Influencing Factors Analysis of Crude Oil Futures Price Volatility Based on Mixed-Frequency Data
This article takes into account the form of mixed data as well as the peak and thick tail characteristics contained in the data characteristics, expands the GARCH-MIDAS (Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling) model, establishes a new GARCH-MIDAS model with the...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-11-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/10/23/8393 |
| _version_ | 1797546777709117440 |
|---|---|
| author | Congxin Wu Xinyu Wang Shan Luo Jing Shan Feng Wang |
| author_facet | Congxin Wu Xinyu Wang Shan Luo Jing Shan Feng Wang |
| author_sort | Congxin Wu |
| collection | DOAJ |
| description | This article takes into account the form of mixed data as well as the peak and thick tail characteristics contained in the data characteristics, expands the GARCH-MIDAS (Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling) model, establishes a new GARCH-MIDAS model with the residual term of the skewed-t distribution, and analyzes the influence factors of crude oil futures price volatility, which can better explain the changing laws of crude oil price volatility. The results show the following: First, the low-frequency factors include crude oil production, consumption, inventory, and natural gas spot price, and the high-frequency factors include on-market trading volume and off-market spot price, which can significantly explain the volatility of oil price. Second, low-frequency factors include crude oil inventory, consumption, crude oil production, and speculative factors, and high-frequency factors include crude oil spot price and substitute prices. The increase in the volatility of trading volume is significantly positively correlated with oil price volatility, and the overall volatility model outperforms the horizontal effect model. Third, from the perspective of the combined effect of a single factor level and volatility, we find that supply and demand are the low-frequency factors; the trading volume of on-market factors, natural gas price, and crude oil spot price of off-market factors, among the high-frequency factors, are the most important factors affecting oil price volatility. Fourth, from the perspective of high-frequency and low-frequency effects combined, there is no significant difference between the various factor models, which shows that the mixed effect model of high and low frequency models has advantages in terms of the stability of the estimation results. |
| first_indexed | 2024-03-10T14:34:01Z |
| format | Article |
| id | doaj.art-7040000353f7436cbac5137911636ed8 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T14:34:01Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-7040000353f7436cbac5137911636ed82023-11-20T22:20:06ZengMDPI AGApplied Sciences2076-34172020-11-011023839310.3390/app10238393Influencing Factors Analysis of Crude Oil Futures Price Volatility Based on Mixed-Frequency DataCongxin Wu0Xinyu Wang1Shan Luo2Jing Shan3Feng Wang4School of Economics and Management, China University of Mining and Technology, No. 1, College Rd., Tongshan Dist., Xuzhou 221116, ChinaSchool of Economics and Management, China University of Mining and Technology, No. 1, College Rd., Tongshan Dist., Xuzhou 221116, ChinaSchool of Economics and Management, China University of Mining and Technology, No. 1, College Rd., Tongshan Dist., Xuzhou 221116, ChinaSchool of Economics and Management, China University of Mining and Technology, No. 1, College Rd., Tongshan Dist., Xuzhou 221116, ChinaSchool of Economics and Management, China University of Mining and Technology, No. 1, College Rd., Tongshan Dist., Xuzhou 221116, ChinaThis article takes into account the form of mixed data as well as the peak and thick tail characteristics contained in the data characteristics, expands the GARCH-MIDAS (Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling) model, establishes a new GARCH-MIDAS model with the residual term of the skewed-t distribution, and analyzes the influence factors of crude oil futures price volatility, which can better explain the changing laws of crude oil price volatility. The results show the following: First, the low-frequency factors include crude oil production, consumption, inventory, and natural gas spot price, and the high-frequency factors include on-market trading volume and off-market spot price, which can significantly explain the volatility of oil price. Second, low-frequency factors include crude oil inventory, consumption, crude oil production, and speculative factors, and high-frequency factors include crude oil spot price and substitute prices. The increase in the volatility of trading volume is significantly positively correlated with oil price volatility, and the overall volatility model outperforms the horizontal effect model. Third, from the perspective of the combined effect of a single factor level and volatility, we find that supply and demand are the low-frequency factors; the trading volume of on-market factors, natural gas price, and crude oil spot price of off-market factors, among the high-frequency factors, are the most important factors affecting oil price volatility. Fourth, from the perspective of high-frequency and low-frequency effects combined, there is no significant difference between the various factor models, which shows that the mixed effect model of high and low frequency models has advantages in terms of the stability of the estimation results.https://www.mdpi.com/2076-3417/10/23/8393crude oil futuresvolatilityGARCH-MIDASmixed-frequency data |
| spellingShingle | Congxin Wu Xinyu Wang Shan Luo Jing Shan Feng Wang Influencing Factors Analysis of Crude Oil Futures Price Volatility Based on Mixed-Frequency Data Applied Sciences crude oil futures volatility GARCH-MIDAS mixed-frequency data |
| title | Influencing Factors Analysis of Crude Oil Futures Price Volatility Based on Mixed-Frequency Data |
| title_full | Influencing Factors Analysis of Crude Oil Futures Price Volatility Based on Mixed-Frequency Data |
| title_fullStr | Influencing Factors Analysis of Crude Oil Futures Price Volatility Based on Mixed-Frequency Data |
| title_full_unstemmed | Influencing Factors Analysis of Crude Oil Futures Price Volatility Based on Mixed-Frequency Data |
| title_short | Influencing Factors Analysis of Crude Oil Futures Price Volatility Based on Mixed-Frequency Data |
| title_sort | influencing factors analysis of crude oil futures price volatility based on mixed frequency data |
| topic | crude oil futures volatility GARCH-MIDAS mixed-frequency data |
| url | https://www.mdpi.com/2076-3417/10/23/8393 |
| work_keys_str_mv | AT congxinwu influencingfactorsanalysisofcrudeoilfuturespricevolatilitybasedonmixedfrequencydata AT xinyuwang influencingfactorsanalysisofcrudeoilfuturespricevolatilitybasedonmixedfrequencydata AT shanluo influencingfactorsanalysisofcrudeoilfuturespricevolatilitybasedonmixedfrequencydata AT jingshan influencingfactorsanalysisofcrudeoilfuturespricevolatilitybasedonmixedfrequencydata AT fengwang influencingfactorsanalysisofcrudeoilfuturespricevolatilitybasedonmixedfrequencydata |