Predicting BRICS stock returns using ARFIMA models

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 econo- mies differ in size, nature and sophistication.

Keywords

Fractional integration, Long memory, Stock returns, Long-horizon prediction, ARFIMA, BRICS

Citation

Goodness C. Aye, Mehmet Balcilar, Rangan Gupta, Nicholas Kilimani, Amandine Nakumuryango & Siobhan Redford (2014) Predicting BRICS stock returns using ARFIMA models, Applied Financial Economics, 24:17, 1159-1166, DOI: 10.1080/09603107.2014.924297

URI

https://nru.uncst.go.ug/handle/123456789/8027

Collections

Social Sciences

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