This paper renders new insights into the predictability of GCC stock returns using crude oil prices using the approach of [1] [2] that accounts for salient features of the predictor. The results show superior performance of the oil-based stock model over time-series models (namely, AR, MA, ARMA, and ARFIMA) for both in-sample and out-of-sample forecasts. The results are robust to different oil price series (Brent and WTI prices) and forecast horizons (30 and 60 days).

This paper renders new evidence on the predictability of GCC dollar exchange rates using crude oil prices relying on the approach of Westerlund [1][2] that accounts for salient features of the predictor. The results show the presence of significant in-sample predictability of exchange rates using crude oil prices (Brent and WTI prices) across the GCC countries. The results of forecast evaluation based on the root mean square error (RMSE), Campbell-Thompson (C-T) statistic and Diebold-Mariano (D-M) statistic are rather mixed. The superior forecast performance of the oil-based exchange rate model is highly sensitive to the choice of benchmark time-series models. We, however, conclude the overwhelming forecast performance of time-series models (namely, AR, ARMA, and ARFIMA) over our oil-based exchange rate model in predicting exchange rates across the GCC region.