A multi-factor predictive model for oil-US stock nexus with persistence, endogeneity and conditional heteroscedasticity effects

In this study, we extend the single-predictor model for US stock market developed by Narayan and Gupta (2014) to capture more important predictors of the market. Our analyses are conducted in three distinct ways. First, we test whether oil price will produce better forecast accuracy in the multiple-factor model than in the single-factor model. Secondly, we also test the plausibility of making generalization about the predictive model for oil-US stocks on the basis of large cap stocks. Thirdly, we employ the recently developed Feasible Quasi Generalized Least Squares (FQGLS) estimator by Westerlund and Narayan (2014) in order to capture the inherent persistence, endogeneity and heteroscedasticity effects in the predictors. Our results reveal that oil price renders better forecast performance in the multiple-factor predictive model than in the single-factor variants for both in-sample and out-of-sample forecasts. Also, we find that generalizing the predictability of oil-US stock market with large cap may lead to misleading inferences. In addition, it may be necessary to pre-test the predictors for persistence, endogeneity and conditional heteroscedasticity particularly when modeling with high frequency series. Our results are robust to different forecast measures and forecast horizons.