Risk management via contemporaneous and temporal dependence structures with applications

This paper presents the estimation methods of the Bayesian Graphical Vector Auto-regression with and without innovations such as external regressors (BG-VAR(X)) and Bayesian Graphical Systems Equation Modelling with and without exogenous variables (BG-SEM(X)), which are developed to examine risk net...

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Autores principales:	Emmanuel Senyo Fianu, Daniel Felix Ahelegbey, Luigi Grossi
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Methods for Risk analysis, and Model performance using Bayesian Graphical Network Vector Autoregression with Exogenous Variables (BG-VARX) and System Equation Modelling (BG-SEM) with real world applications. Science Q
Acceso en línea:	https://doaj.org/article/012c039f75784c16b7647ce2ca8ded1f
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spelling	oai:doaj.org-article:012c039f75784c16b7647ce2ca8ded1f2021-11-26T04:30:12ZRisk management via contemporaneous and temporal dependence structures with applications2215-016110.1016/j.mex.2021.101587https://doaj.org/article/012c039f75784c16b7647ce2ca8ded1f2021-01-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2215016121003770https://doaj.org/toc/2215-0161This paper presents the estimation methods of the Bayesian Graphical Vector Auto-regression with and without innovations such as external regressors (BG-VAR(X)) and Bayesian Graphical Systems Equation Modelling with and without exogenous variables (BG-SEM(X)), which are developed to examine risk network structures embedded in multivariate time series. This methodical approach allows for the analysis of various dynamics and persistence in the multivariate time series in terms of risk propagation. For instance, both the BG-SEMX and BG-VARX can reveal the within-day and across-day major risk transmitters as well as risk recipients from other univariate time series, which better explain risk contagion using complex network models. In addition, the procedures for models with and without exogenous variables have been explored, which shows that the former produce more network structures compared to the latter and therefore depict their influential role. This approach, therefore, provides a platform for future research in terms of extension of the method to encompass different types of multivariate data with additional innovations that might aid feasible analysis and the design of policy instruments and the implementation of relevant policy implications. • Development and application of innovative network models that enhances the efficient analysis of multivariate time series data. • Estimation of intra-day and inter-day interconnection from a daily multivariate time series data and their dynamics and persistence from contagion analysis viewpoint.Emmanuel Senyo FianuDaniel Felix AhelegbeyLuigi GrossiElsevierarticleMethods for Risk analysis, and Model performance using Bayesian Graphical Network Vector Autoregression with Exogenous Variables (BG-VARX) and System Equation Modelling (BG-SEM) with real world applications.ScienceQENMethodsX, Vol 8, Iss , Pp 101587- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Methods for Risk analysis, and Model performance using Bayesian Graphical Network Vector Autoregression with Exogenous Variables (BG-VARX) and System Equation Modelling (BG-SEM) with real world applications. Science Q
spellingShingle	Methods for Risk analysis, and Model performance using Bayesian Graphical Network Vector Autoregression with Exogenous Variables (BG-VARX) and System Equation Modelling (BG-SEM) with real world applications. Science Q Emmanuel Senyo Fianu Daniel Felix Ahelegbey Luigi Grossi Risk management via contemporaneous and temporal dependence structures with applications
description	This paper presents the estimation methods of the Bayesian Graphical Vector Auto-regression with and without innovations such as external regressors (BG-VAR(X)) and Bayesian Graphical Systems Equation Modelling with and without exogenous variables (BG-SEM(X)), which are developed to examine risk network structures embedded in multivariate time series. This methodical approach allows for the analysis of various dynamics and persistence in the multivariate time series in terms of risk propagation. For instance, both the BG-SEMX and BG-VARX can reveal the within-day and across-day major risk transmitters as well as risk recipients from other univariate time series, which better explain risk contagion using complex network models. In addition, the procedures for models with and without exogenous variables have been explored, which shows that the former produce more network structures compared to the latter and therefore depict their influential role. This approach, therefore, provides a platform for future research in terms of extension of the method to encompass different types of multivariate data with additional innovations that might aid feasible analysis and the design of policy instruments and the implementation of relevant policy implications. • Development and application of innovative network models that enhances the efficient analysis of multivariate time series data. • Estimation of intra-day and inter-day interconnection from a daily multivariate time series data and their dynamics and persistence from contagion analysis viewpoint.
format	article
author	Emmanuel Senyo Fianu Daniel Felix Ahelegbey Luigi Grossi
author_facet	Emmanuel Senyo Fianu Daniel Felix Ahelegbey Luigi Grossi
author_sort	Emmanuel Senyo Fianu
title	Risk management via contemporaneous and temporal dependence structures with applications
title_short	Risk management via contemporaneous and temporal dependence structures with applications
title_full	Risk management via contemporaneous and temporal dependence structures with applications
title_fullStr	Risk management via contemporaneous and temporal dependence structures with applications
title_full_unstemmed	Risk management via contemporaneous and temporal dependence structures with applications
title_sort	risk management via contemporaneous and temporal dependence structures with applications
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/012c039f75784c16b7647ce2ca8ded1f
work_keys_str_mv	AT emmanuelsenyofianu riskmanagementviacontemporaneousandtemporaldependencestructureswithapplications AT danielfelixahelegbey riskmanagementviacontemporaneousandtemporaldependencestructureswithapplications AT luigigrossi riskmanagementviacontemporaneousandtemporaldependencestructureswithapplications
_version_	1718409820991651840

Risk management via contemporaneous and temporal dependence structures with applications

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