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Global economic uncertainty and the Chinese stock market: Assessing the impacts of global indicators

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  • Zhang, Lixia
  • Bai, Jiancheng
  • Zhang, Yueyan
  • Cui, Can

Abstract

We comparatively assess the influence of global economic uncertainty measures on Chinese stock market volatility. Using a model based on generalized autoregressive conditional heteroskedasticity and mixed-data sampling, the results show that the global economic policy uncertainty index, the geopolitical risk index, and the global economic condition index all significantly influence the long-term volatility of China’s equity market. We highlight which of these measures has the most explanatory power under differing contexts. As uncertainty measures have wide applicability, investors, policymakers, and academicians will be quite interested in our results.

Suggested Citation

  • Zhang, Lixia & Bai, Jiancheng & Zhang, Yueyan & Cui, Can, 2023. "Global economic uncertainty and the Chinese stock market: Assessing the impacts of global indicators," Research in International Business and Finance, Elsevier, vol. 65(C).
    • Handle: RePEc:eee:riibaf:v:65:y:2023:i:c:s0275531923000752
    DOI: 10.1016/j.ribaf.2023.101949
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    as
    1. 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).
    2. Dario Caldara & Matteo Iacoviello, 2022. "Measuring Geopolitical Risk," American Economic Review, American Economic Association, vol. 112(4), pages 1194-1225, April.
    3. Sébastien Laurent & Jeroen V. K. Rombouts & Francesco Violante, 2012. "On the forecasting accuracy of multivariate GARCH models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(6), pages 934-955, September.
    4. Zhang, Lixia & Luo, Qin & Guo, Xiaozhu & Umar, Muhammad, 2022. "Medium-term and long-term volatility forecasts for EUA futures with country-specific economic policy uncertainty indices," Resources Policy, Elsevier, vol. 77(C).
    5. Salisu, Afees A. & Gupta, Rangan & Demirer, Riza, 2022. "Global financial cycle and the predictability of oil market volatility: Evidence from a GARCH-MIDAS model," Energy Economics, Elsevier, vol. 108(C).
    6. Salisu, Afees A. & Gupta, Rangan & Bouri, Elie & Ji, Qiang, 2020. "The role of global economic conditions in forecasting gold market volatility: Evidence from a GARCH-MIDAS approach," Research in International Business and Finance, Elsevier, vol. 54(C).
    7. Christiane Baumeister & Dimitris Korobilis & Thomas K. Lee, 2022. "Energy Markets and Global Economic Conditions," The Review of Economics and Statistics, MIT Press, vol. 104(4), pages 828-844, October.
    8. Cai, Huifen & Boateng, Agyenim & Guney, Yilmaz, 2019. "Host country institutions and firm-level R&D influences: An analysis of European Union FDI in China," Research in International Business and Finance, Elsevier, vol. 47(C), pages 311-326.
    9. Xiuzhen, Xie & Zheng, Wenxiu & Umair, Muhammad, 2022. "Testing the fluctuations of oil resource price volatility: A hurdle for economic recovery," Resources Policy, Elsevier, vol. 79(C).
    10. Baumeister, Christiane & Guérin, Pierre, 2021. "A comparison of monthly global indicators for forecasting growth," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1276-1295.
    11. Si, Deng-Kui & Wan, Shen & Li, Xiao-Lin & Kong, Dongmin, 2022. "Economic policy uncertainty and shadow banking: Firm-level evidence from China," Research in International Business and Finance, Elsevier, vol. 63(C).
    12. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    13. Guo, Yangli & Ma, Feng & Li, Haibo & Lai, Xiaodong, 2022. "Oil price volatility predictability based on global economic conditions," International Review of Financial Analysis, Elsevier, vol. 82(C).
    14. You, Yu & Liu, Xiaochun, 2020. "Forecasting short-run exchange rate volatility with monetary fundamentals: A GARCH-MIDAS approach," Journal of Banking & Finance, Elsevier, vol. 116(C).
