Nonlinear Dynamics and Recurrence Plots for Detecting Financial Crisis

Peter Martey Addo (), Monica Billio and Dominique Guegan ()
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Peter Martey Addo: PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, University of Ca’ Foscari [Venice, Italy]
Dominique Guegan: PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique

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Abstract: Identification of financial bubbles and crisis is a topic of major concern since it is important to prevent collapses that can severely impact nations and economies. Our analysis deals with the use of the recently proposed "delay vector variance" (DVV) method, which examines local predictability of a signal in the phase space to detect the presence of determinism and nonlinearity in a time series. Optimal embedding parameters used in the DVV analysis are obtained via a differential entropy based method using wavelet-based surrogates. We exploit the concept of recurrence plots to study the stock market to locate hidden patterns, non-stationarity, and to examine the nature of these plots in events of financial crisis. In particular, the recurrence plots are employed to detect and characterize financial cycles. A comprehensive analysis of the feasibility of this approach is provided. We show that our methodology is useful in the diagnosis and detection of financial bubbles, which have significantly impacted economic upheavals in the past few decades.

Keywords: wavelets; financial bubbles; embedding parameters; recurrence plots; Nonlinearity analysis; surrogates; Delay vector variance (DVV) method; plongement; bulles financières; Analyse non linéaire; méthode Delay vector variance; ondelettes (search for similar items in EconPapers)
Date: 2013-02
New Economics Papers: this item is included in nep-ets and nep-ger
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00803450v1
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Citations: View citations in EconPapers (15)

Published in 2013

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Related works:
Journal Article: Nonlinear dynamics and recurrence plots for detecting financial crisis (2013)
Working Paper: Nonlinear dynamics and recurrence plots for detecting financial crisis (2013)
Working Paper: Nonlinear Dynamics and Recurrence Plots for Detecting Financial Crisis (2013)
Working Paper: Nonlinear dynamics and recurrence plots for detecting financial crisis (2013)
Working Paper: Nonlinear dynamics and recurrence plots for detecting financial crisis (2013)
Working Paper: Nonlinear Dynamics and Recurrence Plots for Detecting Financial Crisis (2013)
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