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Crude oil price forecasting based on a novel hybrid long memory GARCH-M and wavelet analysis model

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成果类型:
期刊论文
作者:
Lin, Ling;Jiang, Yong;Xiao, Helu;Zhou, Zhongbao
作者机构:
[Lin, Ling] Hunan Agr Univ, Sch Econ, Changsha 410128, Peoples R China.
[Jiang, Yong] Nanjing Audit Univ, Sch Finance, Nanjing 211815, Peoples R China.
[Xiao, Helu] Hunan Normal Univ, Business Sch, Changsha 410081, Peoples R China.
[Zhou, Zhongbao] Hunan Univ, Sch Business Adm, Changsha 410082, Peoples R China.
语种:
英文
关键词:
Crude oil price forecasting;Structural break period;Wavelet de-noising;Empirical mode decomposition;Complex long memory GARCH-M models
期刊:
Physica A: Statistical Mechanics and its Applications
ISSN:
0378-4371
年:
2020
卷:
543
页码:
123532-
基金类别:
National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [71771082, 71801091]; Hunan Provincial Natural Science Foundation of ChinaNatural Science Foundation of Hunan Province [2017JJ1012]; Hunan Education Department of Foundation of China [18C0172]; Hunan Agricultural University Foundation of China [18QN39]
机构署名:
本校为第一机构
院系归属:
经济学院
摘要:
This paper proposes a novel hybrid forecast model to forecast crude oil price on considering the long memory, asymmetric, heavy-tail distribution, nonlinear and non-stationary characteristics of crude oil price. First, we use a signal de-noising method to reduce excessive noise significantly in the crude oil price. Then we employ empirical mode decomposition to transform the de-noised price into different intrinsic mode functions (IMFs). Finally, some complex long memory GARCH-M models are used to forecast different IMFs and a residual. Empirical results show that the proposed hybrid forecasting model WPD-EMD-ARMA-FIGARCH-M achieves significant effect during periods of extreme incidents. The robustness test shows that this hybrid model is superior to traditional models. (C) 2020 Elsevier B.V. All rights reserved.

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