    15. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    16. Hong, Yanran & Wang, Lu & Liang, Chao & Umar, Muhammad, 2022. "Impact of financial instability on international crude oil volatility: New sight from a regime-switching framework," Resources Policy, Elsevier, vol. 77(C).
    17. Sheng, Xin & Marfatia, Hardik A. & Gupta, Rangan & Ji, Qiang, 2023. "The non-linear response of US state-level tradable and non-tradable inflation to oil shocks: The role of oil-dependence," Research in International Business and Finance, Elsevier, vol. 64(C).
    18. Yan, Xiang & Bai, Jiancheng & Li, Xiafei & Chen, Zhonglu, 2022. "Can dimensional reduction technology make better use of the information of uncertainty indices when predicting volatility of Chinese crude oil futures?," Resources Policy, Elsevier, vol. 75(C).
    19. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    20. Gupta, Rangan & Sheng, Xin & Pierdzioch, Christian & Ji, Qiang, 2021. "Disaggregated oil shocks and stock-market tail risks: Evidence from a panel of 48 economics," Research in International Business and Finance, Elsevier, vol. 58(C).
    21. Lv, Wendai & Wu, Qian, 2022. "Global economic conditions index and oil price predictability," Finance Research Letters, Elsevier, vol. 48(C).
    22. Balcilar, Mehmet & Gupta, Rangan & Segnon, Mawuli, 2016. "The role of economic policy uncertainty in predicting U.S. recessions: A mixed-frequency Markov-switching vector autoregressive approach," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 10, pages 1-20.
    23. Chu, Pyung Kun & Hoff, Kristian & Molnár, Peter & Olsvik, Magnus, 2022. "Crude oil: Does the futures price predict the spot price?," Research in International Business and Finance, Elsevier, vol. 60(C).
    24. Robert F. Engle & Eric Ghysels & Bumjean Sohn, 2013. "Stock Market Volatility and Macroeconomic Fundamentals," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 776-797, July.
    25. Ashok, Shruti & Corbet, Shaen & Dhingra, Deepika & Goodell, John W. & Kumar, Satish & Yadav, Miklesh Prasad, 2022. "Are energy markets informationally smarter than equity markets? Evidence from the COVID-19 experience," Finance Research Letters, Elsevier, vol. 47(PB).
    26. Corbet, Shaen & Goodell, John W. & Günay, Samet, 2020. "Co-movements and spillovers of oil and renewable firms under extreme conditions: New evidence from negative WTI prices during COVID-19," Energy Economics, Elsevier, vol. 92(C).
    27. Xu, Yan & Wang, Xinyu & Liu, Hening, 2021. "Quantile-based GARCH-MIDAS: Estimating value-at-risk using mixed-frequency information," Finance Research Letters, Elsevier, vol. 43(C).
    28. Li, Weiping & Mei, Feng, 2020. "Asset returns in deep learning methods: An empirical analysis on SSE 50 and CSI 300," Research in International Business and Finance, Elsevier, vol. 54(C).
    29. Goodell, John W. & McGee, Richard J. & McGroarty, Frank, 2020. "Election uncertainty, economic policy uncertainty and financial market uncertainty: A prediction market analysis," Journal of Banking & Finance, Elsevier, vol. 110(C).
    30. Shahzad, Umer & Ferraz, Diogo & Nguyen, Huu-Huan & Cui, Lianbiao, 2022. "Investigating the spill overs and connectedness between financial globalization, high-tech industries and environmental footprints: Fresh evidence in context of China," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    31. Lang, Qiaoqi & Lu, Xinjie & Ma, Feng & Huang, Dengshi, 2022. "Oil futures volatility predictability: Evidence based on Twitter-based uncertainty," Finance Research Letters, Elsevier, vol. 47(PA).
    32. Nonejad, Nima, 2021. "The price of crude oil and (conditional) out-of-sample predictability of world industrial production," Journal of Commodity Markets, Elsevier, vol. 23(C).
    33. Jose Arreola Hernandez & Mazin A.M. Al Janabi, 2020. "Forecasting of dependence, market, and investment risks of a global index portfolio," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 512-532, April.
    34. Goodell, John W. & Vähämaa, Sami, 2013. "US presidential elections and implied volatility: The role of political uncertainty," Journal of Banking & Finance, Elsevier, vol. 37(3), pages 1108-1117.
    35. Hossein Asgharian & Ai Jun Hou & Farrukh Javed, 2013. "The Importance of the Macroeconomic Variables in Forecasting Stock Return Variance: A GARCH‐MIDAS Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(7), pages 600-612, November.
