This article introduces Levy-driven graph supOU processes, a parsimonious parametrisation for high-dimensional time series in which dependence between components is governed by a graph structure. Specifically, the model bridges short- and long-range dependence within a single parametric family while accommodating a wide range of marginal distributions. We further develop a generalised method of moments estimator, establish its consistency and asymptotic normality, and assess its finite-sample performance through a simulation study. Finally, we illustrate the practical relevance of our model and estimation method in an empirical study of wind capacity factors in a European electricity network context.

翻译：本文引入Levy驱动的图supOU过程，为高维时间序列提供了一种简约的参数化建模框架，其中各分量间的相依性由图结构所决定。具体而言，该模型在单一参数族内同时刻画了短程与长程相依性，并能兼容多种边际分布。我们进一步提出了广义矩估计方法，证明了估计量的一致性与渐近正态性，并通过模拟研究评估了其有限样本表现。最后，我们以欧洲电力网络中风能容量因子的实证研究为例，说明了所提模型与估计方法的实际应用价值。