MAY–JUNE 2004 87
The Chinese Economy, vol. 37, no. 3, May–June 2004, pp. 87–122.
© 2005 M.E. Sharpe, Inc. All rights reserved.
ISSN 1097–1475 / 2005 $9.50 + 0.00.
GONGMENG CHEN, MICHAEL FIRTH, AND YU XIN
The Price-Volume Relationship in
China’s Commodity Futures Markets
Abstract: This study examines the relationship between returns and trading volume of four actively traded commodity futures contracts in China. Correlation
analyses and Granger causality tests are used to investigate contemporaneous
and lead-lag relationships between trading volume and both signed and absolute
return. We find that the contemporaneous correlations between return and trading
volume are not significantly different from zero, and there is no linearly significant
causality following from trading volume to return or from return to trading volume. However, the contemporaneous correlations between absolute return and
trading volume are significantly positive in all futures markets, and there is a significant relationship of causality following from absolute return to trading volume, which contradicts the mixture of distributions hypothesis and supports the
sequential information arrival hypothesis in all of the futures markets examined
except for aluminum futures. We also find a significant causality following from
trading volume to absolute settlement-to-settlement return in the copper (subsample
1) futures market, but not in the copper (subsample 2) futures market.
In the early 1980s, China set about reforming its stagnant economy and this
involved, among other things, the adoption of market-oriented solutions to its
economic reform. During the first ten years of the reform, China saw significant growth in output and some modernization of its industries. An obvious
impediment to further sustainable growth was the lack of markets for financial assets and commodities. To remedy this shortcoming, China set up its
first stock market and its first commodities exchange in late 1990. The ensuing decade has witnessed many developments in these markets as investors
Gongmeng Chen is an associate professor in the School of Accounting and Finance, the
Hong Kong Polytechnic University. Michael Firth is a professor in the School of Accounting and Finance, the Hong Kong Polytechnic University. Yu Xin is an assistant professor in
the School of Business, Zhongshan University.
87
88
THE CHINESE ECONOMY
and regulators alike learn the intricacies of trading. Continuing improvements
in the stock and commodities markets are vital if China is to reap the full
rewards of its economic restructuring.
The focus of this article is to examine the price-volume relationships of China’s
commodities futures contracts. As in the developed markets of North America and
Europe, commodities futures trading allows investors to hedge or speculate on futures prices and this activity can help in the price-discovery process. Return-volume
studies are of interest as they may unearth dependencies that can form the basis of
profitable trading strategies, and this has implications for market efficiency.
A number of theories have been developed to explain the relationship between
return and volume, and they have been subject to substantial empirical testing in
the established markets of North America and Europe. However, the findings from
these studies have been mixed and no strong consensus has emerged on issues
such as whether returns lead volume or volume leads return. Even if consensus
had emerged, the embryonic state of China’s commodities futures markets and its
unique institutional features make foreign studies of limited relevance.
Our study examines the relationship between returns and trading volume of four
actively traded commodity futures contracts in China. Correlation analyses and
Granger causality tests are used to investigate contemporaneous and lead-lag relationships between trading volume and both signed and absolute return. Based on the
empirical results, we find that the contemporaneous correlations between return and
trading volume are not significantly different from zero, and there is no linearly
significant causality following from trading volume to return or from return to trading volume. However, the contemporaneous correlations between absolute return
and trading volume are significantly positive in all futures markets. There is a significant relationship of causality following from absolute return to trading volume,
which contradicts the mixture of distributions hypothesis and supports the sequential information arrival hypothesis in all of the futures markets examined except for
aluminum futures. We also find a significant causality following from trading volume to absolute settlement-to-settlement return in the copper (subsample 1) futures
market, but not in the copper (subsample 2) futures market.
Theory and Literature Review
This section briefly reviews the theoretical framework and the related empirical
evidence on the relationships between return and volume, and between absolute
return and volume from contemporaneous and causality (lead-lag) perspectives.
