Statistical physicists are recently focusing on the network structure of financial systems in order to model cascading defaults in those systems. Active research is on the characteristics of...Show moreStatistical physicists are recently focusing on the network structure of financial systems in order to model cascading defaults in those systems. Active research is on the characteristics of financial networks. These networks are inferred from statistical ensembles constructed from partial information, due to the fact that researchers usually don’t have full access to the entire network underlying the financial system, because of privacy reasons. Other research simulates the propagation of shocks such as defaults through financial systems and focuses on the characteristics of the network governing the dynamics. This research combines the two areas of research using a unique data set containing all transactions of commercial Dutch ING accounts from the year 2019. A state-of-the-art random network ensemble is tested in the research, whereafter networks are sampled from this ensemble to run the cascading defaults simulation on. Furthermore, the simulation of cascading defaults is improved by implementing non-trivial payment strategies, motivated by experiences from bankers dealing with defaulted companies. The simulation using the empirical network yields similar results to the networks sampled from the ensemble, indicating that the random network ensemble captures information governing the dynamics.Show less
Many systems can be studied by representing them as multilayer networks. Multilayer networks are mathematical structures consisting of a set of objects with multiple kinds of possible relations...Show moreMany systems can be studied by representing them as multilayer networks. Multilayer networks are mathematical structures consisting of a set of objects with multiple kinds of possible relations between the objects. The study of various multilayer networks such as the network of international trade using statistical models with the assumption that the layers are independent leads to the observed overlap in the network being significantly different from the overlap predicted by the model. In an attempt to solve this issue we introduced interdependencies between the layers of a multilayer network in the model by explicitly including the overlap in our model. We then analytically derived the maximum likelihood equations for this model. Using numerical methods, we tested the validity of these obtained equations which proved to be highly accurate when compared to the numerical simulation data. Additionally, we have shown that the overlap of a network for a given number of links can be increased by either increasing the heterogeneity of the network or the value of the coupling parameter that we have introduced into the model. This allows us to create multiplexes with a specific amount of overlap for a given of number of links, given that it is within the theoretical limits. Finally, we conclude that the empirical overlap in the network of international trade is not merely a result of the correlation between the degrees of the different layers but requires a nonzero coupling between the layers in its modeling.Show less
