parametric inference for the linear fractional stable motion in high and low frequency setting. The symmetric linear fractional stable motion is a three-parameter family, which constitutes a natural non-Gaussian analogue of the scaled fractional Brownian motion. It is fully characterised by the scaling parameter $\sigma>0$, the self-similarity parameter $H \in (0,1)$ and the stability index $\alpha \in (0,2)$ of the driving stable motion. The parametric estimation of the model is inspired by the limit theory for stationary increments L\'evy moving average processes that has been recently studied in \cite{BLP}. More specifically, we combine (negative) power variation statistics and empirical characteristic functions to obtain consistent estimates of $(\sigma, \alpha, H)$. We present the law of large numbers and some fully feasible weak limit theorems.