Contemporaneous Relationships Between Return and Volume
The contemporaneous relationship between return and trading volume helps reveal information about the symmetry of trading volumes in markets. Karpoff (1988)
and Suominen (1996) argue that the relationship between trading volume and re-
MAY–JUNE 2004 89
turn in futures markets should not be affected by the sign of return, since the costs
of taking short and long positions in futures markets are identical. Overwhelming
empirical evidence exists that the contemporaneous correlation between return
and volume is close to zero in futures markets (Karpoff 1987). For example,
Kocagil and Shachmurove (1998) found no significant contemporaneous relationship between return and volume, thus confirming the symmetry of trading in
futures markets.
Lead-lag Relationship Between Return and Volume
The lead-lag causality relationship between trading volume and return helps reveal the informational efficiency of futures markets. Blume, Easley, and O’Hara
(1994) developed a model to investigate the informational role of volume and its
applicability for technical analysis. They claimed that volume contains valuable
information about the quality of information, that is, the precision of the signal of
a price. Therefore, volume provides information that cannot be detected from price
alone, and current trading volume can be used to predict future price movements.
DeLong et al. (1990) proposed a noise-trading model, which predicts a positive
feedback relationship between return and volume. The positive causality relationship running from return to volume is consistent with the positive-feedback trading strategy of noise traders who trade on the basis of past price changes, while the
positive causality relationship from volume to return is consistent with the hypothesis that price change is caused by the trading strategies/actions of noise traders.
The fact that past values of volume Granger-causes the actual return can be
interpreted as evidence of informational inefficiency in futures markets. From an
empirical perspective, many studies have found that there is a statistically significant linear causality running from the past values of return to trading volume (for
example, Moosa and Al-Loughani, 1995; Kocagil and Shachmurove 1998), but
past observations of trading volume do not increase the ability to forecast return in
futures markets. On the other hand, Hiemstra and Jones (1994) provide evidence
of significant nonlinear Granger causality from trading volume to stock returns.
Following Hiemstra and Jones, Fujihara and Mougoue (1997) found a significant
bidirectional nonlinear causality relationship between return and trading volume
in three petroleum futures markets. Ciner (2002) found that nonlinear causality
from volume to return disappears in the Tokyo commodity futures markets when
the returns are adjusted for persistence in conditional volatility.
Contemporaneous Relationship Between Absolute Return and Volume
The basic supply and demand model1 (Crouch 1970; Clark 1973; and Westerfield
1977), the dispersion model2 (Epps and Epps 1976; Harris and Raviv 1993; and
Shalen 1993), and the information asymmetry model3 (Wang 1994) are often employed to explain the positive contemporaneous relationship between volume and
90
THE CHINESE ECONOMY
absolute return. No matter which model is used, the positive contemporaneous
relationship between absolute return and volume has been verified by overwhelming evidence (for example, Clark 1973; Cornell 1981; Tauchen and Pitts 1983;
Grammatikos and Saunders 1986; Bessembinder and Seguin 1993; Foster 1995;
Kocagil and Shachmurove 1998; and Ciner 2002). Generally, such a simultaneous
relationship would imply that these markets are highly liquid, with traders being
able to enter and exit as required.
Lead-lag Relationship Between Absolute Return and Volume
In contrast to the contemporaneous relationship, different theories predict different dynamic responses, namely, the lead-lag relationship. Specifically, the mixture
of distributions hypothesis (Tauchen and Pitts 1983; and Harris 1984, 1986, and
1987) argues that price change and volume have a joint response to information
due to their common distribution, which implies that trading volume and price
change synchronously in response to new information. Then, the complete information equilibrium is immediately attained in a single trading round without any
intermediate equilibrium. The implication is that with the mixture of distributions
hypothesis, there is no information in the past absolute return that can be used in
predicting future volume that is not already contained in past volume, and vice
versa.
In contrast, the sequential information arrival hypothesis (Copeland 1974 and
1976; Jennings, Starks, and Fellingham 1981) assumes that traders in a market
receive new information in a sequential, random fashion. The information signal
is observed separately by each individual, and trading occurs after each reception.
When all traders have observed the information signal, a final complete equilibrium is established. Thus, there is a series of intertemporal equilibria before the
final complete equilibrium is reached. This sequential information flow results in
past values of trading volume having the ability to predict current absolute return
and/or vice versa, which means that a causality relationship exists in both directions or either direction between absolute return and trading volume.
