Southern oscillation: Great value of its trends for forecasting crude oil spot price volatility

被引:9
|
作者
Hong, Yanran [1 ]
Yu, Jize [2 ]
Su, Yuquan [3 ]
Wang, Lu [1 ]
机构
[1] Southwest Jiaotong Univ, Sch Math, Chengdu, Peoples R China
[2] Univ London, Singapore Inst Management, London, England
[3] James Cook Univ, Banking & Finance, Townsville, Qld, Australia
关键词
Volatility forecasting; Southern oscillation; Crude oil spot market; STL decomposition; GARCH-MIDAS; STOCK-MARKET VOLATILITY; RARE DISASTER RISKS; COMBINATION; RETURNS; CLIMATE; SAMPLE; ASYMMETRIES; ENERGY; MODEL; ENSO;
D O I
10.1016/j.iref.2022.11.023
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
An increasing number of studies have verified the impact of climate change on commodity spot markets. Based on the forecasting framework, we revisit this work by focusing on the crude oil spot markets. We employ the Southern Oscillation Index (SOI) to capture the climate changes as it determines the weather and climate of numerous living areas. To obtain a more accurate result, we apply the STL decomposition to construct a series of the GARCH-MIDAS models combining the trend, seasonal, and remainder components of SOI. The empirical results reveal that the SOI trend show significance in the in-sample estimation and the model involving it outperforms others in the out-of-sample prediction. Hence, the trend of weather and climate changes may provide greater economic value for market participants investing crude oil spot markets.
引用
收藏
页码:358 / 368
页数:11
相关论文
共 50 条
  • [21] The influence of international crude oil price on the crude oil spot price in China
    Guo, Rong
    Chen, Yanhui
    Lo, Kai Lisa
    Mi, Jinhong Jackson
    8TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND QUANTITATIVE MANAGEMENT (ITQM 2020 & 2021): DEVELOPING GLOBAL DIGITAL ECONOMY AFTER COVID-19, 2022, 199 : 1144 - 1151
  • [22] Forecasting aggregate equity return volatility using crude oil price volatility: The role of nonlinearities and asymmetries
    Nonejad, Nima
    NORTH AMERICAN JOURNAL OF ECONOMICS AND FINANCE, 2019, 50
  • [23] A monthly crude oil spot price forecasting model using relative inventories
    Ye, M
    Zyren, J
    Shore, J
    INTERNATIONAL JOURNAL OF FORECASTING, 2005, 21 (03) : 491 - 501
  • [24] Monthly crude oil spot price forecasting using variational mode decomposition
    Li, Jinchao
    Zhu, Shaowen
    Wu, Qianqian
    ENERGY ECONOMICS, 2019, 83 : 240 - 253
  • [25] Forecasting crude oil spot price using OECD petroleum inventory levels
    Ye M.
    Zyren J.
    Shore J.
    International Advances in Economic Research, 2002, 8 (4) : 324 - 333
  • [26] Forecasting crude oil market volatility using extreme-value method
    Li, Hongquan
    Wang, Shouyang
    Wen, Fenghua
    ADVANCES IN BUSINESS INTELLIGENCE AND FINANCIAL ENGINEERING, 2008, 5 : 964 - +
  • [27] Effect of outliers on volatility forecasting and Value at Risk estimation in crude oil markets
    Sharma, Himanshu
    Dharmaraja, Selvamuthu
    OPEC ENERGY REVIEW, 2016, 40 (03) : 276 - 299
  • [28] Interval forecasting of crude oil price
    Xu, Shanying
    Chen, Xi
    Han, Ai
    INTERVAL / PROBABILISTIC UNCERTAINTY AND NON-CLASSICAL LOGICS, 2008, 46 : 353 - 363
  • [29] Crude oil price forecasting with ANFIS
    Zimberg, B.
    INTERNATIONAL CONFERENCE ON INDUSTRIAL LOGISTICS (ICIL 2008): LOGISTICS IN A FLAT WORLD: STRATEGY, MANAGEMENT AND OPERATIONS, 2008, : 274 - 281
  • [30] Forecasting the Crude Oil Prices Volatility With Stochastic Volatility Models
    Oyuna, Dondukova
    Liu Yaobin
    SAGE OPEN, 2021, 11 (03):