Can oil prices help predict US stock market returns? Evidence using a dynamic model averaging (DMA) approach

Hanan Naser () and Fatema Alaali
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Fatema Alaali: Ahlia University

Empirical Economics, 2018, vol. 55, issue 4, No 14, 1757-1777

Abstract: Abstract Crude oil price behaviour has fluctuated wildly since 1973 which has a major impact on key macroeconomic variables. Although the relationship between stock market returns and oil price changes has been scrutinized excessively in the literature, the possibility of predicting future stock market returns using oil prices has attracted less attention. This paper investigates the ability of oil prices to predict S&P 500 price index returns with the use of other macroeconomic and financial variables. Including all the potential variables in a forecasting model may result in an over-fitted model. So instead, dynamic model averaging (DMA) and dynamic model selection (DMS) are applied to utilize their ability of allowing the best forecasting model to change over time while parameters are also allowed to change. The empirical evidence shows that applying the DMA/DMS approach leads to significant improvements in forecasting performance in comparison to other forecasting methodologies and the performance of these models are better when oil prices are included within predictors.

Keywords: Bayesian methods; Econometric models; Macroeconomic forecasting; Kalman filter; Model selection; Dynamic model averaging; Stock returns predictability; Oil prices (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s00181-017-1323-5

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