Time-Varying General Dynamic Factor Models and the Measurement of Financial Connectedness
48 Pages Posted: 14 Feb 2019 Last revised: 24 Apr 2020
Date Written: April 19, 2020
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
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