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Volatility in International Stock Markets: An Empirical Study during COVID-19

Author

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  • Rashmi Chaudhary

    (Department of Finance, Jaipuria Institute of Management, Lucknow 226010, India)

  • Priti Bakhshi

    (Department of Finance and Banking, Jaipuria Institute of Management, Indore 453771, India)

  • Hemendra Gupta

    (Department of Finance, Jaipuria Institute of Management, Lucknow 226010, India)

Abstract

Predicting volatility is a must in the finance domain. Estimations of volatility, along with the central tendency, permit us to evaluate the chances of getting a particular result. Financial analysts are frequently challenged with the assignment of diversifying assets in order to form efficient portfolios with a higher risk to reward ratio. The objective of this research is to analyze the influence of COVID-19 on the return and volatility of the stock market indices of the top 10 countries based on GDP using a widely applied econometric model—generalized autoregressive conditional heteroscedasticity (GARCH). For this purpose, the daily returns of market indices from January 2019 to June 2020 were taken into consideration. The results reveal daily negative mean returns for all market indices during the COVID period (January 2020 to June 2020). Though the second quarter of the COVID period reflects a bounce back for all market indices with altered strengths, the volatility remains higher than in normal periods, signaling a bearish tendency in the market. The COVID variable, as an exogenous variance regressor in GARCH modeling, is found to be positive and significant for all market indices. Furthermore, the results confirmed the mean-reverting process for all market indices.

Suggested Citation

  • Rashmi Chaudhary & Priti Bakhshi & Hemendra Gupta, 2020. "Volatility in International Stock Markets: An Empirical Study during COVID-19," JRFM, MDPI, vol. 13(9), pages 1-17, September.
    • Handle: RePEc:gam:jjrfmx:v:13:y:2020:i:9:p:208-:d:412561
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    1. Brooks, Chris & Rew, Alistair G, 2002. "Testing for a Unit Root in a Process Exhibiting a Structural Break in the Presence of GARCH Errors," Computational Economics, Springer;Society for Computational Economics, vol. 20(3), pages 157-176, December.
    2. Claudiu Tiberiu Albulescu, 2020. "Do COVID-19 and crude oil prices drive the US economic policy uncertainty?," Working Papers hal-02509450, HAL.
    3. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    4. T. Clifton Green & Stephen Figlewski, 1999. "Market Risk and Model Risk for a Financial Institution Writing Options," Journal of Finance, American Finance Association, vol. 54(4), pages 1465-1499, August.
    5. Huyghebaert, Nancy & Wang, Lihong, 2010. "The co-movement of stock markets in East Asia: Did the 1997-1998 Asian financial crisis really strengthen stock market integration?," China Economic Review, Elsevier, vol. 21(1), pages 98-112, March.
    6. JULES H. Van BINSBERGEN & RALPH S. J. KOIJEN, 2010. "Predictive Regressions: A Present‐Value Approach," Journal of Finance, American Finance Association, vol. 65(4), pages 1439-1471, August.
    7. Claus Michelsen & Guido Baldi & Geraldine Dany-Knedlik & Hella Engerer & Stefan Gebauer & Malte Rieth, 2020. "Coronavirus Causing Major Economic Shock to the Global Economy: DIW Economic Outlook," DIW Weekly Report, DIW Berlin, German Institute for Economic Research, vol. 10(12), pages 180-182.
    8. Ľuboš Pástor & Robert F. Stambaugh, 2009. "Predictive Systems: Living with Imperfect Predictors," Journal of Finance, American Finance Association, vol. 64(4), pages 1583-1628, August.
    9. Chang, Bo Young & Christoffersen, Peter & Jacobs, Kris, 2013. "Market skewness risk and the cross section of stock returns," Journal of Financial Economics, Elsevier, vol. 107(1), pages 46-68.
    10. Mei, Dexiang & Liu, Jing & Ma, Feng & Chen, Wang, 2017. "Forecasting stock market volatility: Do realized skewness and kurtosis help?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 153-159.
    11. John H. Cochrane, 2008. "The Dog That Did Not Bark: A Defense of Return Predictability," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1533-1575, July.
    12. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    13. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    14. Zahedi, Javad & Rounaghi, Mohammad Mahdi, 2015. "Application of artificial neural network models and principal component analysis method in predicting stock prices on Tehran Stock Exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 178-187.
    15. Shraddha Mishra, 2017. "Volatility and calendar anomaly through GARCH model: evidence from the selected G20 stock exchanges," International Journal of Business and Globalisation, Inderscience Enterprises Ltd, vol. 19(1), pages 126-144.
    16. Naliniprava Tripathy, 2017. "Do BRIC countries stock market volatility move together? An empirical analysis of using multivariate GARCH models," International Journal of Business and Emerging Markets, Inderscience Enterprises Ltd, vol. 9(2), pages 104-123.
    17. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    18. Nahida Akter & Ashadun Nobi, 2018. "Investigation of the Financial Stability of S&P 500 Using Realized Volatility and Stock Returns Distribution," JRFM, MDPI, vol. 11(2), pages 1-10, April.
    19. Tamara Teplova & Evgeniya Shutova, 2011. "A Higher Moment Downside Framework for Conditional and Unconditional Capm in the Russian Stock Market," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 1(2), pages 157-178, December.
    20. Roll, R., 1989. "Price Volatility, International Market Links, And Their Implications For Regulatory Policies," Papers t10, Columbia - Center for Futures Markets.
    21. Fletcher, Jonathan & Kihanda, Joseph, 2005. "An examination of alternative CAPM-based models in UK stock returns," Journal of Banking & Finance, Elsevier, vol. 29(12), pages 2995-3014, December.
    22. Mario Arturo Ruiz Estrada, 2014. "Economic Waves: The Effect of the U.S. Economy on the World Economy," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 8(3), September.
    23. Ang, B.W. & Liu, Na, 2007. "Negative-value problems of the logarithmic mean Divisia index decomposition approach," Energy Policy, Elsevier, vol. 35(1), pages 739-742, January.
    24. Laura Alfaro & Anusha Chari & Andrew N. Greenland & Peter K. Schott, 2020. "Aggregate and Firm-Level Stock Returns During Pandemics, in Real Time," NBER Working Papers 26950, National Bureau of Economic Research, Inc.
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