JOURNAL OF CHINESE ECONOMICS, 2014 Vol. 2, No. 1, pp 34-52 http://journals.sfu.ca/nwchp/index.php/journal

Can China Gain Commodity Pricing Power by Developing Futures Markets? Dunguo Mou1 Abstract: Under the situation of bulk commodity financialization, the influence of the commodity futures market determines the pricing power of the host country. Commodity pricing power is not the ability to raise or lower commodity prices, but the ability to wield the price discovering function of the futures market, driving price fluctuations in accordance with the economic cycle and decreasing price manipulation. This paper, using the Time-Varying Parameter Vector Auto-regression method, studies the influence coefficients with the yield series data for copper futures in SHFE, LME and CMX. This analysis finds that LME still has dominant influence on copper pricing; SHFE’s influence fluctuated greatly during the past 10 years. The influence coefficient of the SHFE is positively related with the relative market scale and negatively related with the relative stock level, revealing the effect of commodity financialization on the pricing power of a commodity futures market. Keyword: Commodity Financialization; Commodity Futures; Pricing Power; Time-Varying Parameter Vector Auto-regression Method; Market Scale; Price Shock JEL Classification: D4; F4; F5; G1

1.

Introduction

As China takes the role of world factory under economic globalization, the volume of bulk commodities imported by China is increasing rapidly and the influence of the “China factor” on commodity prices can no longer be ignored. According to UNComtrade, in terms of the commodities imported by China, iron ore represents 60.2%, copper represents 29.5%, vegetable oil represents 45.1%, and crude oil represents 11.3% of the worldwide totals. However, China believes that she has no pricing power in these markets, which results in higher importing prices and national losses; China feels that the situation should be changed. In China, some people hold high hopes for gaining pricing power in bulk commodity markets by developing China’s commodity futures markets. However, we believe that pricing power of the futures market is only the manifestation of the futures market’s influence; given the futures market’s nature and function, it is hard to believe that China will be able to raise or lower prices in her own interest, as some people expected. One function of the futures market is price discovery. Having a commodity futures market in a country can only guarantee that commodity prices will stay at reasonable levels, confronting price manipulations by others and decreasing 1

Corresponding Author, China Center for Energy Economics Research, School of Economics, Xiamen University, Xiamen, Fujian, China, 361005. Email: [email protected], Tel.: +86 05922194949; Fax: +86 05922186075

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the losses resulting from factors such as the “Asian Premium” in the crude oil market. This paper uses the copper futures of the SHFE, the LME and the CMX as a sample to study the evolution of the futures market’s influence by the Time-Varying Parameter Vector Auto-regression method. Copper futures are commonly believed to be the only contract in China’s futures markets that has international influence, and the results will be helpful in clarifying the meaning of pricing power in bulk commodity markets. The paper is organized as follows: the second section provides comprehensive insights into studies about pricing power of the futures market from two perspectives; the third section introduces the Time-Varying Parameter Vector Auto-regression method and data for study; the fourth section provides the empirical results of the evolution of the SHFE’s copper futures market influence coefficient and emphasizes the role of commodity financialization; the fifth section provides the simulation results to clarify the economic sense of the futures market pricing power; the sixth section concludes the paper with suggestions on how to rationally position the futures markets’ pricing power. 2.

Studies Regarding the Futures Market Pricing Power in International Commodity Markets

