IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v13y2020i9p208-d412561.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/13/9/208/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1911-8074/13/9/208/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Claudiu Tiberiu Albulescu, 2020. "Do COVID-19 and crude oil prices drive the US economic policy uncertainty?," Working Papers hal-02509450, HAL.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. Roll, R., 1989. "Price Volatility, International Market Links, And Their Implications For Regulatory Policies," Papers t10, Columbia - Center for Futures Markets.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Adriana AnaMaria Davidescu & Eduard Mihai Manta & Oana Mihaela Vacaru (Boita) & Mihaela Gruiescu & Razvan Gabriel Hapau & Paul Laurentiu Baranga, 2023. "Has the COVID-19 Pandemic Led to a Switch in the Volatility of Biopharmaceutical Companies?," Mathematics, MDPI, vol. 11(14), pages 1-24, July.
    2. Samet Gunay & Walid Bakry & Somar Al-Mohamad, 2021. "The Australian Stock Market’s Reaction to the First Wave of the COVID-19 Pandemic and Black Summer Bushfires: A Sectoral Analysis," JRFM, MDPI, vol. 14(4), pages 1-19, April.
    3. Lúcio, Francisco & Caiado, Jorge, 2022. "COVID-19 and Stock Market Volatility: A Clustering Approach for S&P 500 Industry Indices," Finance Research Letters, Elsevier, vol. 49(C).
    4. Brzeszczyński, Janusz & Gajdka, Jerzy & Pietraszewski, Piotr & Schabek, Tomasz, 2022. "Has the risk of socially responsible investments (SRI) companies stocks changed in the COVID-19 period? International evidence," Finance Research Letters, Elsevier, vol. 49(C).
    5. Hussain, Saiful Izzuan & Nur-Firyal, R. & Ruza, Nadiah, 2022. "Linkage transitions between oil and the stock markets of countries with the highest COVID-19 cases," Journal of Commodity Markets, Elsevier, vol. 28(C).
    6. Hussein Hassan & Minko Markovski & Alexander Mihailov, 2023. "A TGARCH Quantification of the Average Effect of COVID-19 Cases on Share Prices by Sector: Comparing the US and the UK," Economics Discussion Papers em-dp2023-15, Department of Economics, University of Reading.
    7. Wang Yijun & Zhang Yu & Usman Bashir, 2023. "Impact of COVID-19 on the contagion effect of risks in the banking industry: based on transfer entropy and social network analysis method," Risk Management, Palgrave Macmillan, vol. 25(2), pages 1-41, June.
    8. Md. Bokhtiar Hasan & Masnun Mahi & Tapan Sarker & Md. Ruhul Amin, 2021. "Spillovers of the COVID-19 Pandemic: Impact on Global Economic Activity, the Stock Market, and the Energy Sector," JRFM, MDPI, vol. 14(5), pages 1-18, May.
    9. Piñeiro-Chousa, Juan & López-Cabarcos, M. Ángeles & Quiñoá-Piñeiro, Lara & Pérez-Pico, Ada M., 2022. "US biopharmaceutical companies' stock market reaction to the COVID-19 pandemic. Understanding the concept of the ‘paradoxical spiral’ from a sustainability perspective," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    10. Lin, Weinan & Ouyang, Ruolan & Zhang, Xuan & Zhuang, Chengkai, 2023. "Network analysis of international financial markets contagion based on volatility indexes," Finance Research Letters, Elsevier, vol. 56(C).
    11. Dinesh Gajurel & Akhila Chawla, 2022. "International Information Spillovers and Asymmetric Volatility in South Asian Stock Markets," JRFM, MDPI, vol. 15(10), pages 1-18, October.
    12. Krzysztof Dmytrów & Joanna Landmesser & Beata Bieszk-Stolorz, 2021. "The Connections between COVID-19 and the Energy Commodities Prices: Evidence through the Dynamic Time Warping Method," Energies, MDPI, vol. 14(13), pages 1-23, July.
    13. Tihana Škrinjarić, 2021. "Profiting on the Stock Market in Pandemic Times: Study of COVID-19 Effects on CESEE Stock Markets," Mathematics, MDPI, vol. 9(17), pages 1-20, August.
    14. Mehmet Sahiner, 2022. "Forecasting volatility in Asian financial markets: evidence from recursive and rolling window methods," SN Business & Economics, Springer, vol. 2(10), pages 1-74, October.
