A1909
Title: Understanding fluctuations through multivariate circulant singular spectrum analysis
Authors: Pilar Poncela - Universidad Autonoma de Madrid (Spain) [presenting]
Eva Senra - Universidad de Alcala (Spain)
Juan Bogalo - University of Alcala (Spain)
Abstract: Multivariate circulant singular spectrum analysis (M-CiSSA) is introduced to provide a comprehensive framework to analyze fluctuations, extracting the underlying components of a set of time series, disentangling their sources of variation and assessing their relative phase or cyclical position at each frequency. The novel method is non-parametric and can be applied to series out of phase, highly nonlinear and modulated both in frequency and amplitude. A uniqueness theorem is proven that in the case of common information and without the need to fit a factor model, allows the identification of common sources of variation. This technique can be quite useful in several fields such as climatology, biometrics, engineering and economics among others. It shows the performance of M-CiSSA through a synthetic example of latent signals modulated both in amplitude and frequency and through the real data analysis of energy prices to understand the main drivers and co-movements of primary energy commodity prices at various frequencies that are key to assessing energy policy at different time horizons.
