Thesis title: Essays on solvency contagion in interbank networks
This thesis investigates the effects of the network configuration on the stability properties of the interbank market.
The first chapter reviews the literature on interbank networks and systemic risk, providing a taxonomy of the main contagion models and describing the debate on the effects of network connectivity patterns on financial stability. Furthermore, it reviews the more recent literature that evaluates the risk of contagion on real interbank networks after the Global Financial Crisis.
The second chapter studies the difference between the level of systemic risk that is empirically measured on an interbank network and the risk that can be deduced from the balance sheets composition of the participating banks. Generalised DebtRank dynamics is used to measure observed systemic risk on e-MID network data (augmented by BankFocus information) and compare it with the expected systemic of a null model network -- obtained through an appropriate maximum-entropy approach constraining relevant balance sheet variables. The chapter shows that the aggregate levels of observed and expected systemic risks are usually compatible but differ significantly during turbulent times -- in our case, after the default of Lehman Brothers (2009) and the VLTRO implementation by the ECB (2012). At the individual level instead, banks are typically more or less risky than what their balance sheet prescribes due to their position in the network.
The last chapter contributes to the literature on the effects of network connectivity on solvency contagion by analysing how the systemic importance and fragility of the individual institutions changes when their degree is modified. This is achieved using the previously described maximum entropy model on the 2007 eMID network data, by transforming the degree sequence in terms of overall link density and heterogeneity of the number of counterparties. The analysis through the DebtRank, Furfine and Eisenberg-Noe models shows that the effects of connectivity on the Impact and Vulnerability of each bank in the system are highly heterogeneous. More specifically, this analysis reveals the existence of a non-linear relationship between the average exposure of a bank and its indices of systemic importance. Furthermore, depending on the model, aggregate and individual connectivity have opposite effects on banks' fragility. Finally, the introduction of the recovery rate reduces the effects of a varying connectivity.
The results of these exercises confirm on one hand that balance sheet information used within a proper maximum-entropy network model provides good systemic risk estimates and on the other hand the importance of knowing the empirical details of the network for conducting precise stress tests of individual banks -- especially after systemic events. Supervisors should pay particular attention to the connectivity of individual banks, as it can affect their importance and vulnerability in the network.