Time-Varying General Dynamic Factor Models and the Measurement of Financial Connectedness
(2021) Journal of Econometrics — Vol. 222, n° 1, p. 324-343 (2021)
Files
ISBADP2019_24_vonsachs_Time-VaryingGeneralDynamicFactorModelsandtheMeasurementofFinancialConnectedness_dial.pdf
Open Access - Adobe PDF
- 1.97 MB
Details
- Authors
- 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.
- Affiliations
Citations
Barigozzi, M., Hallin, M., Soccorsi, S., & von Sachs, R. (2021). Time-Varying General Dynamic Factor Models and the Measurement of Financial Connectedness. Journal of Econometrics, 222(1), 324-343. https://doi.org/10.1016/j.jeconom.2020.07.004 (Original work published 2021)