Interbank Network Reconstruction Based on Local Clustering Features

Abstract

Accurately reconstructing the interbank network is the primary task of carrying out research on systemic risks in banks. The empirical study found that the minimum density method underestimated the density of the real interbank network and could not reproduce its core-periphery structure. This paper proposes an interbank network reconstruction method that integrates local clustering features. By introducing a local enhancement matrix, the probability matrix of the minimum density method is modified to enhance the connection tendency among core banks. Furthermore, the adaptive factor method is introduced to distribute the weights of the interbank relationship reconstruction process, so as to establish a low-density interbank network with local clustering characteristics. The experimental results on the 2018 annual report data set of 272 Chinese banks show that, compared with the unintegrated interbank network, the interbank network integrated with the local clustering network has increased by 83.9% and 60.1% in terms of density of core bank’s and average clustering coefficient. Moreover, the reconstructed interbank network has empirical network structure characteristics such as sparsity, disassortativity and scale-free properties.