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

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.
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https://hal.archives-ouvertes.fr/hal-01110712
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Submitted on : Wednesday, January 28, 2015 - 5:37:57 PM
Last modification on : Tuesday, January 19, 2021 - 11:08:29 AM
Long-term archiving on: : Wednesday, April 29, 2015 - 11:20:58 AM

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  • HAL Id : hal-01110712, version 1

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Peter Martey Addo, Philippe de Peretti. Detection and quantification of causal dependencies in multivariate time series: a novel information theoretic approach to understanding systemic risk. 2014. ⟨hal-01110712v1⟩

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