The main difficulty of the examination of systemic importance is always its huge data need. From this point of view, core-periphery decomposition is an easier method because we only need the exposure of the banks on the interbank market. Although in many cases this may also result in some difficulty since direct linkages between banks are often unknown. However, in the literature, we can find some good solutions to fill these gaps, for example, the minimum density approach by Anand et al. (2014). Alternatively, there are some other suggestions on how to create a network from market data (for example, Billio et al., 2013).

Due to the differences between the two methods, the results can be confusing. We will give you some ideas on how to interpret the results. The core-periphery decomposition focuses only on one market. It implies that being in the core means that the bank is important on this market. The importance for the whole banking system then depends on the importance of this market. Without this information, we might only say that the core banks are important for the operation of the market.

On the contrary, the simulation method strictly focuses on the banking system's stability. As a result, we get those banks that may trigger a severe crisis. However, it doesn't mean that other banks won't have a crucial effect on the operation of the interbank market. A bank that has well-capitalized partners may freeze the market without jeopardizing the stability of the whole banking system. On the longer horizon, the lack of a well-functioning market will result in ineffective liquidity management.