A0381
Title: Statistical inference for Levy-driven graph supOU processes: From short- to long-memory in high-dimensional time series
Authors: Shreya Mehta - Imperial College London (United Kingdom)
Almut Veraart - Imperial College London (United Kingdom) [presenting]
Abstract: The aim is to introduce Levy-driven graph supOU processes. Such processes offer a parsimonious parametrization for high-dimensional time series, where dependencies between the individual components are governed by a graph structure. Specifically, a model specification is proposed that allows for a smooth transition between short- and long-memory settings while accommodating a wide range of marginal distributions. An inference procedure is further developed based on the generalized method of moments, its asymptotic properties are established, and its strong finite sample performance is demonstrated through a simulation study. Finally, the practical relevance of the new model and estimation method is illustrated in an empirical study of wind capacity factors in a European electricity network.
