A Matrix-Variate t Model for Networks

Monica Billio*, Roberto Casarin, Michele Costola, Matteo Iacopini

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Networks represent a useful tool to describe relationships among financial firms and network analysis has been extensively used in recent years to study financial connectedness. An aspect, which is often neglected, is that network observations come with errors from different sources, such as estimation and measurement errors, thus a proper statistical treatment of the data is needed before network analysis can be performed. We show that node centrality measures can be heavily affected by random errors and propose a flexible model based on the matrix-variate t distribution and a Bayesian inference procedure to de-noise the data. We provide an application to a network among European financial institutions.

Original languageEnglish
Article number674166
Pages (from-to)1-7
Number of pages7
JournalFrontiers in Artificial Intelligence
Volume4
Issue numberMay
Early online date13 May 2021
DOIs
Publication statusPublished - May 2021

Bibliographical note

Funding Information:
We thank Maurizio La Mastra for excellent research assistance. This research used the SCSCF and the HPC multiprocessor cluster systems provided by the Venice Centre for Risk Analytics (VERA) at the University Ca’ Foscari of Venice. MB, RC, and MC acknowledge financial support from the Italian Ministry MIUR under the PRIN project Hi-Di NET – Econometric Analysis of High Dimensional Models with Network Structures in Macroeconomics and Finance (grant agreement no. 2017TA7TYC). MI acknowledges financial support from the Marie Skłodowska-Curie Actions, European

Funding Information:
We thank Maurizio La Mastra for excellent research assistance. This research used the SCSCF and the HPC multiprocessor cluster systems provided by the Venice Centre for Risk Analytics (VERA) at the University Ca' Foscari of Venice. MB, RC, and MC acknowledge financial support from the Italian Ministry MIUR under the PRIN project Hi-Di NET ? Econometric Analysis of High Dimensional Models with Network Structures in Macroeconomics and Finance (grant agreement no. 2017TA7TYC). MI acknowledges financial support from the Marie Sk?odowska-Curie Actions, European Union, Seventh Framework Program HORIZON 2020 under REA grant agreement no. 887220.

Publisher Copyright:
© Copyright © 2021 Billio, Casarin, Costola and Iacopini.

Keywords

  • 62F15
  • 62M10
  • 65C05
  • Bayesian
  • C11
  • C32
  • C58
  • financial markets
  • matrix-variate distributions
  • networks
  • t distribution

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