To the authors’ knowledge, in western countries there is no study about futures market pricing power in the meaning defined by Chinese scholars. This is perhaps because the Chinese scholars’ definition of pricing power in international commodity markets means market manipulation, which is against the market-economic essence and the WTO’s trade principles. However, the studies on bulk commodity financialization and futures market pricing influence suggest that the world pricing mechanism for bulk commodities does put some countries without futures markets at a disadvantage. Falkowski (2011) defines commodity financialization as the vastly expanded role of financial motives, financial markets, financial actors and financial institutions in the commodities markets; it began in the early 2000s, when commodity futures emerged as a popular asset class for many financial institutions after the internet bubble. The index traders’ investment strategy is to treat commodities as an alternative asset to optimize their portfolios and to take only long positions without attention to the fundamental supply and demand relationships in the markets for specific commodities. Mayer (2010) believes that this strategy exerts upward pressure on prices and has also caused commodity and equity prices to move in parallel since 2005. Tang and Xiong (2010) find that, concurrent with the rapidly growing index investment in commodities since the early 2000s, the futures prices of different commodities in the US became increasingly correlated with each other. This correlation helps to explain the synchronized price boom and bust of a set of unrelated commodities since 2006, yet these co-movements did not appear in China. Singleton (2011) finds a link between investor flows and futures prices that is consistent with the financialization hypothesis. Henderson et al. (2012) use a dataset of Commodity-Linked Notes to examine the price impact of commodity investments on

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Can China Gain Commodity Pricing Power

the commodities futures markets. These authors find that hedging trades cause significant price changes in the underlying futures markets, which becomes evidence of the impact of financial trades on the commodity futures prices. Under commodity financialization, most commodities’ futures prices have only an indirect influence on the spot prices except for a few commodities such as crude oil, which is priced directly according to the futures prices. That influence is not, however, negligible. Bernard et al. (2012) address how the financialization of agricultural commodities is making itself felt. UNCTAD (2011) describes the mechanism by which futures prices affect the spot price in two channels: financialization and information asymmetry. In the financial market, investors do not trade on the basis of fundamental supply and demand. Investors simply hold large long positions, enabling them to exert considerable influence on those markets. This influence causes the commodity markets to follow the logic of financial markets—price discovery based on commonly observable events or even on mathematical models—more closely than the logic of the pure goods markets. Although the spot prices are ultimately determined by fundamental supply and demand, up-to-date and reliable information on commodity supply, demand and stocks are not available. Believing in EMH, the market participants may rely on futures prices to convey price signals; herd behavior and wrong information will accentuate any price movements and may cause a sizeable divergence between actual prices and fundamental values, resulting in a speculative bubble. Phillips and Yu (2011) have pointed to some commodity prices that have exhibited bubble-type behavior. When the prices of financialized commodities are affected by financial market behaviors, the influence of the financial market becomes the determining factor of pricing power in commodity markets and can put some countries at disadvantage. Most studies about financial market influence are performed using information or price transmission aspects to judge the price discovery function. Tse (1999) studies price discovery in the Dow Jones Industrial Average (DJIA) spot and futures markets and find that most of the price discovery takes place in the futures market. Ding et al. (1999) study price discovery between the Kuala Lumpur Stock Exchange and the Stock Exchange of Singapore for a Malaysian conglomerate, finding that the majority of price discovery (approximately 70%) occurs in the home country; the price discovery that occurs in Singapore is higher than its share of trading volume. Grammig et al. (2001) also study price discovery in internationally traded stocks and find that, for German stocks that are cross-listed in the US, at least 80 percent of the price discovery takes place in Germany. Covrig et al. (2004) examine the price discovery process of the Nikkei 225 index in three competing markets, finding that the futures market contributes over 75% to price discovery. These authors find that the Singapore Exchange has a contribution of 43% to the futures market and 33% to the total price discovery, which far exceeds its share of the trading volume. These studies also prove that a small satellite market can co-exist with a large home market and play a significant role in the price discovery process. There is also much research studying price discovery in the commodity markets. Xu and Fung (2005) examine across-market information flows for gold, platinum, and