Analyzing the relationship of causality between trading volume and price volatility (absolute return) can help us investigate speculation and its relationship with
price volatility in the futures market, and it is extremely important for regulators in
deciding upon the desirability of market restrictions. As Garcia, Leuthold, and
Zapata (1986) have pointed out, increased trading volume may lead to increased
price variability, which suggests a need for greater regulatory restrictions to curb
speculative positions and daily trading activities; increased price volatility attracts
more trading, which suggests that there should be less regulation of traders and
their activities, and that further regulation may harm the price responsiveness of
futures; trading volume and price volatility increase and decrease simultaneously,
which indicates liquid and efficient markets where the response to new information is nearly instantaneous.
MAY–JUNE 2004 91
From an empirical perspective, Cornell (1981) found that the correlation between changes in price volatility and lead or lagged changes in volume was insignificant, which is consistent with the mixture of distributions hypothesis. Using
causality tests, Rutledge (1979) examined four-month trading periods for 136 different futures contracts for thirteen commodities during the mid-1970s. He finds
modest evidence that changes in price volatility may lead trading volume. Consistent with Smirlock and Starks (1988) and McCarthy and Najand (1993), Kocagil
and Shachmurove (1998) found that price volatility Granger-causes trading volume in almost all of the futures markets examined. Herbert (1995) and Ciner (2002)
found that lagged trading volume contains predictive power for current price volatility. These empirical results provide evidence against the mixture of distributions
hypothesis and, instead, support the sequential information arrival hypothesis.
A Review of Commodity Futures Markets in China
After the abandonment of the planned economy, prices of most commodities and
products were allowed to float according to supply and demand conditions. To
facilitate the pricing of commodities, China began planning for formal commodities exchanges in the late 1980s, and the first one, the China Zhengzhou Grain
Wholesale Market, was established in October 1990. The spot market quickly expanded to include futures transactions. Other exchanges opened shortly thereafter
and the Shenzhen Metal Exchange, established in 1991, introduced the first standardized futures contract in China. The futures markets are still regarded as experimental by the Chinese authorities and so the government monitors the exchanges quite closely and has enacted various reforms that aimed to improve efficiency and reduce market manipulation.
The devolution of power to cities and municipalities led to a profusion of exchanges, products, and brokerages. By the end of 1993 there were more than fifty
commodity futures products, more than fifty exchanges, and in excess of 1,000
brokerages. The uncontrolled growth led to inefficient duplication of products and
to cases of market manipulation and outright fraud. Faced with this situation, the
Chinese government enacted regulations at the end of 1993 and the beginning of
1994 with the aim of curbing the malpractices in the system and making the markets
more effective. After the regulations came into effect, the number of futures exchanges
was reduced to fourteen, the exchanges became not-for-profit organizations, the number
of commodities traded was reduced, restrictions were placed on investors, and a licensing system was introduced for brokers. The new laws, the establishment of clearer
powers for the regulators, and the institutional changes outlined above combined to
improve the credibility of the markets.
While the reforms of 1993 and 1994 resulted in significant improvements in
the effectiveness of commodity futures exchanges, market manipulation still persisted (Li 1999) and so the authorities launched a second round of reforms in late
1998 and early 1999. These reforms focused on enhancing market effectiveness
92
THE CHINESE ECONOMY
and efficiency. After the reforms, the number of exchanges fell to three, namely
the Shanghai Futures Exchange (SHFE), the Zhengzhou Commodity Exchange
(ZCE),4 and the Dalian Commodity Exchange (DCE). Commodity futures contracts were reduced to twelve, although only half of these have active markets.
Higher capital requirements and tougher licensing examinations led to a reduction
in the number of futures brokerage corporations to 213. The reforms expanded
and clarified the legal framework for futures markets, and legal enforcement has
been strengthened. Table 1 reports the detailed information of the two periods of
adjustments.
