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Forecasting the volatility of crude oil futures: The role of oil investor attention and its regime switching characteristics under a high-frequency framework

Yuanyuan Liu, Zibo Niu, Muhammad Tahir Suleman, Libo Yin and Hongwei Zhang

Energy, 2022, vol. 238, issue PA

Abstract: The purpose of this article is to investigate whether oil investor attention (OA), measured by Google search volume, contains incremental information content to predict crude oil futures volatility under high-frequency heterogeneous autoregressive (HAR) model specifications. Moreover, to account for possible structural breaks and nonlinearity in the relation between OA and crude oil volatility, this article extends HAR-type models with regime switching considerations. The results of parameter estimation and out-of-sample prediction show that the in-sample and out-of-sample performance of HAR-type and Markov switching (MS)-HAR-type models with OA is significantly better than that of their corresponding HAR-type and MS-HAR-type models without OA. Furthermore, our findings suggest that (i) HAR-type-OA models tend to produce better forecasts for the volatility of the crude oil market at short horizons (1-day) compared to HAR-type, MS-HAR-type and MS-HAR-type-OA models. (ii) MS-HAR-type-OA models have the best forecasting performance at relatively long prediction horizons (1-week and 1-month). Therefore, the result suggests that the OA and regime switching specifications have a significant positive impact on volatility predictions and can be useful for improving the performance of HAR-type models.

Keywords: Crude oil price; Oil investor attention; Volatility forecasting; Markov switching; MCS test (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1016/

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