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Information network modeling for U.S. banking systemic risk

Nicola, Giancarlo, Cerchiello, Paola and Aste, Tomaso (2020) Information network modeling for U.S. banking systemic risk. Entropy, 22 (11). ISSN 1099-4300

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Identification Number: 10.3390/e22111331

Abstract

In this work we investigate whether information theory measures like mutual information and transfer entropy, extracted from a bank network, Granger cause financial stress indexes like LIBOR-OIS (London Interbank Offered Rate-Overnight Index Swap) spread, STLFSI (St. Louis Fed Financial Stress Index) and USD/CHF (USA Dollar/Swiss Franc) exchange rate. The information theory measures are extracted from a Gaussian Graphical Model constructed from daily stock time series of the top 74 listed US banks. The graphical model is calculated with a recently developed algorithm (LoGo) which provides very fast inference model that allows us to update the graphical model each market day. We therefore can generate daily time series of mutual information and transfer entropy for each bank of the network. The Granger causality between the bank related measures and the financial stress indexes is investigated with both standard Granger-causality and Partial Granger-causality conditioned on control measures representative of the general economy conditions.

Item Type: Article
Official URL: https://www.mdpi.com/journal/entropy
Additional Information: © 2020 The Authors
Divisions: Systemic Risk Centre
Subjects: H Social Sciences > HG Finance
Date Deposited: 03 Dec 2020 16:03
Last Modified: 20 Jan 2021 07:14
URI: http://eprints.lse.ac.uk/id/eprint/107563

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