Predicting the volatility of crude oil futures: The roles of leverage effects and structural changes

被引:6
作者
Gong, Xu [1 ]
Lin, Boqiang [1 ]
机构
[1] Xiamen Univ, China Inst Studies Energy Policy, Collaborat Innovat Ctr Energy Econ & Energy Polic, Sch Management, Xiamen 361005, Peoples R China
基金
中国国家自然科学基金;
关键词
HAR models; leverage effects; realized volatility; structural changes; volatility forecasting; ECONOMIC-POLICY UNCERTAINTY; REALIZED VOLATILITY; LONG-MEMORY; STOCHASTIC VOLATILITY; PRICE VOLATILITY; FORECASTING VOLATILITY; DYNAMIC CORRELATIONS; BREAKS; GARCH; MODEL;
D O I
10.1002/ijfe.2171
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
This paper investigates whether leverage effects and structural changes have positive effects on the volatility prediction of crude oil futures. On the basis of existing HAR models, this paper proposes three classes of new HAR models by considering leverage effects, structural changes, or both. The in-sample and out-of-sample results show that leverage effects and structural changes contain significant information for predicting oil volatility. In most cases, structural changes have more in-sample and out-of-sample incremental information than leverage effect, whereas leverage effects have more out-of-sample information for predicting 1-day volatility. In addition, HAR models with leverage effects and structural changes have better in-sample and out-of-sample performances than the corresponding other three classes of HAR models. The above results mean that leverage effects and structural changes should be considered while modelling and forecasting oil volatility.
引用
收藏
页码:610 / 640
页数:31
相关论文
共 69 条
[31]   Macroeconomic news announcements, systemic risk, financial market volatility, and jumps [J].
Huang, Xin .
JOURNAL OF FUTURES MARKETS, 2018, 38 (05) :513-534
[32]   USE OF CUMULATIVE SUMS OF SQUARES FOR RETROSPECTIVE DETECTION OF CHANGES OF VARIANCE [J].
INCLAN, C ;
TIAO, GC .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1994, 89 (427) :913-923
[33]   How does oil price volatility affect non-energy commodity markets? [J].
Ji, Qiang ;
Fan, Ying .
APPLIED ENERGY, 2012, 89 (01) :273-280
[34]   Forecasting volatility of crude oil markets [J].
Kang, Sang Hoon ;
Kang, Sang-Mok ;
Yoon, Seong-Min .
ENERGY ECONOMICS, 2009, 31 (01) :119-125
[35]   Forecasting Value-at-Risk under Temporal and Portfolio Aggregation* [J].
Kole, Erik ;
Markwat, Thijs ;
Opschoor, Anne ;
van Dijk, Dick .
JOURNAL OF FINANCIAL ECONOMETRICS, 2017, 15 (04) :649-677
[36]   An International Comparison of Implied, Realized, and GARCH Volatility Forecasts [J].
Kourtis, Apostolos ;
Markellos, Raphael N. ;
Symeonidis, Lazaros .
JOURNAL OF FUTURES MARKETS, 2016, 36 (12) :1164-1193
[37]   Leverage effect in energy futures [J].
Kristoufek, Ladislav .
ENERGY ECONOMICS, 2014, 45 :1-9
[38]   Jump dynamics with structural breaks for crude oil prices [J].
Lee, Yen-Hsien ;
Hu, Hsu-Ning ;
Chiou, Jer-Shiou .
ENERGY ECONOMICS, 2010, 32 (02) :343-350
[39]   Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes [J].
Liu, Lily Y. ;
Patton, Andrew J. ;
Sheppard, Kevin .
JOURNAL OF ECONOMETRICS, 2015, 187 (01) :293-311
[40]   The dynamic correlations between the G7 economies and China: Evidence from both realized and implied volatilities [J].
Luo, Xingguo ;
Qi, Xuyuanda .
JOURNAL OF FUTURES MARKETS, 2017, 37 (10) :989-1002