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The predictability of asset returns in the BRICS countries: a nonparametric approach

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  • Muteba Mwamba, John Weirstrass
  • Webb, Daniel

Abstract

One of the earliest and most enduring questions of financial econometrics is the predictability of financial asset prices. In this article, stock market data from Brazil, Russia, India, China and South Africa are used to assess the out-of-sample performance of the ARMA(1,1)-GARCH(1,1) and Non-parametric kernel (Epanechnikov) regression models. The results reveal that the non-parametric kernel regression model outperforms its parametric rival based on the predicted mean square error (PMSE), Diebold-Mariano criterion, Mean-Absolute Deviation (MAD) and Variance statistics. These results confirm those found previously by other researchers whereby non-parametric forecasting models outperform parametric models in the short-term forecasting horizon.

Suggested Citation

  • Muteba Mwamba, John Weirstrass & Webb, Daniel, 2014. "The predictability of asset returns in the BRICS countries: a nonparametric approach," MPRA Paper 72880, University Library of Munich, Germany, revised 15 Nov 2014.
    • Handle: RePEc:pra:mprapa:72880
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    References listed on IDEAS

    as
    1. Lumengo Bonga‐Bonga & Muteba Mwamba, 2011. "The Predictability Of Stock Market Returns In South Africa: Parametric Vs. Non‐Parametric Methods," South African Journal of Economics, Economic Society of South Africa, vol. 79(3), pages 301-311, September.
    2. Keith Jefferis & Pako Thupayagale, 2008. "Long Memory In Southern African Stock Markets," South African Journal of Economics, Economic Society of South Africa, vol. 76(3), pages 384-398, September.
    3. 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.
    4. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    5. Fama, Eugene F. & Schwert, G. William, 1977. "Asset returns and inflation," Journal of Financial Economics, Elsevier, vol. 5(2), pages 115-146, November.
    6. Pesaran, M Hashem & Timmermann, Allan, 1995. "Predictability of Stock Returns: Robustness and Economic Significance," Journal of Finance, American Finance Association, vol. 50(4), pages 1201-1228, September.
    7. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-547, August.
    8. 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..
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    More about this item

    Keywords

    kernel regression; forecasting; non-parametric; BRICS markets;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications

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