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

Abstract

 In this talk, we will introduce Levy-driven graph supOU processes. Such processes offer a parsimonious parametrisation for high-dimensional time series, where dependencies between the individual components are governed by 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 a European electricity network. This talk is based on joint work with Shreya Mehta (Imperial College London).

Our Speaker

Almut Veraart is Professor and Head of the Statistics Section at Imperial College London, where she also co-directs the Centre for Doctoral Training in Mathematics for our Future Climate. She studied Mathematics at the University of Ulm, Germany, and Applied Statistics at the University of Oxford, where she completed her DPhil in Statistics as a Rhodes Scholar. Before joining Imperial in 2011, she held academic positions at Aarhus University in Denmark. Her research spans mathematical statistics, applied probability, and financial econometrics, with recent work focusing on stochastic volatility, network stochastic processes, ambit stochastics, and causality in extremes, applied to finance, energy, and climate data.