This article introduces Levy-driven graph supOU processes, offering a parsimonious parametrisation for high-dimensional time-series, where dependencies between the individual components are governed via a graph structure. Specifically, we propose a model specification that allows for a smooth transition between short- and long-memory settings while accommodating a wide range of marginal distributions. We further develop an inference procedure based on the generalised method of moments, establish its asymptotic properties and demonstrate its strong finite sample performance through a simulation study. Finally, we illustrate the practical relevance of our new model and estimation method in an empirical study of wind capacity factors in an European electricity network context.

翻译：本文引入了基于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，其中各分量间的依赖关系通过图结构进行刻画。具体而言，我们提出的模型设定允许在短记忆与长记忆设置之间实现平滑过渡，同时能够适应广泛的边缘分布类型。我们进一步开发了基于广义矩估计的推断方法，建立了其渐近性质，并通过模拟研究证明了该方法在有限样本下的优异性能。最后，我们通过对欧洲电网中风能容量因子的实证研究，阐明了新模型与估计方法的实际应用价值。