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Volatility forecasting of crude oil market: A new hybrid method
Author(s) -
Zhang YueJun,
Zhang JinLiang
Publication year - 2018
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2502
Subject(s) - volatility (finance) , autoregressive conditional heteroskedasticity , econometrics , autoregressive model , crude oil , heteroscedasticity , markov chain , stochastic volatility , economics , computer science , machine learning , engineering , petroleum engineering
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.