The reforms led to improvements in information disclosure, settlement procedures, and contract enforcement, as well as further standardization of contracts. In
their zeal to dampen speculative trading, the authorities have taken actions that
curtail liquidity and may inhibit the functions of futures markets. Among the actions taken by the authorities are the prohibition of using borrowed funds to finance trading, the prohibition of banks and similar institutions from involvement
in futures (for example, acting as a principal, agent, and lender), and the prohibition of state-owned enterprises (SOEs) from participating in the market beyond
the amount they invest in the spot market.
The main investors in commodity futures are companies, SOEs, and individual
investors. The relatively small lot size of contracts encourages smaller investors to
participate, and this enhances liquidity at the exchanges. In many respects, the
futures markets have been more successful than the spot markets (which are plagued
by commercial disputes). The futures exchanges are electronic and they have borrowed the best features of exchanges in other countries.
Research Design
Data and Data Processing
Four relatively active commodity futures—copper, aluminum, soybean, and
wheat—are the subjects of the analysis, and copper futures are further investigated
by dividing them into two subsamples. The sample periods are from January 4,
1999, to December 31, 2002, for aluminum and soybean futures; from January 4,
2000, to December 31, 2002, for wheat futures; from January 2, 1996, to December 31, 2002, for copper futures; from January 2, 1996, to December 31, 1998, for
the first subsample of copper futures; and from January 4, 1999, to December 31,
2002, for the second subsample of copper futures.
Summary statistics for China’s commodity futures markets are shown in Table
2, which shows some volatility in the turnover of different futures contracts. Natural rubber futures declined precipitously in 1998 although there is some recovery
in 2002. Green bean (mung bean) futures have fallen to virtually nothing in the
last few years. The four products we focus on in our study (copper, aluminum,
soybean, and wheat) have had significant trading volumes since 2000, although
Table 1
China’s Commodity Futures Markets: Exchanges and Products
Panel A: Commodity Futures Exchanges
Number
Name
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
>50
>50c
15
14
14
14
3
3
3
3
Zhengzhou Commodity Exchange (ZCE)
Dalian Commodity Exchange (DCE)
Shanghai Cereals and Oils Exchange
(SCOE)
Shanghai Commodity Exchange (SHCE)
Shanghai Metal Exchange (SHME)
Beijing Commodity Exchange (BCE)
China Commodity Futures Exchange
(CCFE)
Chongqing Commodity Exchange (CQCE)
Suzhou Commodity Exchange (SCE)
Shenyang Commodity Exchange (SYCE)
Shenzhen Metal Exchange (SME) etc.
Dalian Commodity Exchange (DCE)
Shanghai Cereals and Oils Exchange
(SCOE)
Shanghai Commodity Exchange (SHCE
Shanghai Metal Exchange (SHME)
Beijing Commodity Exchange (BCE)
China Commodity Futures Exchange (CCFE)
Zhengzhou Commodity Exchange
(ZCE)
Dalian Commodity Exchange (DCE)
Shanghai Futures Exchange (SHFE)a
Chongqing Commodity Exchange (CQCE)
Chengdu United Futures Exchange (CUFE)
Guangdong United Futures Exchange (GUFE)
Suzhou Commodity Exchange (SCE)
Shenyang Commodity Exchange (SYCE)
Shenzhen Metal Exchange (SME)
Tianjin United Futures Exchange (TUFE)
Changchun United Futures Exchange (CCUFE)b
(continued )
MAY–JUNE 2004 93
Zhengzhou Commodity Exchange (ZCE)
94
Table 1 (continued)
Panel B: Available Commodity Futures Exchanges
Number
Name
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
>50
>50c
35
35
35
35
12
12
12
12
Soybean, soybean meal, broomcorn,
red bean, coffee, cocoa, green bean,
beer barley, natural rubber, wheat, corn,
palm oil, long-grain rice, colza meal,
tung oil, aluminum, nickel, copper, tin,
veneer, steel, sugar, coal, rice, colza
oil, petroleum, etc.
Soybean, soybean meal, broomcorn, red bean,
coffee, cocoa, green bean, beer barley, natural
rubber, wheat, corn, palm oil, long-grain rice,
colza meal, tung oil, aluminum, nickel, copper,
tin, veneer, etc.
