Journal
JOURNAL OF FORECASTING
Volume 37, Issue 8, Pages 781-789Publisher
WILEY
DOI: 10.1002/for.2502
Keywords
crude oil; EGARCH; HMM; LSSVM; volatility forecasting
Categories
Funding
- National Natural Science Foundation of China [71774054, 71101011, 71322103, 71431008]
- National Special Support Program for High-Level Personnel from the Central Government of China
- Changjiang Scholars Program of the Ministry of Education of China
- Hunan Youth Talent Program
- Beijing Philosophy and Social Science Planning Project [2014BJ0264]
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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.
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