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Stock market return predictability revisited: Evidence from a new index constructing the oil market

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  • Chen, Wang
  • Chevallier, Julien
  • Wang, Jiqian
  • Zhong, Juandan

Abstract

An increasing number of studies declare that oil volatility information exhibits superior forecasting performance for equity premiums. This study develops a new predictor, namely, RSJV, using the realized semi-variance framework to reflect the proportion of upward (downward) variance on a specific trading day. Our in-sample and out-of-sample results reveal that the RSJV can outperform the 14 popular macroeconomic indicators of Welch and Goyal (2008). Furthermore, our results reconfirm that the economic constraint can help to improve the accuracy of return predictability.

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  • Chen, Wang & Chevallier, Julien & Wang, Jiqian & Zhong, Juandan, 2022. "Stock market return predictability revisited: Evidence from a new index constructing the oil market," Finance Research Letters, Elsevier, vol. 49(C).
    • Handle: RePEc:eee:finlet:v:49:y:2022:i:c:s1544612322003300
    DOI: 10.1016/j.frl.2022.103106
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    References listed on IDEAS

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    2. Mohsin, Muhammad & Jamaani, Fouad, 2023. "A novel deep-learning technique for forecasting oil price volatility using historical prices of five precious metals in context of green financing – A comparison of deep learning, machine learning, an," Resources Policy, Elsevier, vol. 86(PA).

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