ZCE: wheat, green bean, red bean,
and peanut kernel
DCE: soybean, soybean meal, and
beer barley
SHFE: copper, aluminum, natural
rubber, plywood, and long-grain
rice
Sources: Rearrangements based on materials from China Futures Yearbook (1995); China Securities and Futures Statistical Yearbook (1998–
2002); China Securities Regulatory Commission 1999; Li 1999; Zhu 1999; and www.cfachina.org.
Notes: a In April 1999 the SHFE was established after the reorganization and merging of the SCOE, SHCE, and SHME.
In October 1995 Changchun United Futures Exchange (CCUFE) was closed due to irregular operations.
c
At the end of 1994, the actual number of actively traded commodities was much less than the reported number.
b
THE CHINESE ECONOMY
China’s Commodity Futures Markets: Exchanges and Products
MAY–JUNE 2004 95
soybean and copper clearly dominate. In fact, soybean futures traded on the DCE
and copper futures traded on the SHFE record the second highest volumes after
the CBOT and LME, respectively. Appendix 1 gives details of the four contracts
we examine.
The nearby futures closing and settlement prices are selected in this study. In
the meantime, to obtain an aggregate measure of trading activity in each market,
volume is summed up across all outstanding contracts for each trading day.
The Granger causality test requires that the related variables should be stationary.
Since previous studies have found strong evidence of both linear and nonlinear time
trends in raw trading volume series (Gallant, Rossi, and Tauchen 1992; Lee and Rui
2001), the raw trading volume series needs to be detrended to achieve stationarity.
The following regression is conducted to detrend the raw volume series.
LOG ( ALLVOLUME ) = c + α1 TREND + α 2 TREND * TREND + ε t
(1)
where LOG(ALLVOLUME) is the natural log of the daily total trading volume
(1,000 lots) for all contracts; TREND is the number of observations; and the residuals are the detrended trading volume series (DEVOLUME), which will be used
in following empirical tests. Here TREND is used to model the linear time trend,
and TREND*TREND is used to model the nonlinear time trend. In this study, if we
find that the coefficients of TREND and/or TREND*TREND are not significant,
the related terms will be excluded to obtain a new OLS regression, which will be
used to filter the raw volume series. The detailed results of detrended regressions
for raw trading volume are listed in Table 3.5 Generally, we find that linear and
nonlinear time trends both exist in the copper futures market, and that a linear time
trend exists in the aluminum and soybean futures markets. There is no linear and
nonlinear time trend in the wheat futures market.
Table 4 reports the results of ADF tests for the raw volume and detrended volume series. For all of the futures markets that were examined, the null hypotheses
of nonstationarity are significantly rejected for detrended volume, while sometimes the null hypothesis for raw volume cannot be consistently rejected in five
percent significant level. Therefore, it is appropriate to obtain stationary series
through detrending the raw volume series.
Table 5 presents the results of the ADF test for close-to-close and settlement-tosettlement returns, and close-to-close and settlement-to-settlement absolute returns.6
For all of the futures markets examined, the null hypotheses of nonstationarity are
significantly rejected for both the return and absolute return series.
