Stationary GE-Process and its Application in Analyzing Gold Price Data

TL;DR

This paper introduces a new stationary GE-process with three parameters, providing a flexible model for analyzing gold price data and other applications, with methods for parameter estimation and real data validation.

Contribution

A novel three-parameter GE-process is developed, characterized, and applied to gold price data, enhancing modeling flexibility over existing exponential processes.

Findings

The GE-process effectively models gold price data.

Maximum likelihood estimators are derived for different parameter scenarios.

The model shows good fit on synthetic and real data.

Abstract

In this paper we introduce a new discrete time and continuous state space stationary process $\{X_n; n = 1, 2, \ldots \}$, such that $X_n$ follows a two-parameter generalized exponential (GE) distribution. Joint distribution functions, characterization and some dependency properties of this new process have been investigated. The GE-process has three unknown parameters, two shape parameters and one scale parameter, and due to this reason it is more flexible than the existing exponential process. In presence of the scale parameter, if the two shape parameters are equal, then the maximum likelihood estimators of the unknown parameters can be obtained by solving one non-linear equation and if the two shape parameters are arbitrary, then the maximum likelihood estimators can be obtained by solving a two dimensional optimization problem. Two {\color{black} synthetic} data sets, and one real gold-price data set have been analyzed to see the performance of the proposed model in practice. Finally some generalizations have been indicated.