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Quantifying chaos in stock markets before and during COVID-19 pandemic from the phase space reconstruction

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  • Alves, P.R.L.

Abstract

From a methodology in the reconstruction scheme, applicable to chaotic time series of economic indices, this paper presents an analysis of the underlying dynamics of stock markets of North America, Europe and Asia. The same global fit model and reconstruction parameters—employed to study the time evolution of S&P 500, NASDAQ Composite, IBEX 35, EURONEXT 100, Nikkei 225 and SSE Composite Index—led a convenient simplification in the analysis. The tools chosen to analyse the time dependence of the level of chaos concerning weeks of economic activity were scatter plots, histograms and sample Spearman correlation coefficients. The results permit to evaluate the impact of the pandemic in the underlying dynamics of different stock markets and to compare them to one another.

Suggested Citation

  • Alves, P.R.L., 2022. "Quantifying chaos in stock markets before and during COVID-19 pandemic from the phase space reconstruction," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 202(C), pages 480-499.
    • Handle: RePEc:eee:matcom:v:202:y:2022:i:c:p:480-499
    DOI: 10.1016/j.matcom.2022.07.026
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    References listed on IDEAS

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    1. Zhang, Dayong & Hu, Min & Ji, Qiang, 2020. "Financial markets under the global pandemic of COVID-19," Finance Research Letters, Elsevier, vol. 36(C).
    2. Zribi, Wissal & Boufateh, Talel, 2020. "Asymmetric CEO confidence and CSR: A nonlinear panel ARDL-PMG approach," The Journal of Economic Asymmetries, Elsevier, vol. 22(C).
    3. Lahmiri, Salim & Bekiros, Stelios, 2020. "Renyi entropy and mutual information measurement of market expectations and investor fear during the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    4. Aliyev, Fuzuli & Ajayi, Richard & Gasim, Nijat, 2020. "Modelling asymmetric market volatility with univariate GARCH models: Evidence from Nasdaq-100," The Journal of Economic Asymmetries, Elsevier, vol. 22(C).
    5. Urom, Christian & Ndubuisi, Gideon & Ozor, Jude, 2021. "Economic activity, and financial and commodity markets’ shocks: An analysis of implied volatility indexes," International Economics, Elsevier, vol. 165(C), pages 51-66.
    6. Padhan, Hemachandra & Sahu, Santosh Kumar & Dash, Umakant, 2021. "Non-linear analysis of international reserve, trade and trilemma in India," The Journal of Economic Asymmetries, Elsevier, vol. 23(C).
    7. Jovanovic, Franck & Schinckus, Christophe, 2017. "Econophysics and Financial Economics: An Emerging Dialogue," OUP Catalogue, Oxford University Press, number 9780190205034.
    8. Contreras G., Mauricio & Peña, Juan Pablo & Aros, Rodrigo, 2021. "Second class constraints and the consistency of optimal control theory in phase space," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).
    9. Alves, P.R.L. & Duarte, L.G.S. & da Mota, L.A.C.P., 2018. "Detecting chaos and predicting in Dow Jones Index," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 232-238.
    10. Ghazani, Majid Mirzaee & Khosravi, Reza, 2020. "Multifractal detrended cross-correlation analysis on benchmark cryptocurrencies and crude oil prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    11. Alves, P.R.L. & Duarte, L.G.S. & da Mota, L.A.C.P., 2017. "A new characterization of chaos from a time series," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 323-326.
    12. Lahmiri, Salim & Bekiros, Stelios, 2020. "The impact of COVID-19 pandemic upon stability and sequential irregularity of equity and cryptocurrency markets," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    13. Panagiotou, Dimitrios, 2021. "Asymmetric price responses of the US pork retail prices to farm and wholesale price shocks: A nonlinear ARDL approach," The Journal of Economic Asymmetries, Elsevier, vol. 23(C).
    14. Sharif, Arshian & Aloui, Chaker & Yarovaya, Larisa, 2020. "COVID-19 pandemic, oil prices, stock market, geopolitical risk and policy uncertainty nexus in the US economy: Fresh evidence from the wavelet-based approach," International Review of Financial Analysis, Elsevier, vol. 70(C).
    15. Alves, P.R.L., 2020. "Dynamic characteristic of Bitcoin cryptocurrency in the reconstruction scheme," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
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