In Search of Lost Edges: A Case Study on Reconstructing Financial Networks

by   Michael Lebacher, et al.

To capture the systemic complexity of international financial systems, network data is an important prerequisite. However, dyadic data is often not available, raising the need for methods that allow for reconstructing networks based on limited information. In this paper, we are reviewing different methods that are designed for the estimation of matrices from their marginals and potentially exogenous information. This includes a general discussion of the available methodology that provides edge probabilities as well as models that are focussed on the reconstruction of edge values. Besides summarizing the advantages, shortfalls and computational issues of the approaches, we put them into a competitive comparison using the SWIFT (Society for Worldwide Interbank Financial Telecommunication) MT 103 payment messages network (MT 103: Single Customer Credit Transfer). This network is not only economically meaningful but also fully observed which allows for an extensive competitive horse race of methods. The comparison concerning the binary reconstruction is divided into an evaluation of the edge probabilities and the quality of the reconstructed degree structures. Furthermore, the accuracy of the predicted edge values is investigated. To test the methods on different topologies, the application is split into two parts. The first part considers the full MT 103 network, being an illustration for the reconstruction of large, sparse financial networks. The second part is concerned with reconstructing a subset of the full network, representing a dense medium-sized network. Regarding substantial outcomes, it can be found that no method is superior in every respect and that the preferred model choice highly depends on the goal of the analysis, the presumed network structure and the availability of exogenous information.


page 29

page 30

page 35

page 36

page 37

page 38

page 39

page 40


Evaluating structural edge importance in temporal networks

To monitor risk in temporal financial networks, we need to understand ho...

Reconstructing Sparse Illicit Supply Networks: A Case Study of Multiplex Drug Trafficking Networks

The network structure provides critical information for law enforcement ...

Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages

Research in NLP lacks geographic diversity, and the question of how NLP ...

Application of Deep Neural Networks to assess corporate Credit Rating

Recent literature implements machine learning techniques to assess corpo...

Reconstructing dynamical networks via feature ranking

Empirical data on real complex systems are becoming increasingly availab...

Simulating an Object-Oriented Financial System in a Functional Language

This paper summarises a successful application of functional programming...

Learning Conserved Networks from Flows

The network reconstruction problem is one of the challenging problems in...

Please sign up or login with your details

Forgot password? Click here to reset