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The impact of COVID-19 on global stock markets: early linear and non-linear evidence for Italy

Author

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  • Theodoros Daglis

    (National Technical University of Athens)

  • Ioannis G. Melissaropoulos

    (National Technical University of Athens)

  • Konstantinos N. Konstantakis

    (National Technical University of Athens)

  • Panayotis G. Michaelides

    (National Technical University of Athens)

Abstract

The scientific community still struggles to understand the magnitude of the worldwide infections and deaths induced by COVID-19, partly ignoring the financial consequences. In this paper, using the autoregressive fractionally integrated moving average (ARFIMA)—general autoregressive conditional heteroskedasticity (GARCH) model, we quantify and show the impact of the COVID-19 spread in Italy, utilizing data for the stock market. Using information criteria and forecasting accuracy measures, we show that the COVID-19 confirmed cases contribute with statistically significant information to the modeling of volatility, and also increase the forecasting ability of the volatility of the Italian stock market index, leading to a decrease in the mean stock index.

Suggested Citation

  • Theodoros Daglis & Ioannis G. Melissaropoulos & Konstantinos N. Konstantakis & Panayotis G. Michaelides, 2022. "The impact of COVID-19 on global stock markets: early linear and non-linear evidence for Italy," Evolutionary and Institutional Economics Review, Springer, vol. 19(1), pages 485-495, April.
    • Handle: RePEc:spr:eaiere:v:19:y:2022:i:1:d:10.1007_s40844-021-00230-4
    DOI: 10.1007/s40844-021-00230-4
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    1. John Geweke & Susan Porter‐Hudak, 1983. "The Estimation And Application Of Long Memory Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 4(4), pages 221-238, July.
    2. Daglis, Theodoros & Konstantakis, Konstantinos N. & Michaelides, Panayotis G. & Papadakis, Theodoulos Eleftherios, 2020. "The forecasting ability of solar and space weather data on NASDAQ’s finance sector price index volatility," Research in International Business and Finance, Elsevier, vol. 52(C).
    3. Stefano Ramelli & Alexander F Wagner, 2020. "Feverish Stock Price Reactions to COVID-19," The Review of Corporate Finance Studies, Society for Financial Studies, vol. 9(3), pages 622-655.
    4. Donadelli, Michael & Kizys, Renatas & Riedel, Max, 2017. "Dangerous infectious diseases: Bad news for Main Street, good news for Wall Street?," Journal of Financial Markets, Elsevier, vol. 35(C), pages 84-103.
    5. Niels Joachim Gormsen & Ralph S J Koijen & Nikolai Roussanov, 0. "Coronavirus: Impact on Stock Prices and Growth Expectations," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 10(4), pages 574-597.
    6. Scheicher, Martin, 2001. "The Comovements of Stock Markets in Hungary, Poland and the Czech Republic," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 6(1), pages 27-39, January.
    7. Bekaert, Geert & Harvey, Campbell R., 1997. "Emerging equity market volatility," Journal of Financial Economics, Elsevier, vol. 43(1), pages 29-77, January.
    8. Haroon, Omair & Rizvi, Syed Aun R., 2020. "COVID-19: Media coverage and financial markets behavior—A sectoral inquiry," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    9. Rui Albuquerque & Yrjo Koskinen & Shuai Yang & Chendi Zhang, 2020. "Resiliency of Environmental and Social Stocks: An Analysis of the Exogenous COVID-19 Market Crash," The Review of Corporate Finance Studies, Society for Financial Studies, vol. 9(3), pages 593-621.
    10. Han, Heejoon & Park, Joon Y., 2012. "ARCH/GARCH with persistent covariate: Asymptotic theory of MLE," Journal of Econometrics, Elsevier, vol. 167(1), pages 95-112.
    11. Scott R. Baker & Nicholas Bloom & Steven J. Davis & Kyle J. Kost & Marco C. Sammon & Tasaneeya Viratyosin, 2020. "The Unprecedented Stock Market Impact of COVID-19," NBER Working Papers 26945, National Bureau of Economic Research, Inc.
    12. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    13. 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.
    14. Syriopoulos, Theodore, 2007. "Dynamic linkages between emerging European and developed stock markets: Has the EMU any impact?," International Review of Financial Analysis, Elsevier, vol. 16(1), pages 41-60.
    15. Just, Małgorzata & Echaust, Krzysztof, 2020. "Stock market returns, volatility, correlation and liquidity during the COVID-19 crisis: Evidence from the Markov switching approach," Finance Research Letters, Elsevier, vol. 37(C).
    16. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
    17. Ashraf, Badar Nadeem, 2020. "Economic impact of government interventions during the COVID-19 pandemic: International evidence from financial markets," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
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    1. Jiahui Xi & Conghua Wen & Yifan Tang & Feifan Zhao, 2024. "A factor score clustering approach to analyze the biopharmaceutical sector in the Chinese market during COVID-19," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-28, December.

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    More about this item

    Keywords

    COVID-19; Stock market; Italy;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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