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Short Term Stress of Covid-19 on World Major Stock Indices

Author

Listed:
  • Muhammad Rehan

    (Gaziosmanpasa University)

  • Jahanzaib Alvi

    (Iqra University)

  • Süleyman Serdar Karaca

    (Malatya Turgut Özal University)

Abstract

The main objective of this study is to check the short-term stress of COVID-19 pandemic on the American, European, Asian, and Pacific stock market indices and furthermore the correlation between all the stock markets during the pandemic. Secondary data of 41 stock exchange from 32 countries have been collected from investing.com website from 1st July 2019 to 14th May 2020 for the stock market and the COVID-19 data has been collected according to the first cases reported in the country, stocks market are classified either developed or emerging economy, further divided according to the subcontinent i.e. America, Europe, and Pacific/Asia, the main focus in the data is the report of first COVID-19 cases. The study reveals that there is volatility in the all the 41 stock market (American, Europe, Asia, and Pacific) after reporting of the first case and volatility increase with the increase of COVID-19 cases, moreover, there is a significant negative relationship between the number of COVID-19 cases and 41 major stock indices of American, Europe, Asia and Pacific, European subcontinent market found more effected from the COVID-19 than another subcontinent, there is Clustering effect of COVID-19 on all the stock market except American's stock market due to smart capital investing.

Suggested Citation

  • Muhammad Rehan & Jahanzaib Alvi & Süleyman Serdar Karaca, 2022. "Short Term Stress of Covid-19 on World Major Stock Indices," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 29(3), pages 527-568, September.
    • Handle: RePEc:kap:apfinm:v:29:y:2022:i:3:d:10.1007_s10690-022-09359-7
    DOI: 10.1007/s10690-022-09359-7
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    1. John Eric Humphries & Christopher Neilson & Gabriel Ulyssea, 2020. "The evolving impacts of COVID-19 on small businesses since the CARES Act," Cowles Foundation Discussion Papers 2230, Cowles Foundation for Research in Economics, Yale University.
    2. HaiYue Liu & Aqsa Manzoor & CangYu Wang & Lei Zhang & Zaira Manzoor, 2020. "The COVID-19 Outbreak and Affected Countries Stock Markets Response," IJERPH, MDPI, vol. 17(8), pages 1-19, April.
    3. Nelson, Daniel B & Cao, Charles Q, 1992. "Inequality Constraints in the Univariate GARCH Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(2), pages 229-235, April.
    4. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    5. Goodell, John W. & Huynh, Toan Luu Duc, 2020. "Did Congress trade ahead? Considering the reaction of US industries to COVID-19," Finance Research Letters, Elsevier, vol. 36(C).
    6. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    7. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    8. Schell, Daniel & Wang, Mei & Huynh, Toan Luu Duc, 2020. "This time is indeed different: A study on global market reactions to public health crisis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    9. Zhang, Dayong & Hu, Min & Ji, Qiang, 2020. "Financial markets under the global pandemic of COVID-19," Finance Research Letters, Elsevier, vol. 36(C).
    10. Roberto Rigobon, 2002. "Contagion: How to Measure It?," NBER Chapters, in: Preventing Currency Crises in Emerging Markets, pages 269-334, National Bureau of Economic Research, Inc.
    11. repec:aei:rpaper:1008560098 is not listed on IDEAS
    12. 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.
    13. Ortmann, Regina & Pelster, Matthias & Wengerek, Sascha Tobias, 2020. "COVID-19 and investor behavior," Finance Research Letters, Elsevier, vol. 37(C).
    14. Alexander W. Bartik & Marianne Bertrand & Zoë B. Cullen & Edward L. Glaeser & Michael Luca & Christopher T. Stanton, 2020. "How Are Small Businesses Adjusting to COVID-19? Early Evidence from a Survey," NBER Working Papers 26989, National Bureau of Economic Research, Inc.
    15. Durlauf, Steven N & Phillips, Peter C B, 1988. "Trends versus Random Walks in Time Series Analysis," Econometrica, Econometric Society, vol. 56(6), pages 1333-1354, November.
    16. Bai, Hang & Hou, Kewei & Kung, Howard & Li, Erica X.N. & Zhang, Lu, 2019. "The CAPM strikes back? An equilibrium model with disasters," Journal of Financial Economics, Elsevier, vol. 131(2), pages 269-298.
    17. Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September.
    18. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Cited by:

    1. Fei Su & Feifan Wang & Yahua Xu, 2025. "Economic Policy Uncertainty and Volatility Spillovers Among International Stock Market Indices During the COVID-19 Outbreak," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 32(1), pages 237-266, March.
    2. Xiangyu Chen & Jittima Tongurai & Pattana Boonchoo, 2024. "Revisiting China’s Commodity Futures Market Amid the Main Waves of COVID-19 Pandemics," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 31(4), pages 1035-1063, December.
    3. Subhash Karmakar & Gautam Bandyopadhyay & Jayanta Nath Mukhopadhyay, 2024. "Systemic Risk in Indian Financial Institutions: A Probabilistic Approach," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 31(3), pages 579-656, September.
    4. Yunpeng Su & Jia Li & Baochen Yang & Yunbi An, 2024. "The Impacts of Policy Uncertainty on Asset Prices: Evidence from China’s Market," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 31(4), pages 1087-1133, December.
    5. Lorenzo Escot & Julio E. Sandubete & Łukasz Pietrych, 2023. "Detecting Structural Changes in Time Series by Using the BDS Test Recursively: An Application to COVID-19 Effects on International Stock Markets," Mathematics, MDPI, vol. 11(23), pages 1-18, December.

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    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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