A Generalization of the Ornstein-Uhlenbeck Process: Theoretical Results, Simulations and Parameter Estimation

TL;DR

This paper introduces a broad class of stochastic processes generalizing Ornstein-Uhlenbeck processes, providing theoretical conditions, simulation examples, and estimation methods, with applications to financial and health data.

Contribution

It extends Ornstein-Uhlenbeck processes to include various noise types, establishes their properties, and develops estimation techniques, including Bayesian methods using Fox's H-function.

Findings

Processes driven by Lévy noise are infinitely divisible.

Examples demonstrate properties and autocorrelation functions.

Maximum likelihood and Bayesian estimation methods are effective.

Abstract

In this work, we study the class of stochastic process that generalizes the Ornstein-Uhlenbeck processes, hereafter called by \emph{Generalized Ornstein-Uhlenbeck Type Process} and denoted by GOU type process. We consider them driven by the class of noise processes such as Brownian motion, symmetric $\alpha$-stable L\'evy process, a L\'evy process, and even a Poisson process. We give necessary and sufficient conditions under the memory kernel function for the time-stationary and the Markov properties for these processes. When the GOU type process is driven by a L\'evy noise we prove that it is infinitely divisible showing its generating triplet. Several examples derived from the GOU type process are illustrated showing some of their basic properties as well as some time series realizations. These examples also present their theoretical and empirical autocorrelation or normalized codifference functions depending on whether the process has a finite or infinite second moment. We also present the maximum likelihood estimation as well as the Bayesian estimation procedures for the so-called \emph{Cosine process}, a particular process in the class of GOU type processes. For the Bayesian estimation method, we consider the power series representation of Fox's H-function to better approximate the density function of a random variable $\alpha$-stable distributed. We consider four goodness-of-fit tests for helping to decide which \emph{Cosine process} (driven by a Gaussian or an $\alpha$-stable noise) best fit real data sets. Two applications of GOU type model are presented: one based on the Apple company stock market price data and the other based on the cardiovascular mortality in Los Angeles County data.