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The incremental information content of investor fear gauge for volatility forecasting in the crude oil futures market

Xu Gong and Boqiang Lin ()

Energy Economics, 2018, vol. 74, issue C, 370-386

Abstract: This paper aims to investigate whether investor fear gauge (IFG) contains incremental information content for forecasting the volatility of crude oil futures. For this purpose, we use oil volatility index (OVX) to measure the IFG. Adding the IFG to existing heterogeneous autoregressive (HAR) models, we develop many HAR models with IFG. Subsequently, we employ these HAR models to predict the volatility of crude oil futures. The results from the parameter estimation and out-of-sample forecasting show that the in-sample and out-of-sample performances of HAR models with IFG are significantly better than their corresponding HAR models without IFG. The results are robust in different ways. Thus, the HAR models with IFG are more beneficial to the decision making of all participants (including financial traders, manufacturers and policymakers) in the crude oil futures market. More importantly, the results suggest that the investor fear gauge has a significant positive effect on volatility forecasting, and can help improve the performances of almost all the existing HAR models.

Keywords: Volatility forecasting; Investor fear gauge; Crude oil futures; HAR models; Realized volatility (search for similar items in EconPapers)
JEL-codes: Q41 Q47 G13 G17 C53 C58 (search for similar items in EconPapers)
Date: 2018
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Energy Economics is currently edited by R. S. J. Tol, Beng Ang, Lance Bachmeier, Perry Sadorsky, Ugur Soytas and J. P. Weyant

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