Volatility Forecasting of Copper Futures Based on HAR-RV Model

Abstract

As an important part of the international futures market, copper price prediction is important for international financial market research. This paper selects the high-frequency data every 5 minutes from the database and uses the HAR-RV model based on realized volatility. By introducing investor sentiment and the day of week effects, we have established three new types of non-uniform autoregressive models. Empirical analysis shows that the weekly and monthly fluctuations of copper futures prices are relatively small, while the daily fluctuations are relatively large. The prediction model is more accurate when predicting the long-term volatility, and the stability test shows that the HAR-RV model is relatively stable when predicting the long-term volatility. Investor sentiment has a negative impact on the price volatility of copper futures in the medium and long-term forecasts. Weekend effects have a negative impact on the medium and long-term forecasts of copper futures. This paper complements the existing literature and improves the prediction ability of copper price fluctuations, which is very important to promote effective hedging, risk transfer, and price discovery in the futures market.