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This paper studies the contagion effect within a banking system for the UK and for Germany. In particular, contagion through interbank deposits. Stylized networks are derived from real interbank data, and the theoretical model of Allen and Gale’s 2000 paper is applied. This has shown core banks in both countries to be fragile to unexpected liquidity shocks. Once a liquidity shock is large enough for total demand to exceed the total amount of liquidity in the system, every interbank deposit is withdrawn, and banks are forced to be self-sufficient. Depending on the gravity of the shock, a particular bank can go bust. Depending on the position of the bank going bust, the shock spreads differently. This research can be valuable for policymakers deciding on the optimal number of interbank deposits and connections within a network. Taking into account the total cost one bank run can cause through contagion can drastically change the outcome of profit-maximizing decisions at a high level.

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Scholars have argued how there exists a threshold to the benefits of a more developed financial system, passing which excessive credit growth leads to a fall in output. It is then essential to investigate how credit aggregates relate to macroeconomic factor, and which one of them has the highest predictive power in anticipating potential future crises. To this purpose, the role and predictive power of non-performing loans to total loans ratio (NPLs) were investigated, for two reasons. First, NPLs are an intrinsic measure of the soundness of a country’s financial system; second, the analysis is dedicated to one specific country, Italy, which has collected in its history levels of NPLs among the highest in Europe. A VAR model is performed, with three purposes: 1) to assess the relation between credit aggregates, in the form of credit to households and credit to firms, and economic growth; 2) to analyse the role of NPLs in the system; 3) to investigate the predictive power of NPLs through a forecasting model. Results confirmed the negative relationship between excessive credit growth and RGDP for the corporate sector, found a negative relation between NPLs and RGDP, and showed a better forecasting performance of the model when NPLs are added, though not statistically significant.