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

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 case one-sided estimation method proposed by Forni et al. (2017) data. prove consistency of our estimators impulse response functions as both sample size T dimension n series grow infinity. This approach used in an empirical application order construct measure financial connectedness large panel adjusted intra-day log ranges stocks. show that increases long-run are associated main turmoils. Moreover, we provide evidence significant heterogeneity responses common shocks over different scales, well across industrial sectors.