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silver futures contracts traded in both the U.S. and Japanese markets. The results indicate that the pricing transmissions are strong across the two markets, but the information flows appear to lead from the U.S. market to the Japanese market in terms of returns. Fuangkasem et al. (2012) examine the gold futures information transmission among COMEX, MCX, and TOCOM; the lead-lag relationship among the markets exists with COMEX dominating as the center of price discovery. Booth and Ciner (1997) study information transmission in the corn futures markets, finding that the Tokyo Grain Exchange relies on CBT for price information, which is revealed by the open price in TGE. Margarido et al. (2007), using soybean monthly spot price data to study inter-market influence, show that Rotterdam and the U.S. appeared to be price makers in the international market, while prices from Brazil and Argentina did not influence the behavior of U.S. and Rotterdam prices. Even though data from the UN-COMTRADE show that China and the E.U. currently account for approximately 60% and 15%, respectively, of all soybean imports in the world, the E.U. has more pricing power than China. Spargoli and Zagaglia (2007) study the linkages between oil futures traded on the New York Mercantile Exchange and the Inter-Continental Exchange of London, finding that the oil futures traded on the NYMEX and ICE can be used for mutual hedging purposes when the variances of both innovations are modest; yet, when there are common shocks to both markets, the NYMEX reacts more strongly than the ICE. As to China’s futures markets, the relationship between prices in China’s futures markets and those in the international futures markets has been studied. Jiao et al. (2007) and Chen et al. (2009) study the relationship between the Chinese and the international oil markets. Their conclusions are basically consistent: the impact of the international price on the Chinese price is rapid and dramatic, whereas the reverse impact is relatively slow and weak. Ji and Fan (2012) investigate the different impacts of the Daqing crude oil spot market and the Shanghai fuel oil futures market on international oil markets. Their results indicate that the Daqing crude oil spot market is the price taker and is significantly influenced by the international oil markets, yet the Shanghai fuel oil futures market has a competitive advantage compared with the Singapore fuel oil market. Zhao et al. (2010) investigate price linkages between China, Brazil, the U.S. and Argentina, finding that there is a one-way or two-way leading relationship between domestic and international soybean markets; there has been a volatility spillover effect between the domestic soybean spot market and the futures market since the outbreak of the subprime mortgage crisis. Christofoletti et al. (2012) examine price linkages between soybean futures traded in China, the U.S, Brazil and Argentina for the period ranging from 2002 to 2011. Their findings show that U.S. prices still have a dominant role in explaining price changes in the international markets; their results also indicate stronger linkages between prices in China and in the other three markets, especially after 2006, suggesting that the Chinese market has become more integrated with the international markets in recent years. Hua et al. (2010) investigate the international linkage between the Chinese and other major world copper futures markets in term of the information spillover process. The lead-lag relationship between markets exists and the average Hasbrouck information

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Can China Gain Commodity Pricing Power

shares attributed to the SFE, LME, and NYMEX are 24%, 49% and 27%, respectively, proving that the LME dominates in price discovery in the copper futures market. Combining the commodity financialization with the interaction between futures markets, we know that sometimes the price movement is not driven by fundamental supply and demand and that the changes are not in the Chinese economy’s favor. Moreover, some bulk commodity prices, such as iron ore, are under the control of oligopolistic conglomerates, forcing China to pay higher prices for imports. Without pricing power in these markets, China’s bigger share in demand just means paying more for import: this is why China is yearning for futures market to gain pricing power. However, can China have a futures market guarantee pricing power? What determines the international influence of a futures market? The scale of a futures market may matter: on the one hand, the price in large market gains recognition easily for information reasons, and on the other one hand, a large market’s pricing bias cannot be easily rectified by inter-market arbitrage (Mou, 2011). Even mistakes in pricing can also have a repressive effect on other markets. 3.

