IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/593.html
   My bibliography  Save this paper

Modelling and Forecasting Volatility of Returns on the Ghana Stock Exchange Using GARCH Models

Author

Listed:
  • Frimpong, Joseph Magnus
  • Oteng-Abayie, Eric Fosu

Abstract

This paper models and forecasts volatility (conditional variance) on the Ghana Stock Exchange using a random walk (RW), GARCH(1,1), EGARCH(1,1), and TGARCH(1,1) models. The unique ‘three days a week’ Databank Stock Index (DSI) is used to study the dynamics of the Ghana stock market volatility over a 10-year period. The competing volatility models were estimated and their specification and forecast performance compared with each other, using AIC and LL information criteria and BDS nonlinearity diagnostic checks. The DSI exhibits the stylized characteristics such as volatility clustering, leptokurtosis and asymmetry effects associated with stock market returns on more advanced stock markets. The random walk hypothesis is rejected for the DSI. Overall, the GARCH (1,1) model outperformed the other models under the assumption that the innovations follow a normal distribution.

Suggested Citation

  • Frimpong, Joseph Magnus & Oteng-Abayie, Eric Fosu, 2006. "Modelling and Forecasting Volatility of Returns on the Ghana Stock Exchange Using GARCH Models," MPRA Paper 593, University Library of Munich, Germany, revised 07 Oct 2006.
  • Handle: RePEc:pra:mprapa:593
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/593/1/MPRA_paper_593.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dimson, Elroy & Marsh, Paul, 1990. "Volatility forecasting without data-snooping," Journal of Banking & Finance, Elsevier, vol. 14(2-3), pages 399-421, August.
    2. 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.
    3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    4. Paul Alagidede & Theodore Panagiotidis, 2006. "Calendar Anomalies in an Emerging African Market: Evidence from the Ghana Stock Exchange," Discussion Paper Series 2006_13, Department of Economics, Loughborough University, revised Jun 2006.
    5. Chris Brooks & Simon Burke, 2003. "Information criteria for GARCH model selection," The European Journal of Finance, Taylor & Francis Journals, vol. 9(6), pages 557-580.
    6. Brooks,Chris, 2019. "Introductory Econometrics for Finance," Cambridge Books, Cambridge University Press, number 9781108436823, November.
    7. Theodore Panagiotidis, 2002. "Testing the assumption of Linearity," Economics Bulletin, AccessEcon, vol. 3(29), pages 1-9.
    8. G. Ogum & F. Beer & G. Nouyrigat, 2005. "Emerging Equity market Volatility: An empirical Investigation of Emergent market: Kenya Nigeria," Post-Print halshs-00103119, HAL.
    9. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78, World Scientific Publishing Co. Pte. Ltd..
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Ananda Chatterjee & Hrisav Bhowmick & Jaydip Sen, 2022. "Stock Volatility Prediction using Time Series and Deep Learning Approach," Papers 2210.02126, arXiv.org.
    3. Emenike, Kalu O., 2010. "Modelling Stock Returns Volatility In Nigeria Using GARCH Models," MPRA Paper 22723, University Library of Munich, Germany.
    4. George Amfo-Antiri & Edward Quansah, 2017. "Cointegration of Stock Prices and Domestic Portfolio Diversification Opportunities: Evidence from the Ghana Stock Exchange," Applied Economics and Finance, Redfame publishing, vol. 4(5), pages 78-93, September.
    5. Ntebogang Dinah Moroke, 2015. "An Optimal Generalized Autoregressive Conditional Heteroscedasticity Model for Forecasting the South African Inflation Volatility," Journal of Economics and Behavioral Studies, AMH International, vol. 7(4), pages 134-149.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Caroline Michere Ndei & Stephen Muchina & Kennedy Waweru, 2019. "Modeling stock market return volatility in the presence of structural breaks: Evidence from Nairobi Securities Exchange, Kenya," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 8(5), pages 156-171, September.
    2. 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).
