Time-Varying General Dynamic Factor Models and the Measurement of Financial Connectedness
46 Pages Posted: 14 Feb 2019 Last revised: 9 Dec 2019
Date Written: December 9, 2019
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: Suggested Citation