Volatility forecasting in the Chinese commodity futures market with intraday data
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.
Out-of-Sample Predictability Long Memory Time Series Futures Market Regulation Realized Volatility Econometric Models
Ying Jiang Shamim Ahmed Xiaoquan Liu
Nottingham University Business School China,University of Nottingham Ningbo,Ningbo 315100,China Nottingham University Business School,University of Nottingham,Nottingham NG8 1BB,United Kingdom
国内会议
上海
英文
853-897
2016-07-16（万方平台首次上网日期，不代表论文的发表时间）