Researching Method and Data

3.1. Research Method—Time-Varying Parameter Vector Auto-regression Since Sims (1980), the VAR has been widely used for econometric analysis, but the fixed coefficient assumption constrains its ability to be used for interpretation. Since the late 1990s, time-varying components have been incorporated into the VAR. Sims (1993) models the VAR with drifting coefficients. Cogley and Sargent (2003) use time-varying variances in the context of VARs with drifting coefficients but assume that the simultaneous relationships among variables are time invariant. Boivin (2001) considers time varying simultaneous relationships, but neglects the potential heteroscedasticity of the innovations. Ciccarelli and Rebucci (2003) extend the framework of Boivin (2001), allowing for non-persistent changes in the scale of the variances over time, which is accounted for by t-distributed errors. Primiceri (2005) proposes the Time-Varying Parameter Vector Auto-regression model, which allows all parameters to vary over time. After Primiceri, Time-Varying Parameter Vector Auto-regression has been used to analyze the time-varying structure of the macroeconomy in specific ways. Following Primiceri (2005), suppose that the futures market variables form a structured VAR model:

Ayt = F0 + F1 yt −1 + ... + Fs yt − s + ut

t = ( s + 1),...n

（1）

where yt 、F0 are k × 1 vectors，A , F1 ,..., Fs are k × k coefficient matrices, and

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 σ1 L ut is a k × 1 disturbance vector. Suppose ut : N (0, ΣΣ) and Σ =  M O 0 L 

0  M . σ k 

Structural impact factor A can be written as 0  1  a 1 A =  21  M M   ak1 ak 2

L L O L

0  0 M  1

（2）

Re-writing equation (1), we have

yt = B0 + B1 yt −1 + K + Bs yt − s + A−1Σε t ，

（3）

where ε t ~ N (0, I k ) ，and Bi = A−1 Fi . Stacking the elements in rows of

Bi (i=1,…,s) and defining X t = I k ⊗ (1, yt′−1 ,..., yt′− s ) ，where ⊗ denotes the Kronecker product, equation (3) can be written as

yt = X t β + A−1Σε t

（4）

Suppose all of the parameters in equation (4) are time-variant, written as βt , At and Σ t . Let at = (a21 , a31 , a32 , a41 ,L , ak ,k −1 )′ be the stacked vector of lower-triangular

elements in At , and ht = (h1t ,L , hkt )′ （ h jt = log σ 2jt ）(j=1,…, k). Assume that the parameters follow the random walk process: βt +1 = βt + uβ t

，

at +1 = at + uat

，

ht +1 = ht + uht , and

εt   I 0      uβ t  ~ N  0,  0 Σ β u   0 0  at     0 0  uht 

0 0 Σa 0

0   0  0   Σ h  

（ 5）

Usually, a data-generating process of futures market variables has drifting coefficients and stochastic volatility. It is applicable to model all of the parameters as a non-stationary random walk process, which is flexible enough to capture gradual

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Can China Gain Commodity Pricing Power

and sudden changes in the underlying economic structure. However, allowing time variation in every parameter may create too many parameters to estimate and cause over-identification. Therefore, the Markov Chain Monte Carlo (MCMC) method is used. 3.2. Data Of all of the futures contracts in China, copper is commonly believed to be the only commodity to have international influence. We therefore choose copper futures traded in the Shanghai Futures Exchange (SHFE), the London Metal Exchange (LME) and the New York Commodity Exchange (CMX) to study how the SHFE’s influence evolves with its market scale. Data for the study are the daily yields calculated by the closing prices and open positions of the SHFE Copper Index, the LME Copper Index and the CMX Copper Index, released by Wenhua Inc. (www.wenhua.com.cn). For the same trading day, the SHFE has no overlapping trading time with the other two markets and closes the earliest, LME has overlapping trading time with CMX but closes earlier, and we suppose that information is transmitted in that order. For holiday reasons, we omit any trading days where one exchange is closed for a holiday, ignoring compensatory effects. For contract size differences, the SHFE’s 5 tonnes/contract, the LME’s 25 tonnes/contract and the CMX’s 25,000 pounds/contract, we transform the LME’s and the CMX’s open position to the SHFE standard to compare the market scale by open positions. The sample interval is from September 1st, 2000 to September 28, 2012. The software used for the calculation is OxMetrics, using the program written by Jouchi Nakajima (2011), and we modify the program a little to extract coefficients.

3.

