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
  • [31] Forecasting the volatility of agricultural commodity futures: The role of co-volatility and oil volatility
    Marfatia, Hardik A.
    Ji, Qiang
    Luo, Jiawen
    [J]. JOURNAL OF FORECASTING, 2022, 41 (02) : 383 - 404
  • [32] Stock index realized volatility forecasting in the presence of heterogeneous leverage effects and long range dependence in the volatility of realized volatility
    Louzis, Dimitrios P.
    Xanthopoulos-Sisinis, Spyros
    Refenes, Apostolos P.
    [J]. APPLIED ECONOMICS, 2012, 44 (27) : 3533 - 3550
  • [33] Forecast the realized range-based volatility: The role of investor sentiment and regime switching
    Xu, Weiju
    Wang, Jiqian
    Ma, Feng
    Lu, Xinjie
    [J]. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2019, 527
  • [34] Oil price volatility forecasting: Threshold effect from stock market volatility
    Chen, Yan
    Qiao, Gaoxiu
    Zhang, Feipeng
    [J]. TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, 2022, 180
  • [35] Range-based volatility forecasting: a multiplicative component conditional autoregressive range model
    Xie, Haibin
    [J]. JOURNAL OF RISK, 2020, 22 (05): : 43 - 65
  • [36] The predictive content of oil price and volatility: New evidence on exchange rate forecasting
    Breen, John David
    Hu, Liang
    [J]. JOURNAL OF INTERNATIONAL FINANCIAL MARKETS INSTITUTIONS & MONEY, 2021, 75
  • [37] Forecasting oil price realized volatility using information channels from other asset classes
    Degiannakis, Stavros
    Filis, George
    [J]. JOURNAL OF INTERNATIONAL MONEY AND FINANCE, 2017, 76 : 28 - 49
  • [38] Holidays, weekends and range-based volatility
    Diaz-Mendoza, Ana-Carmen
    Pardo, Angel
    [J]. NORTH AMERICAN JOURNAL OF ECONOMICS AND FINANCE, 2020, 52
  • [39] Forecasting crude oil price volatility
    Herrera, Ana Maria
    Hu, Liang
    Pastor, Daniel
    [J]. INTERNATIONAL JOURNAL OF FORECASTING, 2018, 34 (04) : 622 - 635
  • [40] Oil price volatility and new evidence from news and Twitter
    Abdollahi, Hooman
    [J]. ENERGY ECONOMICS, 2023, 122