# Influencing Factors Analysis of Crude Oil Futures Price Volatility Based on Mixed-Frequency Data
## 2 Literature Review  
The influencing factors of international crude oil price fluctuations can't be attributed solely to a single factor or a certain leading factor. Oil price fluctuations are a complex and changeable process and are the result of a combination of multiple factors. In the selection of factors affecting the fluctuation of international crude oil prices, some time series' adopt daily data, such as the US dollar exchange rate, but some adopt low-frequency monthly data, such as US crude oil inventories and OPEC crude oil production, so both high-frequency and low-frequency data appear simultaneously in the crude oil price volatility model. The processing method of most scholars has involved carrying out preprocessing on the data so that they could have the same frequency. For example, the mixed data are processed into the same frequency data by summing or interpolation, but this will cause the data to be unable to express all the information and finally affect the model results. Therefore, mixed data modeling can be used to reduce information loss caused by data processing. On the study of oil price fluctuations and forecast, researchers believe that oil futures market shall be as a forecasting method. By applying four kinds of evaluation criteria, the futures market and the expert system for the EIA (Electronic Industries Alliance) were compared with each other from the aspects of prediction error and researchers finally found the NYMEX futures market situation is not inferior in terms of price forecast [22]. However, subsequent studies have reached the opposite result. The granger causality test method has been used to confirm that the forward curve is useful for one week and one month in advance in the daily forecast and the weekly forecast, respectively, but almost useless in the long-term forecast [23]. More researchers have used a variety of modeling methods for research. There have been studies chose a generalized regime switching model, which performs noticeably better than nonswitching models regardless of evaluation criteria [5]. Using the dynamic Nelson-Siegel model to explain the term structure of crude oil prices and using a generalized regression framework based on neural networks to forecast oil prices can be more accurately [24]. Developing three kinds of new HAR (Heterogeneous Autoregressive Model)-type models by incorporating investor sentiment and/or leverage effect in the corresponding original HAR-type models can be used to examine the predictable effect of investor sentiment and leverage on daily, as well as weekly and monthly fluctuations in the crude oil futures market [25]. Some scholars believe that using a simple autoregressive norm to simulate long memory, and using 11 predictive HAR-type time-series models to forecast realized variance can achieve the same effect as the more complex models [26]. More specifically, the TGARCH (Threshold Generalized Autoregressive Conditional Heteroskedasticity) model applies to the volatility of heating oil and natural gas, while the GARCH model applies to the volatility of crude oil and leaded gasoline. Simple moving average models can also work well if choosing the right order. Some complex models are even less effective than single-equation GARCH models. In the backtracking test, the nonparametric model for calculating risk measurement is superior to the parametric model in terms of transcendental number [27]. Some researchers studied the relationship between crude oil and natural gas prices, examined the behavior of natural gas and crude oil price volatility, and measured the extent to which crude oil volatility is displayed in long-term gas prices [28; 29; 30; 31]. However, some researchers showed the evidence of causality from gas to oil [32]. The mixed data sampling method (MIDAS) was first proposed in 2007 [33]. Subsequently, a generalized autoregressive conditional heteroscedasticity model of mixed data sampling, namely GARCH-MIDAS, was proposed in 2013 [34].