Statistical inference for Levy-driven graph supOU processes: From short- to long-memory in high-dimensional time series

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.