IDEAS home Printed from https://ideas.repec.org/a/eee/ecofin/v58y2021ics106294082100156x.html
   My bibliography  Save this article

How have the dependence structures between stock markets and economic factors changed during the COVID-19 pandemic?

Author

Listed:
  • Dong, Xiyong
  • Song, Li
  • Yoon, Seong-Min

Abstract

This study investigates how the dependence structures between stock markets and economic factors have changed during the COVID-19 pandemic using the dynamic model averaging approach. A series of economic factors such as commodity markets, cryptocurrency, monetary policy, international capital flows, and market uncertainty indices are considered. We find that the importance of economic variables and the sign and size of their coefficients are significantly different from those before the COVID-19 pandemic. The stock markets are most influenced by economic factors during the COVID-19 outbreak.

Suggested Citation

  • Dong, Xiyong & Song, Li & Yoon, Seong-Min, 2021. "How have the dependence structures between stock markets and economic factors changed during the COVID-19 pandemic?," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    • Handle: RePEc:eee:ecofin:v:58:y:2021:i:c:s106294082100156x
    DOI: 10.1016/j.najef.2021.101546
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S106294082100156X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.najef.2021.101546?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gary Koop & Dimitris Korobilis, 2012. "Forecasting Inflation Using Dynamic Model Averaging," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(3), pages 867-886, August.
    2. Aye, Goodness & Gupta, Rangan & Hammoudeh, Shawkat & Kim, Won Joong, 2015. "Forecasting the price of gold using dynamic model averaging," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 257-266.
    3. Malcolm Baker & Jeffrey Wurgler, 2006. "Investor Sentiment and the Cross‐Section of Stock Returns," Journal of Finance, American Finance Association, vol. 61(4), pages 1645-1680, August.
    4. Zhang, Dayong & Hu, Min & Ji, Qiang, 2020. "Financial markets under the global pandemic of COVID-19," Finance Research Letters, Elsevier, vol. 36(C).
    5. Li, Yan & Liang, Chao & Ma, Feng & Wang, Jiqian, 2020. "The role of the IDEMV in predicting European stock market volatility during the COVID-19 pandemic," Finance Research Letters, Elsevier, vol. 36(C).
    6. Drachal, Krzysztof, 2016. "Forecasting spot oil price in a dynamic model averaging framework — Have the determinants changed over time?," Energy Economics, Elsevier, vol. 60(C), pages 35-46.
    7. Chen, Pu & Semmler, Willi, 2018. "Financial stress, regime switching and spillover effects: Evidence from a multi-regime global VAR model," Journal of Economic Dynamics and Control, Elsevier, vol. 91(C), pages 318-348.
    8. Zaremba, Adam & Kizys, Renatas & Aharon, David Y. & Demir, Ender, 2020. "Infected Markets: Novel Coronavirus, Government Interventions, and Stock Return Volatility around the Globe," Finance Research Letters, Elsevier, vol. 35(C).
    9. Hossein Hassani & Emmanuel Sirimal Silva & Rangan Gupta & Mawuli K. Segnon, 2015. "Forecasting the price of gold," Applied Economics, Taylor & Francis Journals, vol. 47(39), pages 4141-4152, August.
    10. Dong, Xiyong & Li, Changhong & Yoon, Seong-Min, 2020. "Asymmetric dependence structures for regional stock markets: An unconditional quantile regression approach," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    11. Dong, Xiyong & Yoon, Seong-Min, 2019. "What global economic factors drive emerging Asian stock market returns? Evidence from a dynamic model averaging approach," Economic Modelling, Elsevier, vol. 77(C), pages 204-215.
    12. Sakurai, Yuji & Kurosaki, Tetsuo, 2020. "How has the relationship between oil and the US stock market changed after the Covid-19 crisis?," Finance Research Letters, Elsevier, vol. 37(C).
    13. Wei, Yu & Cao, Yang, 2017. "Forecasting house prices using dynamic model averaging approach: Evidence from China," Economic Modelling, Elsevier, vol. 61(C), pages 147-155.
    14. Naser, Hanan, 2016. "Estimating and forecasting the real prices of crude oil: A data rich model using a dynamic model averaging (DMA) approach," Energy Economics, Elsevier, vol. 56(C), pages 75-87.
    15. Reboredo, Juan C. & Rivera-Castro, Miguel A. & Ugolini, Andrea, 2016. "Downside and upside risk spillovers between exchange rates and stock prices," Journal of Banking & Finance, Elsevier, vol. 62(C), pages 76-96.
    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. Dong, Xiyong & Xiong, Youlin & Nie, Siyue & Yoon, Seong-Min, 2023. "Can bonds hedge stock market risks? Green bonds vs conventional bonds," Finance Research Letters, Elsevier, vol. 52(C).
    2. Klose, Jens & Tillmann, Peter, 2022. "The Real and Financial Impact of COVID-19 Around the World," VfS Annual Conference 2022 (Basel): Big Data in Economics 264030, Verein für Socialpolitik / German Economic Association.
    3. Jens Klose & Peter Tillmann, 2023. "The stock market and NO2 emissions effects of COVID‐19 around the world," Economics and Politics, Wiley Blackwell, vol. 35(2), pages 556-594, July.
    4. Okorie, David Iheke & Lin, Boqiang, 2023. "Cryptocurrency spectrum and 2020 pandemic: Contagion analysis," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 29-38.
    5. Liu, Yan & Cheng, Xian & Liao, Stephen Shaoyi & Yang, Feng, 2023. "The impact of COVID-19 on the tourism and hospitality Industry: Evidence from international stock markets," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    6. Gong, Xu & Xu, Jun & Liu, Tangyong & Zhou, Zicheng, 2022. "Dynamic volatility connectedness between industrial metal markets," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    7. Oliyide, Johnson A. & Adekoya, Oluwasegun B. & Marie, Mohamed & Al-Faryan, Mamdouh Abdulaziz Saleh, 2023. "Green finance and commodities: Cross-market connectedness during different COVID-19 episodes," Resources Policy, Elsevier, vol. 85(PA).

