# Influencing Factors Analysis of Crude Oil Futures Price Volatility Based on Mixed-Frequency Data
## 4 Variables and Data Description
### Analysis of the Multi-factors Associative Effect
#### 5.4.1 Summary and Comparison of Estimation Results of Crude Oil Futures Price Volatility Effects  
As the GARCH model is the same frequency data modeling, and the GARCH-MIDAS model is the mixing data modeling, in order to compare these two models, the estimate results of the high-frequency data level and volatility combined effect model in the GARCH-MIDAS model (Table 9) and the independent effect model in the GARCH model (Table 14) were compared and analyzed (Table 15).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & **LV** & **LOI** & **LDI** & **LGP** & **LPS** \\ \cline{2-6}  & **(A)** & **(B)** & **(C)** & **(D)** & **(E)** \\ \hline \multirow{3}{*}{\(\omega\)} & \(6.99\times 10\)–7 & \(0.000314\)*** & \(3.96\times 10\)–7 & \(1.48\times 10\)–6*** & \(8.75\times 10\)–6*** \\  & [0.2873] & [0] & & & \\  & 0.043678 *** & 0.104152 *** & 0.042164 *** & 0.043754 *** & 0.072994 *** \\  & [0] & [0.0004] & [0] & [0] & [0] \\ \multirow{2}{*}{\(\beta_{1}\)} & 0.953208 *** & 0.534416 *** & 0.943901 *** & 0.953893 *** & 0.567250 *** \\  & [0] & [0] & [0] & [0] & [0] \\ \multirow{2}{*}{\(\gamma_{1}\)} & \(3.96\times 10\)–5*** & \(-0.001474\)*** & 0.000873 *** & 9.31\(\times 10\)–5*** & \(-0.000973\)*** \\  & [0] & [0.0001] & [0.0214] & [0.097] & [0.0055] \\ \multirow{2}{*}{\(\gamma_{2}\)} & 1.11\(\times 10\)–5 & 0.040357 *** & 0.196468 *** & 0.000104 * & 0.359574 *** \\  & [0.1854] & [0] & [0] & [0.0907] & [0] \\ \multirow{2}{*}{\(R^{2}\)} & 0.001900 & 0.002526 & 0.002051 & 0.001944 & 0.000947 \\  & 0.001494 & 0.002120 & 0.001645 & 0.001537 & 0.000540 \\ Log likelihood & 6332.2 & 5915.735 & 6337.712 & 6329.677 & 6503.577 \\ AIC & \(-5.146624\) & \(-4.807759\) & \(-5.151108\) & \(-5.187671\) & \(-5.286068\) \\ SC & \(-5.130086\) & \(-4.791221\) & \(-5.134571\) & \(-5.171134\) & \(-5.269530\) \\ \multirow{2}{*}{Q(36)} & 41.685 & 42.432 & 42.127 & 42.944 & 42.122 \\  & [0.237] & [0.213] & [0.221] & [0.198] & [0.223] \\ \multirow{2}{*}{ARCH-LM(1)} & 0.999609 & 0.197764 & 1.115448 & 1.355851 & 0.073082 \\  & [0.3174] & [0.6565] & [0.2909] & [0.2443] & [0.7869] \\ \multirow{2}{*}{T-DIST} & 7.026739 *** & 7.117727 *** & 7.094364 *** & 7.066860 *** & 7.094174 *** \\  & [0] & [0] & [0] & [0] & [0] \\ \multirow{2}{*}{\(\alpha_{1}\)+\(\beta_{1}\)} & 0.996886 & 0.638568 & 0.986065 & 0.997647 & 0.640244 \\ \hline \hline \end{tabular} Note: ***, ***, and * indicate statistically significant at the significance level of 1%, 5%, and 10%, respectively; p values are in brackets [ ].  
\end{table}
Table 14: Estimation results of the influence of various factors on the fluctuation of Brent crude oil futures.  
Regarding the direction of action, there is only a difference between the GARCH and GARCH-MIDAS models in the direction of the horizontal effect of the position volume. The GARCH model estimation result is negative, while the GARCH-MIDAS estimation result is positive but not significant, but the estimation result of the position fluctuation effect model is consistent with that of the GARCH model, that is, negative (not significant). The level of other variables and the direction of action of the volatility effect are consistent.  
From the results of the significant difference, the significance of the volume fluctuation effect, the level and volatility effect of open interest, the dollar index level effect, and the spot price level effect have undergone significant changes. There is no significant difference between the level effect and the volatility effect of other variables.