Detection and quantification of causal dependencies in multivariate time series: a novel information theoretic approach to understanding systemic risk

Peter Martey Addo and Philippe De Peretti ()
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Philippe De Peretti: Centre d'Economie de la Sorbonne, https://centredeconomiesorbonne.univ-paris1.fr

Documents de travail du Centre d'Economie de la Sorbonne from Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne

Abstract: The recent financial crisis has lead to a need for regulators and policy makers to understand and track systemic linkages. We provide a new approach to understanding systemic risk tomography in finance and insurance sectors. The analysis is achieved by using a recently proposed method on quantifying causal coupling strength, which identifies the existence of causal dependencies between two components of a multivariate time series and assesses the strength of their association by defining a meaningful coupling strength using the momentary information transfer (MIT). The measure of association is general, causal and lag-specific, reflecting a well interpretable notion of coupling strength and is practically computable. A comprehensive analysis of the feasibility of this approach is provided via simulated data and then applied to the monthly returns of hedge funds, banks, broker/dealers, and insurance companies

Keywords: Systemic risk; financial crisis; Coupling strength; financial institutions (search for similar items in EconPapers)
JEL-codes: C32 C40 G12 G29 (search for similar items in EconPapers)
Date: 2014-10
New Economics Papers: this item is included in nep-ban, nep-ets and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:mse:cesdoc:14069

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