A Matrix-Variate t Model for Networks
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 estimat...
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| Published in: | Frontiers in artificial intelligence Vol. 4; p. 674166 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Switzerland
Frontiers Media S.A
13-05-2021
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| Subjects: | |
| Online Access: | Get full text |
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| Summary: | 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
distribution and a Bayesian inference procedure to de-noise the data. We provide an application to a network among European financial institutions. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 This article was submitted to Artificial Intelligence in Finance, a section of the journal Frontiers in Artificial Intelligence Edited by: Joerg Osterrieder, Zurich University of Applied Sciences, Switzerland Reviewed by: Andriette Bekker, University of Pretoria, South Africa; Bertrand Kian Hassani, University College London, United Kingdom |
| ISSN: | 2624-8212 2624-8212 |
| DOI: | 10.3389/frai.2021.674166 |