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The on-going debate over the influence of investor demand on spot commodity prices largely attempts to assess this influence by measuring the growth in investor demand in recent years. Given the serious data problems that plague such analyses, this article pursues another approach in the hope of providing useful insights into the impact of investor demand on spot commodity prices. It focuses on the mechanisms by which investor demand affects spot prices, and in particular on two questions. First, how does an increase in investor demand on the futures markets affect the spot market and spot price? Second, when investor demand is increasing and pushing a commodity's price up, do physical stocks of the commodity also have to be rising, as economists and others widely assume? On the first question, the article concludes that a surge in investor demand raising prices on the futures markets will have a direct and comparable effect on the spot market prices when these markets are in strong contango. However, when markets are in weak contango or backwardation, price movements in the futures markets have a much looser effect on spot prices. As a result, changes in investor demand on the futures markets may have little or no influence on spot prices in the absence of a strong contango. Instead, changes in fundamentals (that is, producer supply and consumer demand) and possibly changes in investor demand taking place directly on the spot market largely determine the spot price at such times. On the second question, the article shows that investor demand can be pushing up a commodity's price even when investor stocks are falling, despite the widespread presumption to the contrary.

The term “informationally linked markets” refers to markets in which traded assets are fundamentally related to each other. Although these markets are interrelated, they have different information processing abilities and make different contributions to price discovery due to distinct transaction costs, regulations, liquidities, and other institutional factors. It is important for us to understand the dynamic nature of the price discovery process, because it reflects information transmission across markets, thereby providing an indication of price efficiency. Price discovery and information transmission in informationally linked markets have been extensively examined in the literature. In their seminal paper, Garbade and Silber (1979) first propose the concepts of dominant and satellite markets and analyze the short-run price behavior of an identical asset traded in two different markets: the New York Stock Exchange and regional stock exchanges. Subsequently, a number of studies have investigated the lead–lag relationship between two informationally linked markets, such as spot and futures markets, and domestic and overseas futures markets (Ding et al., 1999, Hasbrouck, 1995, Lihara et al., 1996, Roope and Zurbruegg, 2002, Tse, 1999 and Xu and Fung, 2005). Grammig et al. (2001) examine price discovery in international equity trading by analyzing quotes originating in New York and Frankfurt for internationally-traded firms. On the other hand, some research focuses on the case of three markets. For example, Booth et al. (1996) document the linkages and information transmission of similar Nikkei 225 stock index futures traded on the Osaka Securities Exchange, the Singapore Exchange, and the Chicago Mercantile Exchange, and find that none of the markets can be considered the main source of information flow. Chu et al. (1999) explore the price discovery function in three S&P 500 index markets: the spot index, the futures index, and S&P Depositary Receipts (SPDRs) markets by using matched synchronous intraday trading data. Their results suggest that the futures market serves a dominant role in price discovery, and imply that price adjustments take place in the spot index and SPDRs markets, but not in the futures market. So and Tse (2004) investigate price discovery relations among the Hang Seng Index, Hang Seng Index futures, and the tracker fund using the Hasbrouck (1995) and Gonzalo and Granger (1995) common-factor models as well as the multivariate generalized autoregressive conditional heteroskedasticity (M-GARCH) model. They conclude that futures markets contain the most information, followed by the spot market, while the tracker fund does not contribute to price discovery. Covrig et al. (2004) assess intraday information revelation and price discovery for the Nikkei 225 spot index traded on the Tokyo Stock Exchange (TSE), Nikkei 225 futures traded simultaneously on the Osaka Securities Exchange (OSE) and the Singapore Exchange (SGX), and confirm the dominant role of futures markets in price discovery. This paper investigates price discovery and information transmission across Chinese commodity spot/futures markets and US futures markets. In particular, for Chinese markets we consider copper and soybean spot contracts, copper futures on the Shanghai Futures Exchange (SHFE), and soybean futures on the Dalian Commodity Exchange (DCE). For US markets, we consider copper futures on the New York Mercantile Exchange (NYMEX), soybean futures on the Chicago Board of Trade (CBOT), and CME Globex copper/soybean futures. Our research represents a significant contribution to the literature in a number of ways. First, previous studies on this subject focus mainly on spot and futures markets or the domestic and overseas futures markets that have the same or overlapped trading hours. However, our research is based on both synchronous and non-synchronous trading information in three markets. While the regular trading hours of the NYMEX and CBOT do not overlap at all with those in Chinese markets, CME Globex copper and soybean futures trade throughout the entire Chinese trading session and also trade when Chinese markets are closed. Information flows rapidly between US and Chinese markets, but may exhibit different characteristics during the overlapped and non-overlapped trading periods. It is documented that, as a result of different rates of information flow, asset price volatilities are higher during exchange trading hours than at other times (French and Roll, 1986). Liu et al. (2011) further show that the information accumulated during non-trading hours contributes substantially to integrated risks of Chinese commodity futures markets. Apparently, the trading activity in the US NYMEX/CBOT and CME Globex futures markets represents an important part of this non-trading period information in Chinese markets. Our research serves as an important step toward understanding characteristics of information flow across markets with both overlapped and non-overlapped trading hours, as well as understanding the relative importance