Contemporaneous Correlation Test
Two kinds of contemporaneous correlation, the Pearson correlation and the
Spearman rank correlation, between return and trading volume, and between ab-
96
Table 2
Panel A: Yearly Turnover
1993
Yearly total turnover
770
1994
>6000
1995
1996
1997
9240.8
8411.9
6117.1
402.1 /
226.8 b
19.2 /
3.2 b
1276.8
742.0
79.5
2.3
2853.0
89.5
1274.2
na
1.9
96.2
551.8
398.2 /
271.9 b
217.2 /
10.7 b
777.7
1049.8
148.7
13.3
2487.0
1.1
81.5
na
0.4
41.9
488.8
1998
1999
2000
2001
2002
3298.6 a
2234.7
1607.3
3015.4
3948.1
464.8
424.9
503.6
651.1
918.8
8.3
38.5
73.1
199.5
319.2
30.9
675.5
na
78.6
2029.7
1.7
3.9
na
na
na
na
28.1
642.2
–
8.7
1091.8
0.5
–
–
–
–
–
88.5
760.6
21.3
156.1
4.1
–
–
–
–
–
–
5.3
1912.1
63.9
183.5
≈0
–
–
–
–
–
–
401.7
1925.5
157.7
225.2
≈0
–
–
–
–
–
–
Panel B: Turnover in Terms of Futures Products
Copper
na
na
Aluminum
na
na
Natural rubber
Soybean
Soybean meal
Wheat
Green bean
Long-grain rice
Veneer
Peanut kernel
Corn
Broomcorn
Red bean
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
683.3 /
407.6 b
135.1 /
48.0 b
772.6
223.4
67.1
11.0
2738.6
na
3109.4
na
637.7
na
414.6
THE CHINESE ECONOMY
China’s Commodity Futures Markets: Yearly Turnover and Turnover in Terms of Futures Products (billion yuan)
Coffee
Beer barley
Palm oil
Others
na
na
na
na
na
na
na
na
65.9
24.4
151.3
206.4
918.0
90.9
13.6
0.9
409.3
0.2
2.0
≈0
na
na
na
5.2
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
Sources: Rearrangements based on the materials from China Futures Yearbook (1995); China Securities and Futures Statistical Yearbook
(1998–2002); Zhu 1999; Futures Daily (bound edition, 1999); www.csrc.com.cn; and www.cfachina.org.
Notes: a The data are incomplete here since only the turnovers of ZCE, DCE, SCOE, SHCE, and SHME are included. The turnovers of other
exchanges are not included due to their closure after September 1998.
b
These data are the turnover from SHME.
MAY–JUNE 2004 97
98
The Results of Detrended Regressions for Raw Volume Series
Copper
(all-sample)
Copper
(subsample 1)
Copper
(subsample 2)
Aluminum
(all-sample)
Soybean
(all-sample)
Wheat
(all-sample)
Coefficient
(t-value)
Coefficient
(t-value)
Coefficient
(t-value)
Coefficient
(t-value)
Coefficient
(t-value)
Coefficient
(t-value)
C
1.650699
(36.00872)***
1.307664
(16.99048)***
TREND
0.001757
(14.16433)***
0.003622
(7.510610)***
0.000803
(3.267544)***
0.003515
(10.29034)***
TREND*TREND
–3.20E-07
(–4.554471)***
–1.75E-06
(–2.766765)***
5.36E-07
(2.184909)**
Variables
R Square
0.475640
0.342660
2.670619
(51.67322)***
0.326759
–0.134376
(–1.890408)*
4.470977
(73.42605)***
3.494225
(52.25392)***
0.001349
(4.630026)***
0.000463
(1.041529)
1.43E-07
(0.416114)
3.71E-07
(1.267737)
7.81E-07
(1.260722)
0.657025
0.364695
0.108044
Notes: LOG(ALLVOLUME) is the natural log of the daily total trading volume (1,000 lots) across all contracts for the related futures product;
TREND is the number of observations.
* significant at the 10 percent level; ** significant at the 5 percent level; *** significant at the 1 percent level.
