Predicting BRICS Stock Returns Using ARFIMA Models
AbstractThis 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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Bibliographic InfoPaper provided by University of Pretoria, Department of Economics in its series Working Papers with number 201235.
Length: 24 pages
Date of creation: Dec 2012
Date of revision:
Fractional integration; long-memory; stock returns; long-horizon prediction; ARFIMA; BRICS;
Other versions of this item:
- Goodness C. Aye & Mehmet Balcilar & Rangan Gupta & Nicholas Kilimani & Amandine Nakumuryango & Siobhan Redford, 2014. "Predicting BRICS stock returns using ARFIMA models," Applied Financial Economics, Taylor & Francis Journals, vol. 24(17), pages 1159-1166, September.
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
This paper has been announced in the following NEP Reports:
- NEP-AFR-2013-02-03 (Africa)
- NEP-ALL-2013-02-03 (All new papers)
- NEP-CIS-2013-02-03 (Confederation of Independent States)
- NEP-FOR-2013-02-03 (Forecasting)
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