A1513
Title: Estimating points of structural correlation instabilities with latent factor models
Authors: Sojin Lee - CUNEF Universidad (Spain) [presenting]
Abstract: Cross-correlation structures contain valuable information about the underlying linkages among variables and the channels of spillovers across cross-sectional units. Instabilities in these structures often signal structural changes within the system. We propose a novel method for detecting instabilities in cross-correlation structures using a latent factor model framework. We introduce a suitable object, the column space of the loading matrix (the factor space), to capture structural correlation changes while free from the inherent identification issue of the latent model. The resulting detection criterion is based on an intuitive distance measure between two factor spaces, integrating both the detection and localization of breakpoints. The factor space-based framework can also accommodate extension to online early-warning methods. Within this framework, we discuss how classical approaches in factor model analysis coherently connect with the probability of structural change, thereby offering a baseline criterion for sequential early-warning methods. In applications, our methods effectively detect instability points consistent with the development of the subprime mortgage crisis, as well as major policy changes such as the repeal of the Glass-Steagall Act and the U.S.-China trade war.
