Volatility forecasting of crude oil market: A new hybrid method

Yue‐Jun Zhang and Jin‐Liang Zhang
Authors registered in the RePEc Author Service: 张跃军 ()

Journal of Forecasting, 2018, vol. 37, issue 8, 781-789

Abstract: Given the complex characteristics of crude oil price volatility, a new hybrid forecasting method based on the hidden Markov, exponential generalized autoregressive conditional heteroskedasticity, and least squares support vector machine models is proposed, and the forecasting performance of the new method is compared with that of well‐recognized generalized autoregressive conditional heteroskedasticity class and other related forecasting methods. The results indicate that the new hybrid forecasting method can significantly improve forecasting accuracy of crude oil price volatility. Furthermore, the new method has been demonstrated to be more accurate for the forecast of crude oil price volatility particularly in a longer time horizon.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:37:y:2018:i:8:p:781-789

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