# Influencing Factors Analysis of Crude Oil Futures Price Volatility Based on Mixed-Frequency Data
## 2 Literature Review
The GARCH-MIDAS model divides the conditional variance into long-term and short-term variables, and low-frequency variables affect the conditional variance through long-term components. The effectiveness of the GARCH-MIDAS model in predicting the volatility of the stock market has been proved, and this model has been gradually applied to studies [35]. Past researches have used the GARCH-MIDAS framework to re-examine the relationship between oil and the stock market by analyzing the long-term macroeconomic determinants of the daily stock market and crude oil price returns in the United States [36]. Some scholars also used the GARCH-MIDAS model to study the effects of oil supply and demand levels and fluctuations in the price fluctuations of WTI and Brent crude oil from 1986 to 2015 [37]. The GARCH-MIDAS model is also gradually used in predicting oil price. Some scholars forecasted oil market volatility using the probability (or intensity) of jump occurrences, the sign (e.g., positive or negative) of jumps, and the concurrence with stock market jumps, which are all under the mixed data sampling (MIDAS) modeling framework. The empirical results showed that this model can markedly improve the accuracy of oil market volatility forecasting [38]. Existing factors influencing oil price fluctuations to improve study and forecast changes in the oil market include speculation, economic fundamentals [39; 40], basic (physical) predictors, financial predictors [41], uncertainty of geopolitical risk [42], economic policy uncertainty (EPU), and indicator variables [43; 44]. In order to improve the accuracy of forecasts, scholars have suggested that the use of high-frequency financial data is more helpful in predicting oil prices [45]. Some scholars used MIDAS models and high-frequency data from four stock market indexes to predict WTI and Brent crude oil prices at lower frequencies. The results showed that the high frequency stock market index has a certain advantage over the low-frequency data in predicting the monthly crude oil price, and the MIDAS model of high-frequency data is superior to the common model [46]. However, most of the existing literature studies are based on the basic assumption of normal distribution, which does not meet the characteristics of financial data's spikes and thick tails, and these results cannot fully reflect market conditions. Compared with the normal distribution, a t-distribution can more accurately describe the characteristics of the \"heavy tail\" of the data, and thus can be closer to the actual distribution of financial data. At present, the t-distribution is mostly used in the research on the return and volatility of financial data such as stocks, but the t-distribution still has a relatively important reference for the study of crude oil price fluctuations. In summary, the current researches on the volatility of the crude oil market are still not thorough and it can be manifested in two aspects: 1 the traditional single-frequency data econometric model often ignores different frequency data information, which reduces the model's interpretation ability. 2 from the perspective of the comprehensive characteristics of financial data, it lacks consideration of the complex characteristics of financial market data. In the process of modeling, the statistical properties of the financial data itself, such as peaks and thick tails, are often ignored, which may lead to the use of inappropriate models to analyze financial phenomena.