Afees A. Salisu
Improving the predictability of the oil–US stock nexus: The role of macroeconomic variables
Salisu, Afees A.; Swaray, Raymond; Oloko, Tirimisiyu F.
Dr Raymond Swaray R.Swaray@hull.ac.uk
Senior Lecturer (Associate Professor) in Economics
Tirimisiyu F. Oloko
In this study, we revisit the oil–stock nexus by accounting for the role of macroeconomic variables and testing their in-sample and out-of-sample predictive powers. We follow the approaches of Lewellen (2004) and Westerlund and Narayan (2015), which were formulated into a linear multi-predictive form by Makin et al. (2014) and Salisu et al. (2018) and a nonlinear multi-predictive model by Salisu and Isah (2018). Thereafter, we extend the multi-predictive model to account for structural breaks and asymmetries. Our analyses are conducted on aggregate and sectoral stock price indexes for the US stock market. Our proposed predictive model, which accounts for macroeconomic variables, outperforms the oil-based single-factor variant in forecasting aggregate and sectoral US stocks for both in-sample and out-of-sample forecasts. We find that it is important to account for structural breaks in our proposed predictive model, although asymmetries do not seem to improve predictability. In addition, we show that it is important 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.
Salisu, A. A., Swaray, R., & Oloko, T. F. (2019). Improving the predictability of the oil–US stock nexus: The role of macroeconomic variables. Economic modelling, 76, 153-171. https://doi.org/10.1016/j.econmod.2018.07.029
|Journal Article Type||Article|
|Acceptance Date||Jul 29, 2018|
|Deposit Date||Aug 13, 2018|
|Publicly Available Date||Jan 31, 2019|
|Peer Reviewed||Peer Reviewed|
|Keywords||Oil price; Sectoral US stock; Inflation; Output; Interest rate; Forecast evaluation|
|Copyright Statement||© 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/|
Publisher Licence URL
© 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
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