# Influencing Factors Analysis of Crude Oil Futures Price Volatility Based on Mixed-Frequency Data
## 1 Introduction  
The quantity and quality of statistical data are the cornerstones of macroeconomic model construction, estimation, and forecasting. How much more information can be extracted from economic data has always been a goal worthy of pursuit. As a commodity, crude oil plays an important role in the real economy and the financial market. As a major supplier of energy and raw materials for many industrial materials and agricultural sectors, it has gradually become an investment tool and can even impact the business cycle in the financial market [1]. The boom of commodities that began in 2004 saw international oil prices rise steadily until 2008, along with the increase of frequency and extent of oil price fluctuations. When the subprime crisis broke out in 2008, speculative funds left the market one after another and the dollar strengthened, which caused commodity prices around the world to fall sharply at the same time. The price of crude oil dropped by 70% from its peak in the latter half of 2018, which far exceeded the 40% decline in copper, corn and soybean futures, indicating that oil prices are more volatile. After the financial crisis,the oil price fluctuated and reacted more to external factors. The psychological expectations of market participants and the existence of speculative factors aggravated the fluctuation range of the oil price, and the ensuing risks in the crude oil market and the crude oil futures market were also greater [2]. The volatility shock of crude oil price has an important impact on all markets [3; 4]. The measurement and prediction of volatility are also of great significance for asset pricing and risk management, and the price volatility and volatility changes of commodities will also affect the pricing of commodity derivatives and the construction of the optimal hedging ratio [5]. Therefore, the study of fluctuation factors becomes the focus of research. Simple fundamental factors can no longer fully explain the volatility of oil prices. Existing studies have explored some factors that cause the volatility of crude oil prices and crude oil futures prices. The factors include OPEC's market and demand structure and demand elasticity [6], OPEC and the U.S. strategic petroleum reserve (SPR) announcement for crude oil spot and futures markets [7], the information efficiency of crude oil inventory positions [8; 9], speculative factors [10], and irregular extreme events [2], natural disasters [11], geopolitical risks (GPRS) [12], even including the \"long memory\" of oil price fluctuation [13]. For oil futures, compared to the fundamentals of supply and demand, macroeconomic and financial data have become more important factors affecting oil futures prices [14; 15]. Although traditional research methods have certain effects, they still have obvious disadvantages. As for the study of crude oil price volatility, the previous literature mainly included several methods. One was the GARCH (Generalized Autoregressive Conditional Heteroskedasticity)-class models [16; 17]. After comparing nine different GARCH models (RiskMetrics, GARCH, IGARCH, GIR, EGARCH, APARCH, FIGARCH, FIAPARCH, and HYGARCH (Hyperbolic Memory Generalized Autoregressive Conditional Heteroscedasticity) models), and it was found that none of them were better than other models [18]. Although the addition of oil volatility index (OVX)-based implied volatility can greatly improve the forecast accuracy of crude oil prices compared with the simple GARCH-TYPE volatility models [17], most of these models are difficult to deal with the different frequencies between crude oil earnings volatility and macroeconomic covariables. Another method is that some researchers predicted the actual fluctuation of oil price based on the historical data in the past [19]. This method ignores various external factors and long-term stable conditions that affect the fluctuation of oil price.