    3. David McMillan & Alan Speight & Owain Apgwilym, 2000. "Forecasting UK stock market volatility," Applied Financial Economics, Taylor & Francis Journals, vol. 10(4), pages 435-448.
    4. Ekong, Christopher N. & Onye, Kenneth U., 2017. "Application of Garch Models to Estimate and Predict Financial Volatility of Daily Stock Returns in Nigeria," MPRA Paper 88309, University Library of Munich, Germany.
    5. E. Ramos-P'erez & P. J. Alonso-Gonz'alez & J. J. N'u~nez-Vel'azquez, 2020. "Forecasting volatility with a stacked model based on a hybridized Artificial Neural Network," Papers 2006.16383, arXiv.org, revised Aug 2020.
    6. Novkovska, Blagica & Serafimovic, Gordana, 2018. "Recognizing The Vulnerability Of Generation Z To Economic And Social Risks," UTMS Journal of Economics, University of Tourism and Management, Skopje, Macedonia, vol. 9(1), pages 29-37.
    7. B M, Lithin & chakraborty, Suman & iyer, Vishwanathan & M N, Nikhil & ledwani, Sanket, 2022. "Modeling asymmetric sovereign bond yield volatility with univariate GARCH models: Evidence from India," MPRA Paper 117067, University Library of Munich, Germany, revised 05 Jan 2023.
    8. Gonzalo Cortazar & Alejandro Bernales & Diether Beuermann, 2005. "Methodology and Implementation of Value-at-Risk Measures in Emerging Fixed-Income Markets with Infrequent Trading," Finance 0512030, University Library of Munich, Germany.
    9. Issler, João Victor, 1999. "Estimating and forecasting the volatility of Brazilian finance series using arch models (Preliminary Version)," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 347, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    10. Long H. Vo, 2017. "Estimating Financial Volatility with High-Frequency Returns," Journal of Finance and Economics Research, Geist Science, Iqra University, Faculty of Business Administration, vol. 2(2), pages 84-114, October.
    11. Carol Alexander & Emese Lazar & Silvia Stanescu, 2011. "Analytic Approximations to GARCH Aggregated Returns Distributions with Applications to VaR and ETL," ICMA Centre Discussion Papers in Finance icma-dp2011-08, Henley Business School, University of Reading.
    12. Debabrata Mukhopadhyay & Nityananda Sarkar, 2013. "Stock Returns Under Alternative Volatility and Distributional Assumptions: The Case for India," International Econometric Review (IER), Econometric Research Association, vol. 5(1), pages 1-19, April.
    13. Stavroyiannis, S. & Makris, I. & Nikolaidis, V. & Zarangas, L., 2012. "Econometric modeling and value-at-risk using the Pearson type-IV distribution," International Review of Financial Analysis, Elsevier, vol. 22(C), pages 10-17.
    14. Ricardo Alverola, 2007. "Estimating Volatility Returns Using ARCH Models. An Empirical Case: The Spanish Energy Market," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 66, pages 251-276, Enero-Jun.
    15. Abdou Kâ Diongue & Dominique Guegan, 2008. "The k-factor Gegenbauer asymmetric Power GARCH approach for modelling electricity spot price dynamics," Post-Print halshs-00259225, HAL.
    16. Claudeci Da Silva & Hugo Agudelo Murillo & Joaquim Miguel Couto, 2014. "Early Warning Systems: Análise De Ummodelo Probit De Contágio De Crise Dos Estados Unidos Para O Brasil(2000-2010)," Anais do XL Encontro Nacional de Economia [Proceedings of the 40th Brazilian Economics Meeting] 110, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    17. Thilo A. Schmitt & Rudi Schafer & Holger Dette & Thomas Guhr, 2015. "Quantile Correlations: Uncovering temporal dependencies in financial time series," Papers 1507.04990, arXiv.org.
    18. Christophe Chorro & Dominique Guegan & Florian Ielpo, 2008. "Option Pricing under GARCH models with Generalized Hyperbolic innovations (I) : Methodology," Post-Print halshs-00281585, HAL.
    19. 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.
    20. Sylvia J. Soltyk & Felix Chan, 2023. "Modeling time‐varying higher‐order conditional moments: A survey," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 33-57, February.

    More about this item

    Keywords

    Ghana Stock Exchange; developing financial markets; volatility; GARCH model;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:593. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.