Volatility forecasting in the Chinese commodity futures market with intraday data

Abstract

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.

The untold story of commodity futures in China

“…Recent studies have examined the broad market performance (Fung, Tse, Yau, & Zhao, 2013;Tu, Song, & Zhang, 2013), trend-following strategies (Li, Zhang, & Zhou, 2017), pricing implications (He, Jiang, & Molyboga, 2019), volatility (Jiang, Ahmed, & Liu, 2017;Tian, Yang, & Chen, 2017), diversification potential (Hammoudeh, Nguyen, Reboredo, & Wen, 2014;Liu, Tse, & Zhang, 2018), the impact of speculation (Fan, Mo, & Zhang, 2019;Wellenreuther & Voelzke, 2019), and high frequency trading (Zhao & Wan, 2018). For example, Tu et al (2013) conclude that the correlation between the Chinese and the US markets has increased during the period 2000.…”

Dynamic correlation and volatility spillovers across Chinese stock and commodity futures markets

“…First, to limit the market manipulation and excessive contract speculation in commodity futures markets, the Chinese regulations regarding liquidity patterns stipulate that the most liquid contracts not be nearby contracts with 1 or 2 months to expiration but more distant contracts with 5 to 6 months until expiration (He, Jiang, & Molyboga, ). Second, an increase in the trading margin prevents the speculative position in Chinese commodity markets (Jiang, Ahmed, & Liu, ). Third, the U.S. commodity markets are populated primarily by institutional hedgers and speculators.…”

Volatility modeling and prediction: the role of price impact

“…The generalized autoregressive conditional heteroskedasticity (GARCH) model of Bollerslev (1986) takes account of the time-varying volatility clustering of most financial time series and has been widely applied in many studies (see Andersen and Bollerslev, 1998b;Chortareas et al, 2011;Glosten et al, 1993;Jiang et al, 2017;Martens, 2001). We use the most parsimonious GARCH (1,1) in our study:…”