Copula-Based Time Series for Non-Gaussian and Non-Markovian Stationary Processes
Sven Pappert Harry Joe
作者信息
Abstract
In the copula-based approach to univariate time series modeling, the finite-dimensional temporal dependence of a stationary time series is captured by a copula. Recent studies investigate how copula-based time series models can be generalized to have long-term autoregressive effects. We study a generalization that comes from a Markov sequence of order p and a q-dependent sequence. We derive the relation of the model to Gaussian-ARMA models and to the Gaussian-GARCH(1,1) model. We investigate distributional properties of the process and discuss the maximum likelihood estimation (MLE). Additionally we analyze the copula moving aggregate process of order one, or MAG(1), as it is a basic building block. Last, we assess the model in a probabilistic forecasting study on a data series for German wind power production.引用本文复制引用
Sven Pappert,Harry Joe.Copula-Based Time Series for Non-Gaussian and Non-Markovian Stationary Processes[EB/OL].(2026-10-19)[2026-10-31].https://arxiv.org/abs/2604.01500.学科分类
数学
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