Spillovers and Directional Predictability with a Cross‐Quantilogram Analysis: The Case of U.S. and Chinese Agricultural Futures

Abstract

This paper examines the daily, overnight, intraday, and rolling return spillovers of four key agricultural commodities—soybeans, wheat, corn, and sugar, between the U.S. and Chinese futures markets via a newly developed quantile dependence measure called quantilogram. The results reveal significant bi‐directional dependence between the two markets across commodities which is greater in extreme quantiles and moderately stronger from the United States to China. These findings offer valuable insights into investors’ behavior, market integration, dissimilarity, and market efficiency in both countries. © 2016 Wiley Periodicals, Inc. Jrl Fut Mark 36:1231–1255, 2016

Quantile information share

“…Han et al (2016) examine quantile dependence and directional predictability between the foreign exchange market and the stock market in Korea. Jiang, Su, Todorova, and Roca (2017) consider the futures markets in the United States and China for soybeans, wheat, corn, and sugar separately and document bidirectional quantile dependence. Baumöhl and Lyócsa (2017) compare quantile dependence between gold price and stock market sector indices before and after the subprime crisis.…”

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

“…There are two essential reasons for this choice: (a) Corn, wheat, cotton, and soybean futures are very active in China, and the same choice is also made by Li, Zhang, and Zhou (2017), Jiang et al (2016), Tian et al (2017aTian et al ( , 2017b, Yang, Yang, and Zhou (2012), and Zhang and Qu (2015); and (b) the CSI300 index is a well-applied prospect, because it is commonly used as a representative index to measure the overall performance of the Chinese stock market (Chen, Han, Li, & Wu, 2013). There are two essential reasons for this choice: (a) Corn, wheat, cotton, and soybean futures are very active in China, and the same choice is also made by Li, Zhang, and Zhou (2017), Jiang et al (2016), Tian et al (2017aTian et al ( , 2017b, Yang, Yang, and Zhou (2012), and Zhang and Qu (2015); and (b) the CSI300 index is a well-applied prospect, because it is commonly used as a representative index to measure the overall performance of the Chinese stock market (Chen, Han, Li, & Wu, 2013).…”

“…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.…”

Directional predictability from stock market sector indices to gold: A cross-quantilogram analysis

“…In a recent contribution, Han et al (2016) another. We employ the unconditional quantile version of the approach presented by Han et al (2016), which was also recently used to study the spillovers between agriculture commodity markets in the US and China by Jiang et al (2016).…”