  We revisit the problem of estimating high-dimensional global bank network
connectedness. Instead of directly regularizing the high-dimensional vector of
realized volatilities as in Demirer et al. (2018), we estimate a dynamic factor
model with sparse VAR idiosyncratic components. This allows to disentangle: (I)
the part of system-wide connectedness (SWC) due to the common component shocks
(what we call the "banking market"), and (II) the part due to the idiosyncratic
shocks (the single banks). We employ both the original dataset as in Demirer et
al. (2018) (daily data, 2003-2013), as well as a more recent vintage
(2014-2023). For both, we compute SWC due to (I), (II), (I+II) and provide
bootstrap confidence bands. In accordance with the literature, we find SWC to
spike during global crises. However, our method minimizes the risk of SWC
underestimation in high-dimensional datasets where episodes of systemic risk
can be both pervasive and idiosyncratic. In fact, we are able to disentangle
how in normal times $\approx$60-80% of SWC is due to idiosyncratic variation
and only $\approx$20-40% to market variation. However, in crises periods such
as the 2008 financial crisis and the Covid19 outbreak in 2019, the situation is
completely reversed: SWC is comparatively more driven by a market dynamic and
less by an idiosyncratic one.
