Abstract
This article takes into account the form of mixed data as well as the peak and thick tail characteristics contained in the data characteristics, expands the GARCH-MIDAS (Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling) model, establishes a new GARCH-MIDAS model with the residual term of the skewed-t distribution, and analyzes the influence factors of crude oil futures price volatility, which can better explain the changing laws of crude oil price volatility. The results show the following: First, the low-frequency factors include crude oil production, consumption, inventory, and natural gas spot price, and the high-frequency factors include on-market trading volume and off-market spot price, which can significantly explain the volatility of oil price. Second, low-frequency factors include crude oil inventory, consumption, crude oil production, and speculative factors, and high-frequency factors include crude oil spot price and substitute prices. The increase in the volatility of trading volume is significantly positively correlated with oil price volatility, and the overall volatility model outperforms the horizontal effect model. Third, from the perspective of the combined effect of a single factor level and volatility, we find that supply and demand are the low-frequency factors; the trading volume of on-market factors, natural gas price, and crude oil spot price of off-market factors, among the high-frequency factors, are the most important factors affecting oil price volatility. Fourth, from the perspective of high-frequency and low-frequency effects combined, there is no significant difference between the various factor models, which shows that the mixed effect model of high and low frequency models has advantages in terms of the stability of the estimation results.
Highlights
The quantity and quality of statistical data are the cornerstones of macroeconomic model construction, estimation, and forecasting
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 mixed data sampling (MIDAS) model of high-frequency data is superior to the common model [46]
When the RMAE (RRMSE) value is less than 1, it means that the GARCH-MIDAS model has the effect of improving the accuracy of model prediction and estimation compared with the GARCH (1,1) model
Summary
The quantity and quality of statistical data are the cornerstones of macroeconomic model construction, estimation, and forecasting. 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 article will use the GARCH-MIDAS method, considering the form of mixed data contained in crude oil futures data and the peak and thick tail characteristics of returns, to construct a mixed measurement model and to explore the influence of various factors with different data frequencies on crude oil futures price volatility
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