THE CHINESE ECONOMY
Table 3
MAY–JUNE 2004 99
Table 4
ADF Test for the Volume and Detrended Volume Series
LOG(ALLVOLUME)
(with constant)
ADF value
Copper
All-sample
(1996–2002)
Subsample 1
(1996–98)
Subsample 2
(1999–2002)
DEVOLUME
(with constant)
ADF value
Lag 5
Lag 10
Lag 15
Lag 20
Lag 5
Lag 10
Lag 15
Lag 20
Lag 5
Lag 10
Lag 15
Lag 20
–5.945960***
–3.944368***
–3.204142**
–2.823842*
–4.718275***
–3.215168**
–2.842379*
–2.476234
–5.961207***
–4.056679***
–3.074064**
–2.709030*
–10.44399***
–7.335417***
–6.024102***
–5.157085***
–6.978020***
–5.035614***
–4.525588***
–3.817800***
–9.458976***
–6.842846***
–5.228225***
–4.584215***
Aluminum
All-sample
(1999–2002)
Lag 5
Lag 10
Lag 15
Lag 20
–2.950652**
–2.500955
–2.280528
–2.209501
–5.325152***
–4.083281***
–3.635678***
–3.577745***
Soybean
All-sample
(1999–2002)
Lag 5
Lag 10
Lag 15
Lag 20
–5.081443***
–3.438250**
–2.709105*
–2.172108
–7.390976***
–5.340795***
–4.457031***
–3.987804***
Wheat
All-sample
(2000–2)
Lag 5
Lag 10
Lag 15
Lag 20
–5.551519***
–4.185197***
–3.738460***
–2.825217*
–5.551519***
–4.185197***
–3.738460***
–2.825217*
Notes: LOG(ALLVOLUME) is the natural log of the daily total trading volume (1,000
lots) across all contracts for the related futures product; DEVOLUME is the detrended
trading volume series; DEOPIN is the detrended open interest series.
For ADF test with constant, the critical values are –2.57, –2.86, and –3.44 at the 10
percent, 5 percent, and 1 percent significant levels, respectively. The null hypothesis that
the series is nonstationary is rejected if the test statistic is greater than the critical value.
* significant at the 10 percent level; ** significant at the 5 percent level; *** significant
at the 1 percent level.
100
THE CHINESE ECONOMY
Figure 1 Null hypothesis test using F statistic
Null hypotheses: β1 = β2 = . . . = βm = 0; i.e., x(y) does not cause y(x)
Equation 4-2
Equation 4-3
Conclusions
Reject H0
Reject H0
Reject H0
H0 cannot be rejected
H0 cannot be rejected
H0 cannot be rejected
Reject H0
H0 cannot be rejected
Feedback relationship between
x and y
x causes y
y causes x
No relationship between x and y
(independent)
solute return and trading volume, are calculated. Based on the above analysis, the
contemporaneous correlation between return and volume should be zero due to
the absence of trading cost asymmetry in futures markets, and the contemporaneous correlation between absolute return and trading volume should be significantly positive.
Granger Causality Test
Granger (1969) developed a methodology to examine whether changes in one
series cause changes in another. If the current value of a time series, y, can be
better predicted by using the past value of another time series, x, than by not doing
so, and considering other relevant information including the past values of y, we
may conclude that x causes y (Pindyck and Rubinfeld 1998, 243). The following
two OLS regressions are used in the Granger causality test:
m
yt = α0 + ∑ α i y t −i +
i =1
m
xt = α0 +
∑α x
i
i =1
m
∑β x
i
i =1
t −i
+ εt
(2)
+ εt
(3)
m
t −i
+
∑β y
i
i =1
t −i
The F statistic is calculated to test the null hypothesis. The related conclusions are
listed in Figure 1.
As Gujarati has pointed out (1995, 623), the Granger causality test is very
sensitive to the number of lags used in the analysis. Thus, Davidson and Mackinnon
(1993) have suggested using more rather than fewer lags. From a practical viewpoint, if the Granger causality test is not very sensitive to the lag length, we will
have more confidence in our conclusions than if the results are very sensitive to
the lag length. Therefore, in this study we report the results of the Granger causality test using three methods. The first employs the lags determined by the AIC
(Akaike Information Criterion) and FPE (Final Prediction Error) critiques; 7 the
Table 5
ADF Test with Constant for the Return and Absolute Return Series
Copper
All-sample
(1996–2002)
Subsample 1
(1996–98)
Subsample 2
(1999–2002)
Aluminum
Settlement-tosettlement return
ADF value
Absolute close-toclose return
ADF value
Absolute settlement-tosettlement return
ADF value
Lag 5
Lag 10
Lag 15
Lag 20
Lag 5
Lag 10
Lag 15
Lag 20
Lag 5
Lag 10
Lag 15
Lag 20
–15.76983***
