Bayesian inference of financial networks

Article type: Research Article

Authors: Sosa, Juan^{a; *} | Betancourt, Brenda^b

Affiliations: [a] Universidad Nacional de Colombia, Bogota, Colombia | [b] Department of Statistics and Data Science, NORC at the University of Chicago, Chicago, IL, USA

Correspondence: [*] Corresponding author: Juan Sosa, Universidad Nacional de Colombia, Bogota, Colombia. E-mail: [email protected].

Abstract: Network data arises naturally in a wide variety of applications in different fields. In this article we discuss in detail the statistical modeling of financial networks. The structure of such networks red has not been studied thoroughly in the past, mainly due to limited accessible data. We explore the structure of a real trading network corresponding to transactions within the natural gas future market over a four-year period. The detection of meaningful communities of actors within networks is particularly relevant to understand the topology of a complex system like this. We explore the usage of stochastic block models in conjunction with a nonparametric Bayesian approach in order to identify clusters of traders in a flexible modeling framework. Our findings strongly indicate that the proposed models are highly reliable at detecting community structures.

Keywords: Bayesian inference, community structure, stochastic block models, trading networks

DOI: 10.3233/MAS-231456

Journal: Model Assisted Statistics and Applications, vol. 18, no. 4, pp. 295-310, 2023