Predicting BRICS stock returns using ARFIMA models

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dc.contributor.authorAye, Goodness C.
dc.contributor.authorBalcilar, Mehmet
dc.contributor.authorGupta, Rangan
dc.contributor.authorKilimani, Nicholas
dc.contributor.authorNakumuryango, Amandine
dc.contributor.authorRedford, Siobhan
dc.date.accessioned2023-02-28T16:41:15Z
dc.date.available2023-02-28T16:41:15Z
dc.date.issued2014
dc.description.abstractThis 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.en_US
dc.identifier.citationGoodness 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: 10.1080/09603107.2014.924297en_US
dc.identifier.other10.1080/09603107.2014.924297
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/8027
dc.language.isoenen_US
dc.publisherApplied Financial Economicsen_US
dc.subjectFractional integrationen_US
dc.subjectLong memoryen_US
dc.subjectStock returnsen_US
dc.subjectLong-horizon predictionen_US
dc.subjectARFIMAen_US
dc.subjectBRICSen_US
dc.titlePredicting BRICS stock returns using ARFIMA modelsen_US
dc.typeArticleen_US
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