Flexible Two-point Selection Approach for Characteristic Function-based Parameter Estimation of Stable Laws

TL;DR

This paper introduces a flexible method for selecting points in characteristic function-based parameter estimation of stable laws, improving practical applicability and outperforming existing methods, with demonstrated success on financial data.

Contribution

A novel point selection technique for characteristic function-based stable law parameter estimation, enhancing accuracy and usability over existing methods.

Findings

Outperforms state-of-the-art methods in parameter estimation.

Effective on real financial asset data.

Provides a practical solution for stable distribution modeling.

Abstract

Stable distribution is one of the attractive models that well describes fat-tail behaviors and scaling phenomena in various scientific fields. The approach based upon the method of moments yields a simple procedure for estimating stable law parameters with the requirement of using momental points for the characteristic function, but the selection of points is only poorly explained and has not been elaborated. We propose a new characteristic function-based approach by introducing a technique of selecting plausible points, which could bring the method of moments available for practical use. Our method outperforms other state-of-art methods that exhibit a closed-form expression of all four parameters of stable laws. Finally, the applicability of the method is illustrated by using several data of financial assets. Numerical results reveal that our approach is advantageous when modeling empirical data with stable distributions.