Time-Varying General Dynamic Factor Models and the Measurement of Financial Connectedness

48 Pages Posted: 14 Feb 2019 Last revised: 24 Apr 2020

See all articles by Matteo Barigozzi

Matteo Barigozzi

University of Bologna

Marc Hallin

ECARES, Universite Libre de Bruxelles

Stefano Soccorsi

Department of Economics, Lancaster University Management School

Rainer von Sachs

Catholic University of Louvain - Department of Statistics

Date Written: April 19, 2020

Abstract

We propose a new time-varying Generalized Dynamic Factor Model for high-dimensional, locally stationary time series. Estimation is based on dynamic principal component analysis jointly with singular VAR estimation, and extends to the locally stationary case the one-sided estimation method proposed by Forni et al. (2017) for stationary data. We prove consistency of our estimators of time-varying impulse response functions as both the sample size T and the dimension n of the time series grow to infinity. This approach is used in an empirical application in order to construct a time-varying measure of financial connectedness for a large panel of adjusted intra-day log ranges of stocks. We show that large increases in long-run connectedness are associated with the main financial turmoils. Moreover, we provide evidence of a significant heterogeneity in the dynamic responses to common shocks in time and over different scales, as well as across industrial sectors.

Keywords: locally stationary dynamic factor models, volatility, financial connectedness

JEL Classification: C32, C14

Suggested Citation

Barigozzi, Matteo and Hallin, Marc and Soccorsi, Stefano and von Sachs, Rainer, Time-Varying General Dynamic Factor Models and the Measurement of Financial Connectedness (April 19, 2020). Available at SSRN: https://ssrn.com/abstract=3329445 or http://dx.doi.org/10.2139/ssrn.3329445

Matteo Barigozzi (Contact Author)

University of Bologna ( email )

Piazza Scaravilli 2
Bologna, 40100
Italy

Marc Hallin

ECARES, Universite Libre de Bruxelles ( email )

Ave. Franklin D Roosevelt, 50 - C.P. 114
Brussels, B-1050
Belgium
+32 2 650 5886 (Phone)
+32 2 650 5899 (Fax)

Stefano Soccorsi

Department of Economics, Lancaster University Management School ( email )

Lancaster, LA1 4YX
United Kingdom

Rainer Von Sachs

Catholic University of Louvain - Department of Statistics ( email )

Voie du Roman Pay
34 B-1348 Louvain-La-Neuve, 1348
Belgium

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
177
Abstract Views
1,074
rank
184,096
PlumX Metrics