Volatility forecasting of crude oil market: A new hybrid method

作者：Zhang, Yue Jun; Zhang, Jin Liang^*

来源：Journal of Forecasting, 2018, 37(8): 781-789.

DOI：10.1002/for.2502

• crude oil
• EGARCH
• HMM
• LSSVM
• volatility forecasting

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.

• 出版日期2018-12
• 单位湖南大学; 华北电力大学（保定）; 华北电力大学