Year: 2021
Type: article
Abstract: One of the important and widely used classes of models for non‐Gaussian time series is the generalized autoregressive model average models (GARMA), which specifies an ARMA structure for the conditional mean process of the underlying time series. However, in many applications one often encounters conditional heteroskedasticity. In this article, we propose a new class of models, referred to as GARMA‐GARCH models, that jointly specify both the conditional mean and conditional variance processes of a general non‐Gaussian time series. Under the general modeling framework, we propose three specific models, as examples, for proportional time series, non‐negative time series, and skewed and heavy‐tailed financial time series. Maximum likelihood estimator (MLE) and quasi Gaussian MLE are used to estimate the parameters. Simulation studies and three applications are used to demonstrate the properties of the models and the estimation procedures.
Source: Journal of Time Series Analysis
DOI: 10.1111/jtsa.12602
Authors: Tingguo Zheng, Han Xiao, Rong Chen
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W3161419270