Forecasting oil futures price volatility: New evidence from realized range-based volatility

被引:60
|
作者
Ma, Feng [1 ]
Zhang, Yaojie [1 ]
Huang, Dengshi [1 ]
Lai, Xiaodong [1 ]
机构
[1] Southwest Jiaotong Univ, Sch Econ & Management, Chengdu, Sichuan, Peoples R China
关键词
Volatility forecasting; Oil futures price; Realized range-based volatility; Jump; Jump intensity; CRUDE-OIL; MARKET VOLATILITY; CONTINUOUS-TIME; STOCK-MARKET; MODEL; JUMPS; RETURNS; SAMPLE; UNCERTAINTY; PREDICTION;
D O I
10.1016/j.eneco.2018.09.006
中图分类号
F [经济];
学科分类号
02 ;
摘要
In this article, we investigate the impacts of jump intensity on the volatility of futures in the oil futures market using the heterogeneous autoregressive model of realized range-based volatility (HAR-RRV) and its extended model. We present several interesting and notable findings. First, short-term investors have larger influences on oil futures price volatility. In addition, negative returns are significant, but the effects of jumps and their intensity (probability) appear to not be significant during the in-sample period. Second, the out-of-sample results statistically support that our proposed models are able to achieve higher forecast accuracy than that of the benchmark in both the statistical and economic senses, especially when including the combination of significant jumps and jump intensity. Third, our findings are strongly robust in various checks, such as different forecasting windows, sampling frequencies, and volatility measures. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码:400 / 409
页数:10
相关论文
共 50 条
  • [41] Volatility forecasting on China's oil futures: New evidence from interpretable ensemble boosting trees
    Feng, Lingbing
    Rao, Haicheng
    Lucey, Brian
    Zhu, Yiying
    [J]. INTERNATIONAL REVIEW OF ECONOMICS & FINANCE, 2024, 92 : 1595 - 1615
  • [42] Topological tail dependence: Evidence from forecasting realized volatility
    Souto, Hugo Gobato
    [J]. JOURNAL OF FINANCE AND DATA SCIENCE, 2023, 9
  • [43] Properties of range-based volatility estimators
    Molnar, Peter
    [J]. INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS, 2012, 23 : 20 - 29
  • [44] Forecasting crude oil volatility and stock volatility: New evidence from the quantile autoregressive model
    Chen, Yan
    Zhang, Lei
    Zhang, Feipeng
    [J]. NORTH AMERICAN JOURNAL OF ECONOMICS AND FINANCE, 2024, 74
  • [45] The effect of new futures contracts on gold futures price volatility: Evidence from the Thailand futures exchange
    Jongadsayakul, Woradee
    [J]. COGENT ECONOMICS & FINANCE, 2020, 8 (01):
  • [46] Forecasting the Asian stock market volatility: Evidence from WTI and INE oil futures
    Ghani, Maria
    Ma, Feng
    Huang, Dengshi
    [J]. INTERNATIONAL JOURNAL OF FINANCE & ECONOMICS, 2024, 29 (02) : 1496 - 1512
  • [47] Multifractal analysis of realized range-based volatility in Shanghai Stock Exchange Composite Index
    Jia Zhanliang
    Li Handong
    [J]. PROCEEDINGS OF 2010 INTERNATIONAL CONFERENCE ON LOGISTICS SYSTEMS AND INTELLIGENT MANAGEMENT, VOLS 1-3, 2010, : 985 - 988
  • [48] Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss
    Gkillas, Konstantinos
    Gupta, Rangan
    Pierdzioch, Christian
    [J]. JOURNAL OF INTERNATIONAL MONEY AND FINANCE, 2020, 104
  • [49] The Information Contents of VIX Index and Range-based Volatility on Volatility Forecasting Performance of S&P 500
    Hung, Jui-Cheng
    Ni, Ren-Xi
    Chang, Matthew C.
    [J]. ECONOMICS BULLETIN, 2009, 29 (04): : 2592 - 2604
  • [50] The economic value of volatility timing using a range-based volatility model
    Chou, Ray Yeutien
    Liu, Nathan
    [J]. JOURNAL OF ECONOMIC DYNAMICS & CONTROL, 2010, 34 (11): : 2288 - 2301