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Modelling Stock Returns Volatility In Nigeria Using GARCH Models

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  • Emenike, Kalu O.

Abstract

There is quite an extensive literature documenting the behaviour of stock returns volatility in both developed and emerging stock markets, but such studies are scanty for the Nigerian Stock Exchange (NSE). Modelling volatility is an important element in pricing equity, risk management and portfolio management. For these reasons, this paper investigates the behaviour of stock return volatility of the Nigerian Stock Exchange returns using GARCH (1,1) and the GJR-GARCH(1,1) models assuming the Generalized Error Distribution (GED). Monthly All Share Indices of the NSE from January 1999, to December 2008, provided the empirical sample for investigating volatility persistence and asymmetric properties of the series. The results of GARCH (1,1) model indicate evidence of volatility clustering in the NSE return series. Also, the results of the GJR-GARCH (1,1) model show the existence of leverage effects in the series. Finally, the Generalized Error Distribution (GED) shape test reveals leptokurtic returns distribution. Overall results from this study provide evidence to show volatility persistence, fat-tail distribution, and leverage effects for the Nigeria stock returns data.

Suggested Citation

  • Emenike, Kalu O., 2010. "Modelling Stock Returns Volatility In Nigeria Using GARCH Models," MPRA Paper 22723, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:22723
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    References listed on IDEAS

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    Cited by:

    1. Lakshmi Padmakumari & S Maheswaran, 2016. "A Regression Based Approach to Capturing the Level Dependence in the Volatility of Stock Returns," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 6(12), pages 706-718, December.
    2. Aastha KHERA & Dr. Miklesh Prasad YADAV, 2020. "Predicting the volatility in stock return of emerging economy: An empirical approach," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(4(625), W), pages 233-244, Winter.
    3. Emenike Kalu O., 2017. "Weak-form Efficiency After Global Financial Crisis: Emerging Stock Market Evidence," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 16(1), pages 90-113, April.
    4. Yaya, OlaOluwa S & Adekoya, Oluwasegun B. & Adesiyan, Femi, 2020. "The Persistence of Stock Market Returns during the Presidential elections in Nigeria," MPRA Paper 99390, University Library of Munich, Germany.
    5. Yaya, OlaOluwa S. & Gil-Alana, Luis A., 2014. "The persistence and asymmetric volatility in the Nigerian stock bull and bear markets," Economic Modelling, Elsevier, vol. 38(C), pages 463-469.
    6. Charan Raj Chimrani & Farhan Ahmed & Vinesh Kumar Panjwani, 2018. "Modeling Sectoral Stock Indexes Volatility: Empirical Evidence from Pakistan Stock Exchange," International Journal of Economics and Financial Issues, Econjournals, vol. 8(2), pages 319-324.
    7. Nageri Kamaldeen Ibraheem, 2019. "Evaluating Good and Bad News During Pre and Post Financial Meltdown: Nigerian Stock Market Evidence," Studia Universitatis Babeș-Bolyai Oeconomica, Sciendo, vol. 64(3), pages 1-22, December.
    8. Aluko Olufemi Adewale & Adeyeye Patrick Olufemi & Migiro Stephen Oseko, 2017. "Modelling Volatility Persistence and Asymmetry with Structural Break: Evidence from the Nigerian Stock Market," Journal of Economics and Behavioral Studies, AMH International, vol. 8(6), pages 153-160.

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    More about this item

    Keywords

    Modeling; Volatility; Stock Returns; GARCH Models; Nigerian Stock Exchange;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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