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Related Experiment Video

Updated: Oct 7, 2025

An R-Based Landscape Validation of a Competing Risk Model
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Risk management via contemporaneous and temporal dependence structures with applications.

Emmanuel Senyo Fianu1,2, Daniel Felix Ahelegbey3, Luigi Grossi4

  • 1Mainz University of Applied Sciences, School of Business, Lucy-Hillebrand-Str. 2, Mainz, 55128 Germany.

Methodsx
|January 10, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces Bayesian Graphical Vector Auto-regression (BG-VAR) and Bayesian Graphical Systems Equation Modelling (BG-SEM) to analyze risk networks in time series data. These models reveal risk transmitters and recipients, enhancing understanding of financial risk contagion.

Keywords:
Complex networks price volatilityMultivariate time seriesOR in marketsSystemic risk

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Area of Science:

  • Econometrics
  • Network Analysis
  • Time Series Analysis

Background:

  • Multivariate time series data often contain complex interdependencies.
  • Understanding risk propagation and contagion is crucial in financial and economic systems.
  • Existing models may not fully capture the dynamic network structures within time series.

Purpose of the Study:

  • To develop and present estimation methods for Bayesian Graphical Vector Auto-regression (BG-VAR(X)) and Bayesian Graphical Systems Equation Modelling (BG-SEM(X)).
  • To examine risk network structures embedded in multivariate time series data.
  • To analyze risk propagation dynamics and persistence using complex network models.

Main Methods:

  • Estimation of BG-VAR(X) and BG-SEM(X) models, with and without exogenous variables.
  • Application of network models to identify within-day and across-day risk transmitters and recipients.
  • Comparative analysis of models with and without exogenous variables to assess their impact on network structure.

Main Results:

  • Both BG-VARX and BG-SEM(X) effectively reveal major risk transmitters and recipients within multivariate time series.
  • Models incorporating exogenous variables (BG-VARX and BG-SEM(X)) generate richer network structures compared to those without.
  • The methods allow for the estimation of intra-day and inter-day interconnections and their dynamics.

Conclusions:

  • The developed Bayesian graphical models provide a robust framework for analyzing risk networks in multivariate time series.
  • Exogenous variables play a significant role in depicting influential network structures and risk propagation.
  • This approach offers a platform for future research, including extensions to diverse data types and policy implications.