of NYMEX/CBOT and CME Globex trading in information transmission between US and Chinese futures markets. Second, we provide a comprehensive analysis of the price discovery process and the contribution of each market to price discovery. Using the M-GARCH model, we investigate lead–lag relationships among the Chinese futures, Chinese spot, and US futures markets for both copper and soybean contracts. We also investigate volatility spillovers among these markets to further describe the information transmission process. Importantly, we assess the contribution of each market to price discovery using a new measure that properly accounts for both synchronous and non-synchronous trading information. Specifically, in the case of synchronous trading in Chinese and CME Globex markets, the modified information share (MIS) model proposed by Lien and Shrestha (2009) is directly adopted. In the non-synchronous trading case, we use two orderings of the price sequence to capture the interactions between Chinese and NYMEX/CBOT markets, and define the weighted average of the MISs implied by the two sequences as the information share of a particular market. The overall contribution of the market to price discovery is obtained based on the MISs in these two cases. Third, we analyze daily information flows. To analyze both overlapping and non-overlapping trading information, we utilize daily closing data for regular trading in Chinese and NYMEX/CBOT markets and the data from CME Globex that matches Chinese market data. Moreover, we employ commodity futures data as opposed to market index or financial futures data used in most previous work. This is especially interesting, given that individual commodity futures markets are more volatile than are index futures markets. Additionally, while previous studies provide insightful findings in information transmission across financial futures markets, there is little research on commodity futures in this area. By focusing on copper and soybean futures, we are able to evaluate their relative informational roles in international commodity futures markets. Finally, we document the international role of Chinese markets in price discovery and information transmission relative to developed futures markets (US markets). From an empirical perspective, examining information transmission between emerging markets and mature markets and their relative information processing abilities is of particular importance. This is because emerging markets are typically more volatile, less liquid, and less informationally efficient than mature markets such as those in the US and Europe. With the dramatic growth of Chinese economy over the past three decades, Chinese financial markets have become increasingly important in international markets. According to the Futures Industry Association (FIA), in 2008 the trading volume of Chinese commodity futures was 36.5% of the world’s total trading volume, and China’s is now the second largest commodity futures market in the world, with the US market being the largest.1 However, there are significant structural and institutional differences between Chinese markets and developed markets. Consequently, Chinese markets present themselves as an interesting case for research. Most previous work on price discovery focused primarily on mature markets rather than emerging markets. Due to the aforementioned and other reasons, more and more research on Chinese informationally linked markets has been conducted with an emphasis on the interrelation between Chinese futures and US/European futures markets. Using a cointegration analysis and the bivariate EGARCH model, Hua and Chen (2004) and Gao and Liu (2007) show that there are indeed significant cointegration relationships and bidirectional lead–lag relationships between the SHFE and LME copper and aluminum futures markets, and a cointegration relationship between the DCE and CBOT soybean futures markets. Overall, US/European futures markets play a dominant role in information transmission between US/European and Chinese markets. In addition, Xia and Cheng (2006) study the relationships among the DCE futures market, CBOT futures market, and Chinese spot market using the vector autoregressive (VAR) and vector error-correction models (VECM). They also find that there are long-run equilibrium and lead–lag relationships between one another. This paper extends these studies by examining how information is transmitted across Chinese spot/futures markets and US futures markets for copper as well as soybeans, and by quantifying the contributions of each market to the price discovery process based on both synchronous and non-synchronous futures trading information. Our study provides further insight into the dynamic nature of price discovery and information transmission between emerging and mature financial markets. Our results indicate that Chinese futures/spot and US futures markets for both copper and soybeans are interrelated, and that information flows rapidly from one market to others. However, there are asymmetric relationships between futures and spot markets as well as between Chinese and US futures markets in terms of price transmission and volatility spillovers, with a stronger effect from futures markets to spot markets and a stronger effect from US to Chinese futures markets than the other way around. In addition, the NYMEX/CBOT plays a more important role than the CME Globex in information transmission between Chinese and US markets. Moreover, we find that the Chinese copper market adjusts more quickly than the NYMEX copper market to correct the disparity between both markets, and it interprets shocks to the long-run relation as particularly important information that needs to be quickly reflected in price movements. However, the converse is true in the case of soybeans. The information share based on non-synchronous trading information accounts for 65.05% of the overall price discovery in copper markets, while it accounts for 90.24% in soybean markets. The contributions of the Chinese futures, Chinese spots, and US futures to price discovery are 38.58%, 17.89%, and 43.53% for copper, respectively, and 40.33%, 17.52%, and 42.15% for soybeans, respectively. The results imply that about 47%–49% of the total information share of futures markets comes from Chinese futures markets. It follows that the NYMEX and CBOT are still the main driving force in information transmission and price discovery, but the informational role of Chinese markets is remarkable. The remainder of this paper is organized as follows. Section 2 describes the models for price transmission, volatility spillovers, and price discovery measures. Section 3 discusses the data used for our analysis. Section 4 analyzes the empirical results, and Section 5 concludes this paper.