Predicting BRICS stock returns using ARFIMA models
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Date
Authors
Aye, Goodness Chioma
Balcilar, Mehmet
Gupta, Rangan
Kilimani, Nicholas
Nakumuryango, Amandine
Redford, Siobhan
Journal Title
Journal ISSN
Volume Title
Publisher
Routledge
Abstract
This article 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 for the different countries whose economies differ in size, nature and sophistication.
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Keywords
Fractional integration, Long-memory, Stock returns, Long-horizon prediction, Brazil, Russia, India, China and South Africa (BRICS), Autoregressive fractionally integrated moving average (ARFIMA), ARFIMA models, BRICS countries
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Citation
Aye, GC, Balcilar, M, Gupta, R, Kilimani, N, Nakumuryango, A & Redford, S 2014, 'Predicting BRICS stock returns using ARFIMA models', Applied Financial Economics, vol. 24, no. 17, pp. 1159-1166.