Predicting BRICS stock returns using ARFIMA models

Show simple item record Aye, Goodness Chioma Balcilar, Mehmet Gupta, Rangan Kilimani, Nicholas Nakumuryango, Amandine Redford, Siobhan 2014-07-31T08:19:54Z 2014-09
dc.description.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. en_US
dc.description.embargo 2016-02-27
dc.description.librarian hb2014 en_US
dc.description.uri en_US
dc.identifier.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. en_US
dc.identifier.issn 0960-3107 (print)
dc.identifier.issn 1466-4305 (online)
dc.identifier.other 10.1080/09603107.2014.924297
dc.language.iso en en_US
dc.publisher Routledge en_US
dc.rights © Taylor and Francis. This is an electronic version of an article published in Applied financial economics, vol. 24, no. 17, pp. 1159-1166, 2014. doi : 10.1080/09603107.2014.924297. Applied financial economics is available online at : en_US
dc.subject Fractional integration en_US
dc.subject Long-memory en_US
dc.subject Stock returns en_US
dc.subject Long-horizon prediction en_US
dc.subject Brazil, Russia, India, China and South Africa (BRICS) en_US
dc.subject Autoregressive fractionally integrated moving average (ARFIMA) en_US
dc.subject ARFIMA models en_US
dc.subject BRICS countries en_US
dc.title Predicting BRICS stock returns using ARFIMA models en_US
dc.type Postprint Article en_US

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