Prediction of metal futures price volatility and empirical analysis based on symbolic time series of high-frequency
(1. School of Business, Central South University, Changsha 410083, China;
2. Institute of Metal Resources Strategy, Central South University, Changsha 410083, China)
2. Institute of Metal Resources Strategy, Central South University, Changsha 410083, China)
Abstract: The metal futures price fluctuation prediction model was constructed based on symbolic high-frequency time series using high-frequency data on the Shanghai Copper Futures Exchange from July 2014 to September 2018, and the sample was divided into 194 histogram time series employing symbolic time series. The next cycle was then predicted using the K-NN algorithm and exponential smoothing, respectively. The results show that the trend of the histogram of the copper futures earnings prediction is gentler than that of the actual histogram, the overall situation of the prediction results is better, and the overall fluctuation of the one-week earnings of the copper futures predicted and the actual volatility are largely the same. This shows that the results predicted by the K-NN algorithm are more accurate than those predicted by the exponential smoothing method. Based on the predicted one-week price fluctuations of copper futures, regulators and investors in China’s copper futures market can timely adjust their regulatory policies and investment strategies to control risks.
Key words: high-frequency; copper; metal futures; symbolic time series; price fluctuation; prediction