Empirical Analysis of SHFE, LME and CMX Influence

The Time-Varying Parameter Vector Auto-regression method can analyze not only the influence of lagged variables but also the synchronous influence amongst the variables, as revealed by the structure matrix At . When considering the quick response to new information, the synchronous influence coefficient is the key factor. The unit root test of the daily yield series of the three markets shows that they are all stable series. We choose the VAR with lag period 1 and no intercept. Given the order of closing times, we suppose that the latter closing market has no influence on the former closing market’s yield, but does have influence on its second day’s yield; this assumption is in accordance with Primiceri’s (2005) conclusion that At is sensitive to the order of the series. For the same trading day, the yields of the three markets have a VAR relationship as follows:

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yt ,s = bt11. yt −1, s + bt12 . yt −1,l + bt13 . yt −1,c + ε t , s yt ,l = at 21. yt , s + bt 21. yt −1, s + bt 22 . yt −1,l + bt 23 . yt −1,c + ε t ,l

（ 6）

yt ,c = at 31. yt , s + at 32 yt ,l + bt 21. yt −1, s + bt 22 . yt −1,l + bt 23 . yt −1,c + ε t ,c

The subscripts, s, l, and c represent the SHFE, the LME and the CMX, respectively. Equation (6) is transformed as  1 −a  t 21  − at 31

0 1 − at 32

0   yt , s   bt11 bt12   0  ⋅  yt ,l  = bt 21 bt 22 1   yt ,c  bt 31 bt 32

bt13   yt −1, s  ε t , s      bt 23   yt −1,l  +  ε t ,l  bt 33   yt −1,c  ε t ,c 

（ 7）

Equation (7) is a simplified form of equation (1). In consideration of the date change, when studying the influence of the SHFE copper contract, the VAR order is yt , s , yt ,l , yt ,c ; when studying the influences of the LME and the CMX, the orders are y t ,l , yt ,c , yt +1,s and yt ,c , y t +1, s , y t +1,l , respectively.

4.1. The Evolution of the SHFE Copper Contract Influence According to equation (7), the Time-Varying Parameter Vector Auto-regression analysis is performed in the order of [yt,s, yt,l, yt,c], getting the lower triangle matrix At. It can be concluded from equation (6) that at21 is the synchronous influence coefficient of the SHFE on the LME: the bigger at21, the stronger the SHFE’s influence on the LME. Similarly, the influence coefficient of the SHFE on the CMX can be calculated by (at31＋at21.at32). Figure 1 (A) describes the evolution of the influence coefficients of the SHFE on the LME and the CMX from September 2000 to September 2012. During 2002-2003, the SHFE has a strong influence on the LME and the CMX. Subsequently, the influence declines, and from approximately 2006, it rises again and fluctuates at high levels. There is no interpretation as to why the SHFE’s influence declines during 2003-2007 (of course, this may be the first paper that checks the evolution of the SHFE’s influence), but this decline coincides with the period when financial institutions cumulated large long positions in their portfolios. As previously mentioned, the authors believe that the market’s influence is supported by its scale, and this belief is supported by figure 1 (B). The horizontal axis in figure 1 (B) is the relative scale of the SHFE copper futures market (the ratio of the SHFE open position to the LME open position). For some time, the open position of LME copper declined significantly without reason: those pairs of influence coefficients and relative scales with open positions that deviate significantly from the moving average are omitted; the remaining combinations are shown in figure 1(B). It is obvious that as the relative scale of the SHFE increases, its influence on the LME and the CMX also increases.

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Can China Gain Commodity Pricing Power

The relationship between the relative scale and the influence coefficients is not in simple function form. The relationship has regime switching characteristics and is highly scattered; the bigger the relative scale, the higher the level of scattering. However, the relationship reveals that the relative scale is the pivotal factor in determining the influence coefficient, although it is not the only determining factor. Other factors, such as fundamental supply and demand, connecting channels between Chinese and international markets and world recognition, etc., all affect the influence coefficients. However, the relative scale needs to surpass a certain threshold, for example 10% in figure 1(B), as a prerequisite for the futures market to have an obvious influence in pricing. This finding does not conflict with the conclusion that small satellite markets can play important role in price discovery. Figure 1. The SHFE Influence Coefficients’ Evolution and the Relationship with Relative Scale S->L Infl. Coef.

