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Predicting BRICS Stock Returns Using ARFIMA Models

Listed author(s):
  • 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)

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.

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Paper provided by University of Pretoria, Department of Economics in its series Working Papers with number 201235.

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Length: 24 pages
Date of creation: Dec 2012
Handle: RePEc:pre:wpaper:201235
Contact details of provider: Postal:
PRETORIA, 0002

Phone: (+2712) 420 2413
Fax: (+2712) 362-5207
Web page: http://www.up.ac.za/economics

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  2. Diebold, Francis X & Rudebusch, Glenn D, 1991. "Is Consumption Too Smooth? Long Memory and the Deaton Paradox," The Review of Economics and Statistics, MIT Press, vol. 73(1), pages 1-9, February.
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  27. 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, 09.
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