benchmark model DJIM forecasting methods out-of-sample forecasts
This study employs 14 global economic and financial variables to predict the return of the Islamic stock market as identified by the Dow Jones Islamic Stock Market (DJIM). It implements alternative forecasting methods and allows for nonlinearity in the multivariate predictive regressions by estimating time-varying parameter models. All the methods fail to forecast the returns of the Sharia-based DJIM index over the out-of-sample period. The forecasts are weak at best, with only four predictors, the 3-month Treasury bill rate, inflation, oil price and return on the S&P500 Index, outperforming the benchmark autoregressive model of order one. The study suggests that the DJIM return is best predicted by an autocorrelation(1) model, and that future research should aim at analysing whether the performance of the linear autoregressive model can be improved by using nonlinear methods.
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Details
Title
Can the Sharia-based Islamic stock market returns be forecasted using large number of predictors and models?
Creators
Rangan Gupta - University of Pretoria
Shawkat Hammoudeh - Drexel University
Beatrice D. Simo-Kengne - University of Pretoria
Soodabeh Sarafrazi - Drexel University
Richa Gupta - Pharmacology and Physiology
Publication Details
Applied financial economics, v 24(17), pp 1147-1157
Publisher
Routledge
Resource Type
Journal article
Language
English
Academic Unit
Economics (School of Economics); Pharmacology and Physiology