Forecasting the volatility of crude oil futures using high-frequency data: further evidence

Feng Ma, Yu Wei, Wang Chen () and Feng He
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Feng Ma: Southwest Jiaotong University
Yu Wei: Southwest Jiaotong University
Wang Chen: Yangtze Normal University
Feng He: Nankai University

Empirical Economics, 2018, vol. 55, issue 2, No 12, 653-678

Abstract: Abstract We forecast the realized volatility of crude oil futures market using the heterogeneous autoregressive model for realized volatility and its various extensions. Out-of-sample findings indicate that the inclusion of jumps does not improve the forecasting accuracy of the volatility models, whereas the “leverage effect” pertaining to the difference between positive and negative realized semi-variances can significantly improve the forecasting accuracy in predicting the short- and medium-term volatility. However, the signed jump variations and its decomposition couldn’t significantly enhance the models’ forecasting accuracy on the long-term volatility.

Keywords: Volatility forecasting; High-frequency volatility models; Signed jump variation; Forecasting evaluation; C53; E27; E37 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (8)

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DOI: 10.1007/s00181-017-1294-6

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