    15. Pierdomenico Duttilo & Stefano Antonio Gattone & Tonio Di Battista, 2021. "Volatility Modeling: An Overview of Equity Markets in the Euro Area during COVID-19 Pandemic," Mathematics, MDPI, vol. 9(11), pages 1-18, May.
    16. Runumi Das & Arabinda Debnath, 2022. "Analyzing the COVID-19 Pandemic Volatility Spillover Influence on the Collaboration of Foreign and Indian Stock Markets," Revista Finanzas y Politica Economica, Universidad Católica de Colombia, vol. 14(2), pages 411-452, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Timmermann, Allan, 2018. "Forecasting Methods in Finance," CEPR Discussion Papers 12692, C.E.P.R. Discussion Papers.
    3. Mahdi Moradi & Mehdi Jabbari Nooghabi & Mohammad Mahdi Rounaghi, 2021. "Investigation of fractal market hypothesis and forecasting time series stock returns for Tehran Stock Exchange and London Stock Exchange," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 662-678, January.
    4. Allan Timmermann, 2018. "Forecasting Methods in Finance," Annual Review of Financial Economics, Annual Reviews, vol. 10(1), pages 449-479, November.
    5. Mehmet Sahiner, 2022. "Forecasting volatility in Asian financial markets: evidence from recursive and rolling window methods," SN Business & Economics, Springer, vol. 2(10), pages 1-74, October.
    6. Leland E. Farmer & Lawrence Schmidt & Allan Timmermann, 2023. "Pockets of Predictability," Journal of Finance, American Finance Association, vol. 78(3), pages 1279-1341, June.
    7. Daniel Andrei & Bruce Carlin & Michael Hasler, 2019. "Asset Pricing with Disagreement and Uncertainty About the Length of Business Cycles," Management Science, INFORMS, vol. 67(6), pages 2900-2923, June.
    8. Anisha Ghosh & George M. Constantinides, 2010. "The Predictability of Returns with Regime Shifts in Consumption and Dividend Growth," NBER Working Papers 16183, National Bureau of Economic Research, Inc.
    9. Daniel Mantilla-García & Vijay Vaidyanathan, 2017. "Predicting stock returns in the presence of uncertain structural changes and sample noise," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 31(3), pages 357-391, August.
    10. Rubio-Ramírez, Juan Francisco & Petrella, Ivan & Antolin-Diaz, Juan, 2021. "Dividend Momentum and Stock Return Predictability: A Bayesian Approach," CEPR Discussion Papers 16613, C.E.P.R. Discussion Papers.
    11. Michael Johannes & Arthur Korteweg & Nicholas Polson, 2014. "Sequential Learning, Predictability, and Optimal Portfolio Returns," Journal of Finance, American Finance Association, vol. 69(2), pages 611-644, April.
    12. Carlo A. Favero & Andrea Tamoni, 2010. "Demographics and the Econometrics of the Term Structure of Stock Market Risk," Working Papers 367, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    13. Avdis, Efstathios & Wachter, Jessica A., 2017. "Maximum likelihood estimation of the equity premium," Journal of Financial Economics, Elsevier, vol. 125(3), pages 589-609.
    14. Rounaghi, Mohammad Mahdi & Nassir Zadeh, Farzaneh, 2016. "Investigation of market efficiency and Financial Stability between S&P 500 and London Stock Exchange: Monthly and yearly Forecasting of Time Series Stock Returns using ARMA model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 10-21.
    15. Mykola Babiak & Jozef Barunik, 2020. "Deep Learning, Predictability, and Optimal Portfolio Returns," CERGE-EI Working Papers wp677, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    16. Zhu, Xiaoneng & Zhu, Jie, 2013. "Predicting stock returns: A regime-switching combination approach and economic links," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4120-4133.
    17. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 328-383, Elsevier.
    18. Ferreira, Miguel A. & Santa-Clara, Pedro, 2011. "Forecasting stock market returns: The sum of the parts is more than the whole," Journal of Financial Economics, Elsevier, vol. 100(3), pages 514-537, June.
    19. Chia-Lin Chang & Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Long Run Returns Predictability and Volatility with Moving Averages," Risks, MDPI, vol. 6(4), pages 1-18, September.
    20. Neuhierl, Andreas & Weber, Michael, 2019. "Monetary policy communication, policy slope, and the stock market," Journal of Monetary Economics, Elsevier, vol. 108(C), pages 140-155.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jjrfmx:v:13:y:2020:i:9:p:208-:d:412561. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.