S->L

1.4

1.40

1.2

1.20

1.0

1.00

0.8

0.80

0.6

0.60

0.4

0.40

0.2

0.20 Sep-12

Mar-12

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0.0

0.00 0.00

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0.20

S->C Infl. Coef.

0.30

0.40

0.50 0.60 S:L in Open Pos.

2.00

2.50 3.00 S:C in Open Pos.

S->C

1.4

1.40

1.2

1.20

1.0

1.00

0.8

0.80

0.6

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0.40 0.20

0.2 Sep-12

Mar-12

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Sep-01

Mar-01

Sep-00

0.0

0.00 0.00

0.50

（ A）

Figure 2.

1.00

1.50

（B ）

The Relationship between the Relative Stock and the Influence Coefficient S->L Infl. Coef. 1.2 1 0.8 0.6 0.4 0.2 0 0

2

4

6

8

10 12 Relative Stock

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The effect of the factors from the real economy on the influence coefficients is studied using data from the weekly relative stock, and the Relative Stock = (SHFE Stock/SHFE Open Position)/ (LME Stock / LME Open Position). The SHFE stock data come from the Essence Securities Weekly Data Report on Non-Ferrous Metals, and the sample interval is January 2003-September 2012. To match the data, the influence coefficients from the same stock dates are chosen. The relationship between the relative stock level and the influence coefficient is shown by figure 2. The result of figure 2 is surprising. It is generally believed that the stock level is tightly connected with real economic fundamentals: if the relative stock level is high, arbitrage trading is high; if the relative stock level is low, speculative trading is high. If supply and demand at real economic levels plays an important role in futures market pricing, there should be a positive relationship between the relative stock level and the influence coefficient, yet the result in figure 2 shows that it is negative. This result cannot be explained unless we take commodity financialization into consideration: under financialization, the financial factors play an increasingly important role and speculation determines the influence coefficient. Further study considering time shows that the high relative stock period coincides with the period where the SHFE influence coefficient decreases. This connection proves from another perspective that enhancing the scale of the futures market is a prerequisite to enhancing influence, even if the enhancement may be purely driven by financial factors. 4.2. Impulse Response Analysis of the SHFE Copper Futures Shocks Except for the synchronous influence among the three markets, impulse response can also show the influence of the SHFE. In figure 3, we choose four time points that represent the deflation, prosperity, post-crisis and recovery periods of China to check the impulse response relationships among markets. For reasons of closing times, the SHFE’s daily yield has a synchronous influence on the LME’s and the CMX’s yield, represented by the response in the lag period 0. Considering the aspect of impulse feedback, we can see that the impulse does not last long—it almost disappear after two periods—showing that the futures market can incorporate information quickly and reflect it in prices. To study how the SHFE affects the other two markets in the full sample interval, figure 4 describes the impulse response evolution results for a lag period of 0, 1 and 2. For the lag period 0, the impulse response is tightly connected with the coefficients in At. We can see that the responses of the LME and the CMX to the SHFE impulse experience a large fluctuation; the response of the CMX is greater than that of the LME, although they move almost synchronously. The sub-figure in position [2,3] reveals the response of the CMX to the LME. We can see that the LME maintains a significant influence on the CMX for the entire sample interval; after 2010, the response of the CMX is larger than the impulse in the LME itself.

