We develop the necessary methodology to conduct principal component analysis at high
frequency. We construct estimators of realized eigenvalues, eigenvectors, and principal …

Dynamic factor models are parsimonious representations of relationships among time series
variables. With the surge in data availability, they have proven to be indispensable in …

Second‐order source separation (SOS) is a data analysis tool which can be used for
revealing hidden structures in multivariate time series data or as a tool for dimension …

Global inflation has surged to 7.5 percent in August 2022, from an average of 2.1 percent in
the decade preceding the COVID-19 pandemic, threatening to become an entrenched …

With a systematic approach, this study investigates the roles of the peak-valley prices and
idiosyncratic factors in the information spillover mechanism between the electricity and …

This paper proposes a new network topology approach to identify the contemporaneous and
noncontemporaneous idiosyncratic spillovers of lower‐moment and higher‐moment risks in …

This article considers the estimation and inference of the low-rank components in high-
dimensional matrix-variate factor models, where each dimension of the matrix-variates (p× …

In this article, which consists of two parts (Part I: representation results; Part II: estimation and
forecasting methods), we set up the theoretical foundations for a high‐dimensional …

Interconnectedness between stocks and firms plays a crucial role in the volatility contagion
phenomena that characterize financial crises, and graphs are a natural tool in their analysis …

Abstract High-Dimensional Dynamic Factor Models are presented in detail: The main
assumptions and their motivation, main results, illustrations by means of elementary …