Improving the predictability of the oil–US stock nexus: The role of macroeconomic variables

By Afees A. Salisu, R. Swaray, Tirimisiyu F. Oloko. Economic Modelling, 2019

113 citations recorded by the source corpus.

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 as well as the constant returns (historical average) model 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 and are useful for making effective hedging decisions in the US stock market.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/99cc14665bfa2208a0723e7902776a01a51e7158

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Citations in the corpus

Pandemics and the Asia-Pacific Islamic Stocks (2020, methodology): “We also control for an additional predictor, oil price (using the West Texas Intermediate crude oil price3), given evidence of its strong connection with the stock market (Salisu et al., 2019, among others; see Smyth & Narayan, 2018).”
Constructing a Global Fear Index for the COVID-19 Pandemic (2020, background): “For completeness, we also account for some other important factors that can influence stock returns (see also Bannigidadmath and Narayan 2015; Narayan et al. 2016; Devpura, Narayan, and Sharma 2018; Salisu, Swaray, and Oloko 2019a, 2019b, 2019c, 2019d and 2019e).”