Modeling shock propagation and resilience in financial temporal networks

Modeling how a shock propagates in a temporal network and how the system relaxes back to equilibrium is challenging but important in many applications, such as financial systemic risk. Most studies, so far, have focused on shocks hitting a link of the network, while often it is the node and its propensity to be connected that are affected by a shock. Using the configuration model—a specific exponential random graph model—as a starting point, we propose a vector autoregressive (VAR) framework to analytically compute the Impulse Response Function (IRF) of a network metric conditional to a shock on a node. Unlike the standard VAR, the model is a nonlinear function of the shock size and the IRF depends on the state of the network at the shock time. We propose a novel econometric estimation method that combines the maximum likelihood estimation and Kalman filter to estimate the dynamics of the latent parameters and compute the IRF, and we apply the proposed methodology to the dynamical network describing the electronic market of interbank deposit.