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Predicting BRICS Stock Returns Using ARFIMA Models

Author

Listed:
  • Goodness C. Aye

    (Department of Economics, University of Pretoria)

  • Mehmet Balcilar

    (Department of Economics, Eastern Mediterranean University, Famagusta, North Cyprus,via Mersin 10, Turkey)

  • Rangan Gupta

    (Department of Economics, University of Pretoria)

  • Nicholas Kilimani

    (Department of Economics, University of Pretoria)

  • Amandine Nakumuryango

    (Department of Economics, University of Pretoria)

  • Siobhan Redford

    (Department of Economics, University of Pretoria)

Abstract
This paper examines the existence of long memory in daily stock market returns from Brazil, Russia, India, China, and South Africa (BRICS) countries and also attempts to shed light on the efficacy of Autoregressive Fractionally Integrated Moving Average (ARFIMA) models in predicting stock returns. We present evidence which suggests that ARFIMA models estimated using a variety of estimation procedures yield better forecasting results than the non-ARFIMA (AR, MA, ARMA and GARCH) models with regard to prediction of stock returns. These findings hold consistently the different countries whose economies differ in size, nature and sophistication.

Suggested Citation

  • Goodness C. Aye & Mehmet Balcilar & Rangan Gupta & Nicholas Kilimani & Amandine Nakumuryango & Siobhan Redford, 2012. "Predicting BRICS Stock Returns Using ARFIMA Models," Working Papers 201235, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:201235
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    2. Alia Afzal & Philipp Sibbertsen, 2023. "Long Memory, Spurious Memory: Persistence in Range-Based Volatility of Exchange Rates," Open Economies Review, Springer, vol. 34(4), pages 789-811, September.
    3. Donald A. Otieno & Rose W. Ngugi & Peter W. Muriu, 2019. "The impact of inflation rate on stock market returns: evidence from Kenya," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 43(1), pages 73-90, January.
    4. Tripathy, Naliniprava, 2022. "Long memory and volatility persistence across BRICS stock markets," Research in International Business and Finance, Elsevier, vol. 63(C).
    5. Abounoori, Abbas Ali & Naderi, Esmaeil & Gandali Alikhani, Nadiya & Amiri, Ashkan, 2013. "Financial Time Series Forecasting by Developing a Hybrid Intelligent System," MPRA Paper 45860, University Library of Munich, Germany.
    6. Hazem Krichene & Mhamed-Ali El-Aroui, 2018. "Agent-Based Simulation and Microstructure Modeling of Immature Stock Markets," Computational Economics, Springer;Society for Computational Economics, vol. 51(3), pages 493-511, March.
    7. Rafik Nazarian & Esmaeil Naderi & Nadiya G. Alikhani & Ashkan Amiri, 2014. "Long Memory Analysis: An Empirical Investigation," International Journal of Economics and Financial Issues, Econjournals, vol. 4(1), pages 16-26.
    8. Syriopoulos, Theodore & Makram, Beljid & Boubaker, Adel, 2015. "Stock market volatility spillovers and portfolio hedging: BRICS and the financial crisis," International Review of Financial Analysis, Elsevier, vol. 39(C), pages 7-18.
    9. Majid Delavari & Nadiya Gandali Alikhani & Esmaeil Naderi, 2013. "Do Dynamic Neural Networks Stand a Better Chance in Fractionally Integrated Process Forecasting?," International Journal of Economics and Financial Issues, Econjournals, vol. 3(2), pages 466-475.
    10. Diteboho Xaba & Ntebogang Dinah Moroke & Ishmael Rapoo, 2019. "Modeling Stock Market Returns of BRICS with a Markov-Switching Dynamic Regression Model," Journal of Economics and Behavioral Studies, AMH International, vol. 11(3), pages 10-22.
    11. Salisu, Afees A. & Gupta, Rangan, 2021. "Oil shocks and stock market volatility of the BRICS: A GARCH-MIDAS approach," Global Finance Journal, Elsevier, vol. 48(C).
    12. Mtiraoui, Amine & Boubaker, Heni & BelKacem, Lotfi, 2023. "A hybrid approach for forecasting bitcoin series," Research in International Business and Finance, Elsevier, vol. 66(C).
    13. Gadhoum, Anouar & Masih, Mansur, 2018. "Emerging market equities and US policy uncertainty: evidence from Malaysia based on ARDL," MPRA Paper 105469, University Library of Munich, Germany.
    14. Boryana Bogdanova & Ivan Ivanov, 2016. "A wavelet-based approach to the analysis and modelling of financial time series exhibiting strong long-range dependence: the case of Southeast Europe," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(4), pages 655-673, March.
    15. Momin, Ebaad & Masih, Mansur, 2015. "Do US policy uncertainty, leveraging costs and global risk aversion impact emerging market equities? An application of bounds testing approach to the BRICS," MPRA Paper 65834, University Library of Munich, Germany.

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    More about this item

    Keywords

    Fractional integration; long-memory; stock returns; long-horizon prediction; ARFIMA; BRICS;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • 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

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