Distributional properties and parameters estimation of GSB Process: An approach based on characteristic functions

TL;DR

This paper studies the Gaussian Split-BREAK (GSB) process, deriving its properties, proposing a parameter estimation method based on the empirical characteristic function, and demonstrating its effectiveness through simulations and real stock market data.

Contribution

It introduces a new parameter estimation procedure for the GSB process using the empirical characteristic function, improving over existing methods.

Findings

The characteristic function of the GSB process is derived.

The proposed ECF-based estimation method performs well in simulations.

Application to stock market data illustrates practical utility.

Abstract

A general type of a Split-BREAK process with Gaussian innovations (henceforth, Gaussian Split-BREAK or GSB process) is considered. The basic stochastic properties of the model are studied and its characteristic function derived. A procedure to estimate the parameter of the GSB model based on the Empirical Characteristic Function (ECF) is proposed. Our simulations suggest that the proposed method performs well compared to a Method of Moment procedure used as benchmark. The empirical use of the GSB model is illustrated with an application to the time series of total values of shares traded at Belgrade Stock Exchange.