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Dynamic graph in a symbolic data framework: An account of the causal relation using COVID-19 reports and some reflections on the financial world

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Listed:
  • Nascimento, Diego C.
  • Pimentel, Bruno A.
  • Souza, Renata M.C.R.
  • Costa, Lilia
  • Gonçalves, Sandro
  • Louzada, Francisco

Abstract

This article aims to evaluate a complex relation structure represented by a graph, considering a high dimensional dataset in the Symbolic Data Analysis domain. We consider COVID-19 pandemic dynamic data regarding the first semester of 2020 associated with the daily infection rate in 214 countries with remarkable trends from the financial market; thus, the empirical causality. This work is innovative as we developed a dynamic graphical model for interval data based on center-range representation, which can shrink the parametric high-dimensional time series space and uncovers causal relations. Symbolic Data Analysis provides tools to reduce data dimension through the fusion of multivariate time series in data classes, which allows considering complex information through symbolic interval multivalued variables. Additionally, the Multiregression Dynamic Model (MDM) approach estimates a Directed Acyclic Graph (DAG) which distinguishes structural changes and irregular patterns by modeling the joint learning of multivariate time series, that is, allowing heterogeneous pattern collections and simultaneously estimating relationships across series, now as symbolic interval data. Time-varying parameter estimates of allowed us to translate the influence (internal and external) of these structures dynamically, during the first months of 2020, on the interconnectedness of global regions and the spread of coronavirus worldwide. Then, descriptions of the internal variation of the regions are obtained, after the first months of the semester, reflecting the lockdown (that is, the virus transmission occurs in a generalized way worldwide, then reduced, but concentrated within the regions and not more between them). Finally, an association was sought on the impact of the disclosure (news) of COVID-19 and empirical impacts with performances of the main indices of the global financial market, in which an association between these phenomena was noticeable.

Suggested Citation

  • Nascimento, Diego C. & Pimentel, Bruno A. & Souza, Renata M.C.R. & Costa, Lilia & Gonçalves, Sandro & Louzada, Francisco, 2021. "Dynamic graph in a symbolic data framework: An account of the causal relation using COVID-19 reports and some reflections on the financial world," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    • Handle: RePEc:eee:chsofr:v:153:y:2021:i:p2:s0960077921007943
    DOI: 10.1016/j.chaos.2021.111440
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    1. Jushan Bai, 1994. "Least Squares Estimation Of A Shift In Linear Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 15(5), pages 453-472, September.
    2. Blanchard, Olivier Jean, 1990. "Why does money affect output? A survey," Handbook of Monetary Economics, in: B. M. Friedman & F. H. Hahn (ed.), Handbook of Monetary Economics, edition 1, volume 2, chapter 15, pages 779-835, Elsevier.
    3. Gruber, Lutz F. & West, Mike, 2017. "Bayesian online variable selection and scalable multivariate volatility forecasting in simultaneous graphical dynamic linear models," Econometrics and Statistics, Elsevier, vol. 3(C), pages 3-22.
    4. Warwick McKibbin & Roshen Fernando, 2021. "The Global Macroeconomic Impacts of COVID-19: Seven Scenarios," Asian Economic Papers, MIT Press, vol. 20(2), pages 1-30, Summer.
    5. Raquel Prado & Mike West & Andrew D. Krystal, 2001. "Multichannel electroencephalographic analyses via dynamic regression models with time‐varying lag–lead structure," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(1), pages 95-109.
    6. Scott R Baker & Nicholas Bloom & Steven J Davis & Kyle Kost & Marco Sammon & Tasaneeya Viratyosin & Jeffrey Pontiff, 0. "The Unprecedented Stock Market Reaction to COVID-19," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 10(4), pages 742-758.
    7. Lima Neto, Eufrasio de A. & de Carvalho, Francisco de A.T., 2008. "Centre and Range method for fitting a linear regression model to symbolic interval data," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1500-1515, January.
    8. Vanessa Ratten & Seyed Khaksar, 2021. "COVID-19: Future Marketing and Technology Research Challenges," Springer Books, in: Vanessa Ratten & Park Thaichon (ed.), COVID-19, Technology and Marketing, edition 1, chapter 0, pages 143-157, Springer.
    9. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    10. Neil M. Ferguson & Derek A. T. Cummings & Christophe Fraser & James C. Cajka & Philip C. Cooley & Donald S. Burke, 2006. "Strategies for mitigating an influenza pandemic," Nature, Nature, vol. 442(7101), pages 448-452, July.
    11. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    12. Mandal, Manotosh & Jana, Soovoojeet & Nandi, Swapan Kumar & Khatua, Anupam & Adak, Sayani & Kar, T.K., 2020. "A model based study on the dynamics of COVID-19: Prediction and control," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    13. Lux, Thomas, 2021. "The social dynamics of COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(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. Sims, Christopher A, 1972. "Money, Income, and Causality," American Economic Review, American Economic Association, vol. 62(4), pages 540-552, September.
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