  Current understanding holds that financial contagion is driven mainly by the
system-wide interconnectedness of institutions. A distinction has been made
between systematic and idiosyncratic channels of contagion, with shocks
transmitted through the latter expected to be substantially more likely to lead
to systemic crisis than through the former. Idiosyncratic connectivity is
thought to be driven not simply by obviously shared characteristics among
institutions, but more by latent characteristics that lead to the holding of
related securities. We develop a graphical model for multivariate financial
time series with interest in uncovering the latent positions of nodes in a
network intended to capture idiosyncratic relationships. We propose a
hierarchical model consisting of a VAR, a covariance graphical model (CGM) and
a latent position model (LPM). The VAR enables us to extract useful information
on the idiosyncratic components, which are used by the CGM to model the network
and the LPM uncovers the spatial position of the nodes. We also develop a
Markov chain Monte Carlo algorithm that iterates between sampling parameters of
the CGM and the LPM, using samples from the latter to update prior information
for covariance graph selection. We show empirically that modeling the
idiosyncratic channel of contagion using our approach can relate latent
institutional features to systemic vulnerabilities prior to a crisis.
