Empirical differences between the overnight and day trading hour returns

Abstract

Purpose The purpose of this paper is to provide a stable model which covers market information of return to examine the empirical differences between the returns during night and day in Chinese commodity futures market. Design/methodology/approach Commodity indices are constructed using principal components analysis to represent the market returns for day and night trading in the Chinese commodity futures market. Then VAR models are employed to predict the commodity indices’ returns and squared returns. Findings The symmetric VAR model failed to model the market returns since the asymmetric effects of positive and negative returns are not taken into account. By allowing asymmetric behavior among positive and negative variables, asymmetric VAR model is utilized to trace the leading effect of overnight returns to daytime trading returns. However, the symmetric VAR model outperforms the asymmetric model when evaluating the predictive power of squared returns during night trading hours. Two major results based on asymmetric model for the return are: There is a 6-day leading effect of nighttime return to daytime return in Chinese commodity futures market. It is risky to hold day trading position overnight. Research limitations/implications Asymmetric VAR model provides a new approach to forecasting the direction of price movement. Practical implications Investment managers are able to create a stable portfolio contains major market information. Day and night traders are likely to gain some suggestions to discover arbitrage opportunities. Social implications Since there is no commodity futures index in China, the method for creating indices for Chinese commodity futures is provided to market regulators. Originality/value Combining principal component analysis and asymmetric VAR model provides a stable and predictable model to obtain market information.

Night trading with futures in China: The case of Aluminum and Copper

“…3 Fung et al (2016) used daily prices and trading activity data from Chinese commodity futures to document that the returns have become more symmetric and that interactions between trading activity and volatility have conformed better to the observed patterns in developed markets. Using daily closing and opening prices, Du (2018) ran VAR models to predict commodity returns and volatility and established the presence of a leading effect of overnight returns to daytime trading returns. Jin et al (2018) and Xu & Zhang (2019) used intraday data to investigate the price discovery and market quality of Chinese gold markets and provided evidence of the importance of NTS in this regard.…”

The role of jumps in the agricultural futures market on forecasting stock market volatility: New evidence

“…The first contribution of this paper is that it considers cojumps within the agricultural futures market, based on the corn, wheat, cotton, and soybean commodities, and cojumps between the agricultural futures market and the stock market to explore cojumps' predictive ability. There are two main motivations for this research: (a) In recent years, commodity markets have received more attention from scholars and practitioners-for example, Kellard, Newbold, Rayner, and Ennew (1999), Tomek and Peterson (2001), Sørensen (2002), Tang and Xiong (2012), Anderson, Rausser, and Swinnen (2013), Nazlioglu, Erdem, and Soytas (2013), Jiang, Su, Todorova, and Roca (2016), Le Pen and Sévi (2017), Tan and Ma (2017), Tian, Yang, and Chen (Tian, Yang, & Chen, 2017a;Tian, Yang, & Chen, 2017b), Bakas and Triantafyllou (2018), Du (2018), Gong and Lin (2018), and Wu, Dorfman, and Karali (2018); and (b) as documented by Le Pen and Sévi (2017), commodity markets are now more closely related to the financial market. Moreover, the existing literature (see, e.g., Berger & Uddin, 2016;Büyükşahin & Robe, 2014;Hammoudeh, Nguyen, Reboredo, & Wen, 2014) indicates that the dependence between equities and commodities becomes stronger in market turmoil, especially after the Lehman-filed bankruptcy.…”

“…As we know, agricultural commodity futures have received more attention in academia-for example, Kellard et al (1999), Tomek and Peterson (2001), Sørensen (2002), Anderson et al (2013), Nazlioglu et al (2013), Jiang et al (2016, Tan and Ma (2017), Tian et al (2017aTian et al ( , 2017b, Du (2018), and Gong and Lin (2018). However, none of these studies has investigated cojumps between the agricultural futures market and the stock market, nor explored cojumps' abilities to predict stock market volatility.…”