The Effect of the Interbank Network Structure on Contagion and Common Shocks

'The Devil is in the Details, But so is Salvation': Different Approaches in Money Market Measurement

“…In most cases, survey data are not available as granular, transaction-level data, but are reported as aggregate figures. Transaction-level data are important as the microstructure of interbank markets should be taken into account when designing policy measures, as shown for example by Georg (2011). This is especially true in times of stress.…”

Reinforcement Learning Policy Recommendation for Interbank Network Stability

“…Firstly, the literature has made an effort to understand and include in the economic models the sources of contagion. Regardless of the modeling approach used, which ranges from New Keynesian models solved globally or using reduced functional form (see, for instance, Boissay et al (2016), Gertler et al (2020), Svensson (2017)) to agentbased models and the most recent network-oriented approaches (see Battiston et al (2012a,b), Georg (2013), Haldane and May (2011), Upper (2011), Capponi et al (2020, Calice et al (2020)), there is a general agreement that identifies interaction and heterogeneity as the drivers of endogenous crises. Moreover, the post-Lehman studies have placed particular emphasis on the propagation of contagion, determining the direction of the attack from financial to real markets and its fuse in the portfolio structure of financial institutions (see Brunnermeier et al (2012)).…”

Contagion in economic networks: a data-driven machine learning approach

“…Still in the agent-based modeling, Georg (2013) and Bluhm, Faia and Krahnen (2014) share similarities with our research, as they analyze the central bank as a liquidity provider in an interbank network. Liquidity provision makes banks more resilient to shocks.…”

“…For instance, scale-free networks are the most fragile when markets are illiquid. Georg (2013) finds that, while in normal times different network topologies have a similar performance regarding systemic risk, the network topology becomes quite important in times of crisis. In these circumstances, contagion seems to be stronger in random networks, whereas scale-free networks are more resilient.…”

“…But only a few financial network models have incorporated monetary policy issues to their analytical framework. In Georg (2013) and Bluhm, Faia and Krahnen (2014), for instance, the central bank provides liquidity in the interbank market. Meanwhile, Silva et al (2020) estimate both direct and indirect impacts of monetary policy shocks (as measured by changes in the policy interest rate) on the economy by applying a multi-layer network model to a unique Brazilian data set.…”