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Can China Gain Commodity Pricing Power

Figure 3. Impulse Response Results for Four Time Points ε ↑ →s

ε ↑ →l

s

ε ↑ →c

s

2002.96 2008.95 2009.156 2012.163

0.02

s

0.02

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l

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0 ε↑ →c

0

10

Figure 4. Impulse Response Evolutions

0.03

ε ↑ →s s

0-period ahead 1-period 2-period

0.03

ε ↑ →l s

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2010

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2010

c

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ε ↑ →l

2005

2010

c

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2010

ε ↑ →c s

ε↑ →c

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2010

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2010

2005

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l

ε ↑ →c c

0.00 2005

2010

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Figure 5 Impulse Response Evolutions of the Three Markets

0.03

ε ↑ →l s

0.03

ε ↑ →c s

0.03

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ε↑ →c l

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（A）：SLC 0.03

εl ↑ → c

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ε↑ →s l

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（B）：LCS ε ↑ →s

ε ↑ →l c

c

ε ↑ →l s

0.02

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2010

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（C）：CSL Notes: “——”is the response of lag period 0; “– – –” is the response of lag period 1; “……” is the response of lag period 2.

4.3. Comparison Analysis of the Three Markets’ Influence Because structure matrix At is sensitive to the order of series, a VAR test is started using the LME and CMX to compare the influence of the three markets. When studying the LME’s influence, the series are in the order [yt,l, yt,c, yt+1,s]; when studying the CMX influence, the series are in the order [yt,c, yt+1,s, yt+1,l]. The impulse response results are combined in figure 5. The SLC shows the VAR series in the order [SHFE, LME, CMX]; LCS and CSL are organized in the same way. For the convenience of comparison, the units of the vertical axes are in the same scale. Figure 5 (A) shows that the SHFE has an obvious influence on LME and CMX, and the influence experiences large fluctuations; (B) shows that the LME has a strong influence on the CMX, growing stronger after 2011, but its influence on the SHFE is relatively weak, although stable; (C) shows that the CMX has only a weak influence

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Can China Gain Commodity Pricing Power

on the SHFE and the LME. Although in different orders, the third sub-figure in (A) is identical to the first sub-figure in (B); the third in (B) is identical to the first in (C); and the third in (C) is identical to the first in (A) (Theoretically, they all should be the same; biases are produced by errors in the MCMC). In summary, for copper futures pricing, the LME has an undisputably dominant status; its yield influences the CMX and the SHFE; the SHFE’s influence experienced a large fluctuation in the past decade, and now it is very near to the LME’s; and the CMX has always followed the LME. From this perspective, we can say that the SHFE has pricing power in copper market. However, the pricing power did not function as some specialists had hoped: it did not deliberately price copper low in China’s favor. Instead it made the price fluctuate more in accordance with the rhythm of China’s economy. A question regarding the nature of pricing power is now revealed.

5.

The Nature of Pricing Power—Confronting International Price Shocks

Bulk commodities, as the raw material of economic inputs, have a profound influence on the macro-economy: the rise of bulk commodities may depress the development of a country and even cause a recession. Under financialization, the spot prices are set in reference to the futures prices; if the price fluctuation is not in accordance with the cycle of the economy, it may hurt the economy. To clarify, a simple model is used to show how this mechanism works. Suppose in scenario 0 that there are two countries A and B; A has no commodity futures market but B does. In the futures market of country B, the futures prices are deeply influenced by the financial and the macroeconomic conditions, meaning that the price fluctuation is in accordance with country B’s cycle. Suppose that both economies are in equilibrium, facing a stochastic shock of yXt (X=A, B, yXt ~ N(0,0.01)); the futures market can forecast macroeconomic shocks and reflect it in the futures price changes pt = α . yBt . If commodity prices remain stable, the economic shocks can be fully realized; when spot commodity prices change synchronously with the futures prices, it has a depressing effect on the real economy, represented by rXt = − β . pt . The finally realized economic shock will be ( y Xt + rXt ) (with α .β < 1 ). We can see that if the shock in country A is negative but the shock in country B is positive, the economy in country A will be deeply depressed. Otherwise, if the shock in country A is positive, but the shock in country B is negative, the economy in country A will be propelled. Only when both countries face the same shock will country A’s economic shocks not be amplified. For comparison, suppose in scenario 1 that both countries have futures markets and they are connected by some channels. The futures prices in both markets are equal and are determined by equation pt = α .( y At + yBt ) / 2 ; then both economies will be

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affected by this price.

