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Modelling and forecasting of Nigeria stock market volatility

Author

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  • Olufemi Samuel Adegboyo

    (Xi’an Jiaotong University)

  • Kiran Sarwar

    (Xi’an Jiaotong University)

Abstract

This study models and forecasts the volatility of the Nigerian Stock Exchange (NSE) using advanced econometric techniques, focusing on examining the asymmetric volatility and the leverage effect. Daily data from the NSE All Share Index spanning from 30th January, 2012, to 16th October, 2024 (3,176 days) are analysed using generalized autoregressive conditional heteroskedasticity family models, including EGARCH and GJR-GARCH, along with non-Gaussian distributions like the Student’s t-distribution. The findings reveal a significant leverage effect, where negative shocks impact stock prices more than positive ones, supporting asymmetric volatility theory. The study also identifies volatility clustering, where high-volatility periods are followed by continued volatility, further highlighting the persistence of market turbulence. Among the models tested, GJR-GARCH with the Student’s t-distribution performs best in forecasting volatility, providing superior fit and forecasting accuracy. These insights offer practical implications for investors and policymakers in managing risks in emerging markets, particularly during periods of high volatility.

Suggested Citation

  • Olufemi Samuel Adegboyo & Kiran Sarwar, 2025. "Modelling and forecasting of Nigeria stock market volatility," Future Business Journal, Springer, vol. 11(1), pages 1-13, December.
    • Handle: RePEc:spr:futbus:v:11:y:2025:i:1:d:10.1186_s43093-025-00536-4
    DOI: 10.1186/s43093-025-00536-4
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    1. Aliyev, Fuzuli & Ajayi, Richard & Gasim, Nijat, 2020. "Modelling asymmetric market volatility with univariate GARCH models: Evidence from Nasdaq-100," The Journal of Economic Asymmetries, Elsevier, vol. 22(C).
    2. Kwadwo Boateng Prempeh & Joseph Magnus Frimpong & Newman Amaning, 2023. "Determining the return volatility of the Ghana stock exchange before and during the COVID-19 pandemic using the exponential GARCH model," SN Business & Economics, Springer, vol. 3(1), pages 1-20, January.
    3. Abdullah H. Alenezy & Mohd Tahir Ismail & S. Al Wadi & Muhammad Tahir & Nawaf N. Hamadneh & Jamil J. Jaber & Waqar A. Khan & Basil K. Papadopoulos, 2021. "Forecasting Stock Market Volatility Using Hybrid of Adaptive Network of Fuzzy Inference System and Wavelet Functions," Journal of Mathematics, Hindawi, vol. 2021, pages 1-10, August.
    4. Joseph Paul Chunga & Ping YU, 2024. "The impact of external shocks on volatility persistence and market efficiency of the foreign exchange rate regime: evidence from Malawi," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.
    5. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    6. Massimiliano Caporin & Michele Costola, 2019. "Asymmetry and leverage in GARCH models: a News Impact Curve perspective," Applied Economics, Taylor & Francis Journals, vol. 51(31), pages 3345-3364, July.
    7. Brooks, C. & Clare, A. D. & Persand, G., 2000. "A word of caution on calculating market-based minimum capital risk requirements," Journal of Banking & Finance, Elsevier, vol. 24(10), pages 1557-1574, October.
    8. Vilasuso, Jon, 2002. "Forecasting exchange rate volatility," Economics Letters, Elsevier, vol. 76(1), pages 59-64, June.
    9. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    10. Naeem, Muhammad Abubakr, 2024. "Navigating median and extreme volatility in stock markets: Implications for portfolio strategies," International Review of Economics & Finance, Elsevier, vol. 95(C).
    11. 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.
    12. 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.
    13. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    14. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    15. Collin Chikwira & Jahed Iqbal Mohammed, 2023. "The Impact of the Stock Market on Liquidity and Economic Growth: Evidence of Volatile Market," Economies, MDPI, vol. 11(6), pages 1-19, May.
    16. Ngozi G. Emenogu & Monday Osagie Adenomon & Nwaze Obini Nweze, 2020. "On the volatility of daily stock returns of Total Nigeria Plc: evidence from GARCH models, value-at-risk and backtesting," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-25, December.
    17. Yuling Wang & Yunshuang Xiang & Xinyu Lei & Yucheng Zhou, 2022. "Volatility analysis based on GARCH-type models: Evidence from the Chinese stock market," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 35(1), pages 2530-2554, December.
    18. Carl H. Korkpoe & Peterson Owusu Junior, 2018. "Behaviour of Johannesburg Stock Exchange All Share Index Returns - An Asymmetric GARCH and News Impact Effects Approach," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 68(1), pages 26-42, January-M.
    19. Hakan Yıldırım & Festus Victor Bekun, 2023. "Predicting volatility of bitcoin returns with ARCH, GARCH and EGARCH models," Future Business Journal, Springer, vol. 9(1), pages 1-8, December.
    20. Jian Liu & Ziting Zhang & Lizhao Yan & Fenghua Wen, 2021. "Forecasting the volatility of EUA futures with economic policy uncertainty using the GARCH-MIDAS model," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-19, December.
    21. 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.
    22. Fahad Mostafa & Pritam Saha & Mohammad Rafiqul Islam & Nguyet Nguyen, 2021. "GJR-GARCH Volatility Modeling under NIG and ANN for Predicting Top Cryptocurrencies," JRFM, MDPI, vol. 14(9), pages 1-22, September.
    23. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Keywords

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    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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