An empirical likelihood approach for symmetric $\alpha$-stable processes

TL;DR

This paper extends empirical likelihood methods to symmetric $oldsymbol{ extalpha}$-stable processes with heavy tails, providing new tools for statistical inference in fields like finance where such data are common.

Contribution

It introduces a novel empirical likelihood ratio approach for symmetric $ extalpha$-stable linear processes, deriving its asymptotic distribution and enabling inference for heavy-tailed data.

Findings

Derived the asymptotic distribution of the empirical likelihood ratio statistic for $ extalpha$-stable processes

Extended empirical likelihood theory to heavy-tailed, infinite variance data

Applicable to financial data analysis involving heavy-tailed distributions

Abstract

Empirical likelihood approach is one of non-parametric statistical methods, which is applied to the hypothesis testing or construction of confidence regions for pivotal unknown quantities. This method has been applied to the case of independent identically distributed random variables and second order stationary processes. In recent years, we observe heavy-tailed data in many fields. To model such data suitably, we consider symmetric scalar and multivariate $\alpha$-stable linear processes generated by infinite variance innovation sequence. We use a Whittle likelihood type estimating function in the empirical likelihood ratio function and derive the asymptotic distribution of the empirical likelihood ratio statistic for $\alpha$-stable linear processes. With the empirical likelihood statistic approach, the theory of estimation and testing for second order stationary processes is nicely extended to heavy-tailed data analyses, not straightforward, and applicable to a lot of financial statistical analyses.