Volatility forecasting in the Chinese commodity futures market with intraday data
利用中国商品期货市场最活跃的三个月期合约，在日度和三种日内采样频率下，系统比较了多种波动率模型对铝、铜、燃料油和糖的预测效果，发现长记忆性是关键特征，ARFIMA模型预测表现最优或不逊于最优。
Given the unique institutional regulations in the Chinese commodity futures market as well as the characteristics of the data it generates, we utilize contracts with three months to delivery, the most liquid contract series, to systematically explore volatility forecasting for aluminum, copper, fuel oil, and sugar at the daily and three intraday sampling frequencies. We adopt popular volatility models in the literature and assess the forecasts obtained via these models against alternative proxies for the true volatility. Our results suggest that the long memory property is an essential feature in the commodity futures volatility dynamics and that the ARFIMA model consistently produces the best forecasts or forecasts not inferior to the best in statistical terms.