Modelling shock propagation and resilience in financial temporal networks

TL;DR

This paper introduces a novel VAR-based framework for modeling shock propagation and resilience in financial temporal networks, focusing on node-based shocks and their effects on network metrics.

Contribution

It develops a nonlinear VAR model with a new estimation method combining MLE and Kalman filter, applied to interbank market data.

Findings

The model captures nonlinear shock effects on network metrics.

Application to e-MID reveals insights into financial network resilience.

The methodology improves understanding of systemic risk propagation.

Abstract

Modelling 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 as starting point the configuration model, a specific Exponential Random Graph model, 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 (e-MID).