Filtering for Risk Assessment of Interbank Network

Abstract

We develop a framework for risk assessment in an interbank network, where banks interact with each other via short-term debt contracts. Focusing on the demand side of liquidity and omitting credit rationing, the framework identifies the interbank network structure and its degree of interconnectedness. Identification is facilitated by a statistical learning procedure that reverse engineers signals (transactions) observed in the interbank market and conducts inference about banks’ individual and network-level characteristics. Performing a series of simulation studies after banking network identification, we measure the interconnectedness of the network with respect to its resilience to absorb systemic risk. The findings from the simulations imply that the benefits of integration outweigh the cost of interconnectedness as long as the interconnectedness technology is hedge serving (risk diversifying). If it is not the case, the simulation risk metrics we compute increase exponentially with the level of network interconnectedness. This, as a result, undermines the value of integration, even when the network is identified in its simplest forms.