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.
A Matrix-Variate t Model for Networks / Billio, M.; Casarin, R.; Costola, M.; Iacopini, Matteo. - In: FRONTIERS IN ARTIFICIAL INTELLIGENCE. - ISSN 2624-8212. - 4:(2021), pp. 1-7. [10.3389/frai.2021.674166]
A Matrix-Variate t Model for Networks
Iacopini M.
2021
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.File | Dimensione | Formato | |
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