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The predictability of stock market volatility in emerging economies: Relative roles of local, regional, and global business cycles

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  • Elie Bouri
  • Riza Demirer
  • Rangan Gupta
  • Xiaojin Sun

Abstract

This paper explores the role of business cycle proxies, measured by the output gap at the global, regional, and local levels, as potential predictors of stock market volatility in the emerging BRICS nations. We observe that the emerging BRICS nations display a rather heterogeneous pattern when it comes to the relative role of idiosyncratic factors as a predictor of stock market volatility. While domestic output gap is found to capture significant predictive information for India and China particularly, the business cycles associated with emerging economies and the world in general are strongly important for the BRIC countries and weakly for South Africa, especially in the postglobal financial crisis era. The findings suggest that despite the increase in the financial integration of world capital markets, emerging economies can still bear significant exposures to idiosyncratic risk factors, an issue of high importance for the profitability of global diversification strategies.

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  • Elie Bouri & Riza Demirer & Rangan Gupta & Xiaojin Sun, 2020. "The predictability of stock market volatility in emerging economies: Relative roles of local, regional, and global business cycles," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 957-965, September.
  • Handle: RePEc:wly:jforec:v:39:y:2020:i:6:p:957-965
    DOI: 10.1002/for.2672
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    2. Plakandaras, Vasilios & Gupta, Rangan & Balcilar, Mehmet & Ji, Qiang, 2022. "Evolving United States stock market volatility: The role of conventional and unconventional monetary policies," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    3. Afees A. Salisu & Rangan Gupta, 2021. "Commodity Prices and Forecastability of South African Stock Returns Over a Century: Sentiments versus Fundamentals," Working Papers 202144, University of Pretoria, Department of Economics.
    4. Plastun, Alex & Bouri, Elie & Gupta, Rangan & Ji, Qiang, 2022. "Price effects after one-day abnormal returns in developed and emerging markets: ESG versus traditional indices," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    5. Gupta, Rangan & Nel, Jacobus & Nielsen, Joshua, 2023. "US monetary policy and BRICS stock market bubbles," Finance Research Letters, Elsevier, vol. 51(C).
    6. David Gabauer & Rangan Gupta & Sayar Karmakar & Joshua Nielsen, 2022. "Stock Market Bubbles and the Forecastability of Gold Returns (and Volatility)," Working Papers 202228, University of Pretoria, Department of Economics.
    7. Lu, Xinjie & Ma, Feng & Li, Haibo & Wang, Jianqiong, 2023. "INE oil futures volatility prediction: Exchange rates or international oil futures volatility?," Energy Economics, Elsevier, vol. 126(C).
    8. Mohammad Sahabuddin & Md. Aminul Islam & Mosab I. Tabash & Suhaib Anagreh & Rozina Akter & Md. Mizanur Rahman, 2022. "Co-Movement, Portfolio Diversification, Investors’ Behavior and Psychology: Evidence from Developed and Emerging Countries’ Stock Markets," JRFM, MDPI, vol. 15(8), pages 1-15, July.
    9. Mehmet Sahiner & David G. McMillan & Dimos Kambouroudis, 2023. "Do artificial neural networks provide improved volatility forecasts: Evidence from Asian markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 47(3), pages 723-762, September.
    10. 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.
    11. Zhang, Yulian & Hamori, Shigeyuki, 2022. "A connectedness analysis among BRICS’s geopolitical risks and the US macroeconomy," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 182-203.
    12. Maria Ghani & Usman Ghani, 2024. "Economic Policy Uncertainty and Emerging Stock Market Volatility," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 31(1), pages 165-181, March.

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    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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