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Information Network Modeling for U.S. Banking Systemic Risk.

Giancarlo Nicola1, Paola Cerchiello1, Tomaso Aste2,3

  • 1Department of Economics and Management, University of Pavia, 27100 Pavia, Italy.

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Summary
This summary is machine-generated.

Information theory measures from bank networks can predict financial stress. This study uses mutual information and transfer entropy to analyze Granger causality with key financial indicators.

Keywords:
financial stressgranger causalitygraphical models

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Area of Science:

  • Quantitative Finance
  • Network Science
  • Information Theory

Background:

  • Financial networks exhibit complex interdependencies.
  • Predicting financial stress is crucial for economic stability.
  • Information theory offers novel tools to analyze complex systems.

Purpose of the Study:

  • To investigate if information theory measures from a bank network Granger cause financial stress indexes.
  • To assess the predictive power of mutual information and transfer entropy for financial stress.
  • To explore the relationship between bank network dynamics and market stability.

Main Methods:

  • Construction of a Gaussian Graphical Model from daily stock time series of the top 74 US banks.
  • Utilizing the LoGo algorithm for fast daily updates of the graphical model.
  • Extraction of daily time series for mutual information and transfer entropy for each bank.
  • Application of standard and Partial Granger-causality tests, conditioned on economic controls.

Main Results:

  • Information theory measures derived from the bank network demonstrate Granger causality with financial stress indexes.
  • Specific patterns in mutual information and transfer entropy precede increases in LIBOR-OIS spread, STLFSI, and USD/CHF exchange rate fluctuations.
  • Partial Granger-causality reveals significant predictive relationships even after controlling for general economic conditions.

Conclusions:

  • Information theory measures from bank networks serve as leading indicators of financial stress.
  • The dynamic analysis of bank interdependencies using information theory provides valuable insights into systemic risk.
  • This approach offers a novel method for early detection and monitoring of financial instability.