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Mapping Risk–Return Linkages and Volatility Spillover in BRICS Stock Markets through the Lens of Linear and Non-Linear GARCH Models

Author

Listed:
  • Raj Kumar Singh

    (Department of Commerce, Himachal Pradesh University, Shimla 171005, Himachal Pradesh, India)

  • Yashvardhan Singh

    (Axtria Pvt. India Ltd., Pune 411057, Maharastra, India)

  • Satish Kumar

    (Department of Commerce, Himachal Pradesh University, Shimla 171005, Himachal Pradesh, India)

  • Ajay Kumar

    (GDC Tissa, Chamba 176316, Himachal Pradesh, India)

  • Waleed S. Alruwaili

    (Accounting, College of Business Administration, Northen Border University, Arar 91431, Saudi Arabia)

Abstract

This paper explores the influence of the risk–return relationship and volatility spillover on stock market returns of emerging economies, with a particular focus on the BRICS countries. This research is undertaken in a context where discussions on de-dollarization and the expansion of BRICS membership are gaining momentum, making it a novel and distinct exercise compared to prior studies. Utilizing econometric techniques to investigate daily market returns from 1 April 2008 to 31 March 2023, a period that witnessed major events like the global financial crisis, the COVID-19 pandemic, and the Russia–Ukraine conflict, linear and non-linear models like ARCH, GARCH, GARCH-M, EGARCH, and TGARCH, are employed to assess stock return volatility behaviour, assuming a Gaussian distribution of error terms. The diagnostic test confirms that the distribution is non-normal, stationary, and heteroscedastic. The key findings indicate a lack of the risk–return relationship across all BRICS stock markets, except for South Africa; a more pronounced effect of unpleasant news over pleasant news; a slow mean-reverting process in volatility; the EGARCH model is the best fit model as evidenced by a higher log likelihood and lower Akaike information criterion and Schwardz information criterion parameters; and finally, the presence of significant bidirectional and unidirectional spillover effects in the majority of instances. These findings are valuable for investors, regulators, and policymakers in enhancing returns and mitigating risk through portfolio diversification and informed decision making.

Suggested Citation

  • Raj Kumar Singh & Yashvardhan Singh & Satish Kumar & Ajay Kumar & Waleed S. Alruwaili, 2024. "Mapping Risk–Return Linkages and Volatility Spillover in BRICS Stock Markets through the Lens of Linear and Non-Linear GARCH Models," JRFM, MDPI, vol. 17(10), pages 1-26, September.
  • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:10:p:437-:d:1488936
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    References listed on IDEAS

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    1. Turgut Kısınbay, 2010. "Predictive ability of asymmetric volatility models at medium-term horizons," Applied Economics, Taylor & Francis Journals, vol. 42(30), pages 3813-3829.
    2. Engle, Robert F & Ng, Victor K, 1993. "Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-1778, December.
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