Time-varying general dynamic factor models and the measurement of financial connectedness

Matteo Barigozzi, Marc Hallin, Stefano Soccorsi and Rainer von Sachs

Journal of Econometrics, 2021, vol. 222, issue 1, 324-343

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 (search for similar items in EconPapers)
JEL-codes: C14 C32 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

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Related works:
Working Paper: Time-varying general dynamic factor models and the measurement of financial connectedness (2020)
Working Paper: Time-Varying General Dynamic Factor Models and the Measurement of Financial Connectedness (2019)
Working Paper: Time-Varying General Dynamic Factor Models and the Measurement of Financial Connectedness (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:222:y:2021:i:1:p:324-343

DOI: 10.1016/j.jeconom.2020.07.004

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