    36. Chen, Jian & Jiang, Fuwei & Li, Hongyi & Xu, Weidong, 2016. "Chinese stock market volatility and the role of U.S. economic variables," Pacific-Basin Finance Journal, Elsevier, vol. 39(C), pages 70-83.
    37. Goodell, John W., 2020. "COVID-19 and finance: Agendas for future research," Finance Research Letters, Elsevier, vol. 35(C).
    38. Conlon, Thomas & Corbet, Shaen & McGee, Richard J., 2020. "Are cryptocurrencies a safe haven for equity markets? An international perspective from the COVID-19 pandemic," Research in International Business and Finance, Elsevier, vol. 54(C).
    39. Wang, Kai-Hua & Umar, Muhammad & Akram, Rabia & Caglar, Ersin, 2021. "Is technological innovation making world "Greener"? An evidence from changing growth story of China," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    40. Díaz, Fernando & Henríquez, Pablo A. & Winkelried, Diego, 2022. "Stock market volatility and the COVID-19 reproductive number," Research in International Business and Finance, Elsevier, vol. 59(C).
    41. Ali, Fahad & Sensoy, Ahmet & Goodell, John W., 2023. "Identifying diversifiers, hedges, and safe havens among Asia Pacific equity markets during COVID-19: New results for ongoing portfolio allocation," International Review of Economics & Finance, Elsevier, vol. 85(C), pages 744-792.
    42. Wang, Yudong & Ma, Feng & Wei, Yu & Wu, Chongfeng, 2016. "Forecasting realized volatility in a changing world: A dynamic model averaging approach," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 136-149.
    43. Li, Tao & Ma, Feng & Zhang, Xuehua & Zhang, Yaojie, 2020. "Economic policy uncertainty and the Chinese stock market volatility: Novel evidence," Economic Modelling, Elsevier, vol. 87(C), pages 24-33.
    44. Wang, Lu & Ma, Feng & Liu, Jing & Yang, Lin, 2020. "Forecasting stock price volatility: New evidence from the GARCH-MIDAS model," International Journal of Forecasting, Elsevier, vol. 36(2), pages 684-694.
    45. Chen, Wang & Ma, Feng & Wei, Yu & Liu, Jing, 2020. "Forecasting oil price volatility using high-frequency data: New evidence," International Review of Economics & Finance, Elsevier, vol. 66(C), pages 1-12.
    46. Liang, Chao & Umar, Muhammad & Ma, Feng & Huynh, Toan L.D., 2022. "Climate policy uncertainty and world renewable energy index volatility forecasting," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    47. Sui, Lu & Sun, Lijuan, 2016. "Spillover effects between exchange rates and stock prices: Evidence from BRICS around the recent global financial crisis," Research in International Business and Finance, Elsevier, vol. 36(C), pages 459-471.
    48. Li, Xiafei & Guo, Qiang & Liang, Chao & Umar, Muhammad, 2023. "Forecasting gold volatility with geopolitical risk indices," Research in International Business and Finance, Elsevier, vol. 64(C).
    49. Feng Ma & Xinjie Lu & Lu Wang & Julien Chevallier, 2021. "Global economic policy uncertainty and gold futures market volatility: Evidence from Markov regime‐switching GARCH‐MIDAS models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 1070-1085, September.
    50. Li, Xiafei & Liao, Yin & Lu, Xinjie & Ma, Feng, 2022. "An oil futures volatility forecast perspective on the selection of high-frequency jump tests," Energy Economics, Elsevier, vol. 116(C).
    51. Zhang, Yaojie & Wei, Yu & Zhang, Yi & Jin, Daxiang, 2019. "Forecasting oil price volatility: Forecast combination versus shrinkage method," Energy Economics, Elsevier, vol. 80(C), pages 423-433.
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    More about this item

    Keywords

    World economic situation; Global economic condition (GECON) index; Stock market volatility; Generalized autoregressive conditional heteroskedasticity (GARCH)–mixed-data sampling (MIDAS) modeling;
    All these keywords.

    JEL classification:

    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • F65 - International Economics - - Economic Impacts of Globalization - - - Finance
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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