# 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  
According to the results of the aforementioned single-layer and combination analysis, there is a big difference between the results of single-factor single-effect analysis and combination effect analysis. This result is related to the type and number of factors included in the model. The modeling in the previous part is based on similar factors (a low-frequency or high-frequency factor), without considering the influence of other factors. In order to comprehensively consider the influence of the combination of different types of factors on the price volatility of Brent crude oil futures, this article attempts to conduct a multifactor combination analysis. However, if multiple combinations of various factors are considered, the number of combinations will be very large. Therefore, a combination of low-frequency and high-frequency factors is considered in a model. According to the previous analysis, low-frequency factors such as the US crude oil inventory (Inventory), OPEC crude oil production (OPEC), and OECD crude oil consumption (OECD) and trading volume of high-frequency factors (V) have significant impacts on the volatility of Brent crude oil futures prices, and their conclusions are stable. Therefore, constructing a combined effect model from certain low-frequency factors (Inventory, OPEC, OECD) and high-frequency factors (V). The estimation results of the combined effect model of high-frequency and low-frequency factors are shown in Tables 11 and 12. Firstly, in order to intuitively judge the estimation ability of the above-mentioned comprehensive low-frequency and high-frequency mixed effect model and the corresponding cofrequency effect model, the graphs of the realized volatility RVF estimated by the corresponding model and the realized volatility RV calculated using the original data were compared (graphics omitted). It was found that the RV and RVF of different models are basically consistent, indicating that the prediction effects of the models are acceptable. Secondly, the MAE and RMSE values of each model are basically the same, indicating that the estimated RV and RVF errors of different models are basically the same, and the fluctuation trends are basically the same. The out-of-sample prediction method was used to test the prediction accuracy of the GARCH-MIDAS model. The sample interval of the high-frequency variables in this article is from 1 January 2008 to 31 December 2017, and the total sample size is 2459 (excluding nontrading days). The sample includes 2376 data for modeling between 1 January 2008 and 31 August 2017, and 83 data for forecasting between 1 September 2017, and 31 December 2017. The developed GARCH-MIDAS model was used to calculate the MAE and RMSE indicators inside and outside the sample according to the results of parameter estimation to examine the prediction effect of the model. MAE and RMSE were used to compare the error between the estimated realized volatility and the actual realized volatility; the smaller the value, the more accurate the model's estimate of the volatility. Table 13 shows the estimated results of the predicted parameters of each model. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variable** & **InventoryL+V+VV** & \begin{tabular}{c} **InventoryL+InventoryV+** \\ **VL+VV** \\ \end{tabular}  & \begin{tabular}{c} **OPECL+VL+VV** \\ **VLV+VV** \\ \end{tabular}  & \ \begin{tabular}{c} **OPECL+VV** \\ **VL+VV** \\ \end{tabular}  & \begin{tabular}{c} **OECDL+V+VV** \\ **VL+VV** \\ \end{tabular}  \\ \hline \(\mu\) & 0.0001 & \(-\)0.0003 & \(-\  
According to Table 13, the MAEout value of various models is lower than MSEin, and the RMSEout value is lower than RMSEin, indicating that the parameters estimated by the fitting sample have a good estimation effect on the data outside the fitting sample, and the model does not have the problem of overfitting. Parameter estimation has a strong ability to predict the volatility of financial time series.