Adjustable network reconstruction with applications to CDS exposures

This paper is concerned with reconstructing weighted directed networks from the total in- and out-weight of each node. This problem arises for example in the analysis of systemic risk of partially observed financial networks. Typically a wide range of networks is consistent with this partial information. We develop an empirical Bayesian methodology that can be adjusted such that the resulting networks are consistent with the observations and satisfy certain desired global topological properties such as a given mean density, extending the approach by Gandy and Veraart (2017). Furthermore we propose a new fitness-based model within this framework. We provide a case study based on a data set consisting of 89 fully observed financial networks of credit default swap exposures. We reconstruct those networks based on only partial information using the newly proposed as well as existing methods. To assess the quality of the reconstruction, we use a wide range of criteria, including measures on how well the degree distribution can be captured and higher order measures of systemic risk. We find that the empirical Bayesian approach performs best.