    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. Dong, Xiyong & Yoon, Seong-Min, 2019. "What global economic factors drive emerging Asian stock market returns? Evidence from a dynamic model averaging approach," Economic Modelling, Elsevier, vol. 77(C), pages 204-215.
    2. Jan Prüser, 2019. "Adaptive learning from model space," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(1), pages 29-38, January.
    3. Nima Nonejad, 2021. "An Overview Of Dynamic Model Averaging Techniques In Time‐Series Econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 566-614, April.
    4. Nonejad, Nima, 2021. "Predicting equity premium using dynamic model averaging. Does the state–space representation matter?," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    5. Krzysztof Drachal, 2018. "Some Novel Bayesian Model Combination Schemes: An Application to Commodities Prices," Sustainability, MDPI, vol. 10(8), pages 1-27, August.
    6. Zhongxin Ni & Xing Lu & Wenjun Xue, 2021. "Does the belt and road initiative resolve the steel overcapacity in China? Evidence from a dynamic model averaging approach," Empirical Economics, Springer, vol. 61(1), pages 279-307, July.
    7. Wang, TianTian & Zhang, Dayong & Clive Broadstock, David, 2019. "Financialization, fundamentals, and the time-varying determinants of US natural gas prices," Energy Economics, Elsevier, vol. 80(C), pages 707-719.
    8. Donghua Wang & Yang Xin & Xiaohui Chang & Xingze Su, 2021. "Realized volatility forecasting and volatility spillovers: Evidence from Chinese non‐ferrous metals futures," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2713-2731, April.
    9. Dong, Xiyong & Li, Changhong & Yoon, Seong-Min, 2021. "How can investors build a better portfolio in small open economies? Evidence from Asia’s Four Little Dragons," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    10. Krzysztof Drachal, 2018. "Determining Time-Varying Drivers of Spot Oil Price in a Dynamic Model Averaging Framework," Energies, MDPI, vol. 11(5), pages 1-24, May.
    11. Zhang, Yaojie & Ma, Feng & Wang, Yudong, 2019. "Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors?," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 97-117.
    12. Cheng, Xian & Wu, Peng & Liao, Stephen Shaoyi & Wang, Xuelian, 2023. "An integrated model for crude oil forecasting: Causality assessment and technical efficiency," Energy Economics, Elsevier, vol. 117(C).
    13. Drachal, Krzysztof, 2018. "Comparison between Bayesian and information-theoretic model averaging: Fossil fuels prices example," Energy Economics, Elsevier, vol. 74(C), pages 208-251.
    14. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    15. Abdulsalam Abidemi Sikiru & Afees A. Salisu, 2022. "Assessing the hedging potential of gold and other precious metals against uncertainty due to epidemics and pandemics," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(4), pages 2199-2214, August.
    16. Wei, Yu & Liu, Jing & Lai, Xiaodong & Hu, Yang, 2017. "Which determinant is the most informative in forecasting crude oil market volatility: Fundamental, speculation, or uncertainty?," Energy Economics, Elsevier, vol. 68(C), pages 141-150.
    17. Yarovaya, Larisa & Brzeszczyński, Janusz & Goodell, John W. & Lucey, Brian & Lau, Chi Keung Marco, 2022. "Rethinking financial contagion: Information transmission mechanism during the COVID-19 pandemic," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).
    18. Drachal, Krzysztof, 2016. "Forecasting spot oil price in a dynamic model averaging framework — Have the determinants changed over time?," Energy Economics, Elsevier, vol. 60(C), pages 35-46.
    19. Robert A. Hill & Paulo M. M. Rodrigues, 2022. "Forgetting approaches to improve forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1356-1371, November.
    20. Chiah, Mardy & Zhong, Angel, 2020. "Trading from home: The impact of COVID-19 on trading volume around the world," Finance Research Letters, Elsevier, vol. 37(C).

    More about this item

    Keywords

    COVID-19; Stock markets; Economic factors; Dynamic model averaging;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • F32 - International Economics - - International Finance - - - Current Account Adjustment; Short-term Capital Movements
    • F41 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Open Economy Macroeconomics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

    Statistics

    Access and download statistics

    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:eee:ecofin:v:58:y:2021:i:c:s106294082100156x. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620163 .

    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.