Figure 6 Futures Market and Economic Fluctuation Comparison 0.4 0.3 0.2 0.1 0 -0.1 1

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（B ） Figure 6 describes the simulation results. In figure 6 (A), ya represents the stochastic shock of country A, yra0 represents the realized economic fluctuation under scenario 0, and yra1 represents the realized economic fluctuation under scenario 1. The variables in (B) have the same meanings for country B. Looking at figure 6, it can be seen that, with the independent stochastic shocks, country A’s realized economic fluctuation in scenario 0 will be higher than the original shocks, but the realized economic fluctuation in scenario 1 will be lower than the original shocks. Country B’s situation is simply different; the realized economic fluctuation in scenario 1 is higher than in scenario 0 but lower than the original shocks. The result in figure 6 proves two functions of the futures market: stabilizing the economy and risk sharing. When both countries have futures markets, a commodity can be priced at a reasonable level and stabilize the entire world economy. When only one country has a futures market, the economic fluctuation of the country without a futures market will be amplified because she has no pricing power. In other words, pricing power will protect the economy from unfavorable fluctuation, but it will not price a commodity that is largely imported at lower prices.

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Can China Gain Commodity Pricing Power

Conclusion

The leading effect of futures prices on spot prices under commodity financialization and the international influence of a country’s futures market guarantees pricing power in commodity market. The pricing power of the futures market is not the power to raise or lower prices, but to wield the price discovery function of the futures market to maintain prices at a reasonable level and move in accordance with the fluctuation of the economy, confronting unfavorable movements. A Time-Varying Parameter Vector Auto-regression analysis about the SHFE, the LME and the CMX copper futures daily yield shows that the LME has a dominant influence on the copper price; the influence of the SHFE experienced a large fluctuation and regained its high level in recent years. The evolution of the SHFE’s influence coefficient shows that market influence has a tight relationship with the market scale and financial factors play a more important role than the real economy’s fundamentals. Expanding the market’s scale is a prerequisite to enhance the influence of China’s futures market in pricing. However, to further enhance the international influence of China’s futures markets, China should do some extra work in regime arrangement. Firstly, the connecting channels between China’s and the international futures markets should be strengthened. To enhance the influence of China’s futures market and to make China’s price widely accepted, the authority should remove the barriers affecting the capital flows between China’s and the international futures market to guarantee arbitrage trade between the markets and to allow foreign investors to trade in China’s futures markets. Secondly, enterprises should be encouraged to participate in the futures market for hedging. Currently, speculative trade takes a large share of the volume and the hedging trade is relatively small; this situation is not conducive to a combination of the futures markets and the real economy. Attracting more enterprises to use futures for hedging can serve the real economy and drive commodity prices to reflect more fundamental information. This assertion may be in conflict with the conclusion obtained in this paper; it can prevent the futures market from becoming a purely speculative market and provide base for market expansion. Thirdly, the futures market supervision and regulation should be treated more carefully. After the subprime mortgage crisis, there was a dispute about how to handle institutional investment in the commodity market because the positions accumulated by financial institutions have created an increase in prices. In the future, if foreign countries maintain the policy of allowing institutional investment in commodities, to confront international financial influence on prices, the Chinese authority will also have no choice but to allow financial institutions to include commodities in their portfolios. Even if the foreign countries’ policy returns to the situation from before 2000, in consideration of the huge OTC market (which China does not have), China should still think twice before making a decision regarding supervision and regulation.

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Acknowledgments: I would like to thank the helps and suggestions from editors and reviewers; I would also like to thank the help from staff of Dalian Commodity Exchange (DCE). Of course I am responsible for the paper.

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