–10.89472***
–9.477387***
–8.853226***
–10.10844***
–6.832493***
–5.467781***
–5.654503***
–12.24358***
–8.682189***
–8.200146***
–6.957401***
–15.54872***
–10.97230***
–9.455237***
–8.906230***
–9.951283***
–6.937163***
–5.512255***
–5.724517***
–12.10984***
–8.661332***
–8.134053***
–6.944219***
–12.47691***
–8.690913***
–7.451506***
–6.770912***
–7.465509***
–5.396135***
–4.870229***
–4.460350***
–10.54105***
–7.255427***
–5.786084***
–5.008057***
–13.11204***
–9.126439***
–7.520050***
–7.002851***
–7.973142***
–5.936722***
–5.264226***
–4.867491***
–10.82742***
–7.233469***
–5.454933***
–4.782662***
Lag 5
Lag 10
Lag 15
Lag 20
–12.04447***
–7.872839***
–6.408500***
–5.443323***
–11.92862***
–7.838144***
–6.327634***
–5.566321***
–9.305675***
–5.762358***
–4.479372***
–3.790225***
–9.500588***
–5.709643***
–4.798337***
–3.939969***
(continued)
MAY–JUNE 2004 101
All-sample
(1999–2002)
Close-to-close
return
ADF value
102
Table 5 (continued)
Close-to-close
return
ADF value
Settlement-tosettlement return
ADF value
Absolute close-toclose return
ADF value
Absolute settlement-tosettlement return
ADF value
Soybean
All-sample
(1999–2002)
Lag 5
Lag 10
Lag 15
Lag 20
–12.16677***
–8.237777***
–7.312682***
–6.068303***
–12.15435***
–8.247613***
–7.332347***
–5.955664***
–12.14458***
–8.977853***
–6.956143***
–6.328290***
–11.82785***
–8.699855***
–7.029278***
–6.557952***
Wheat
All-sample
(2000–2002)
Lag 5
Lag 10
Lag 15
Lag 20
–11.12655***
–8.578416***
–7.093146***
–5.997040***
–11.04383***
–8.424210***
–7.105650***
–5.888453***
–10.38328***
–7.659530***
–6.337063***
–5.305653***
–10.75606***
–7.791635***
–6.335503***
–5.399861***
Notes: Close-to-close Return is the first difference of the LOG(NCLOSEP); Settlement-to-settlement Return is the first difference of the
LOG(NSETTLEP); Absolute Close-to-close Return is the absolute value of Close-to-close Return; Absolute Settlement-to-settlement Return is
the absolute value of Settlement-to-settlement Return.
For ADF test with constant, the critical values are –2.57, –2.86, and –3.44 at the 10 percent, 5 percent, and 1 percent significant levels,
respectively. The null hypothesis that the series is nonstationary is rejected if the test statistic is greater than the critical value.
* significant at the 10 percent level; ** significant at the 5 percent level; *** significant at the 1 percent level.
THE CHINESE ECONOMY
ADF Test with Constant for the Return and Absolute Return Series
MAY–JUNE 2004 103
second employs the lags determined by the SC (Schwarz Information Criterion)
critique; and the third employs the lags with 5, 10, and 15.8 By comparing the
empirical results of these three methods together, a more robust and appropriate
conclusion can be obtained.
In this study the linear causality relationship between return and trading volume is examined in China’s commodity futures markets. If there is significant
causality running from the past value of trading volume to return, this means that
informational efficiency is not achieved, and technical analysis can be used to
obtain abnormal profits. In the meantime, the linear causality relationship between
absolute return and trading volume is examined to ascertain whether the mixture
of distributions hypothesis or the sequential information arrival hypothesis is supported. If there is no significant causality from past values of absolute return to
trading volume, and vice versa, the mixture of distributions hypothesis is supported. This would suggest that there is no information in past observations of
absolute return that can be used to forecast trading volume, and vice versa. Further, this implies that in China’s futures markets, the dissemination of information
is contemporaneous, and the final equilibrium is immediately attained. However,
if there is significant causality from the past values of absolute return to trading
volume and/or vice versa, the sequential information arrival hypothesis will be
supported, which will suggest that there are several intermediate equilibria before
the final complete informational equilibrium is reached.
In addition, the test of causality between absolute return and trading volume
allows us to investigate speculation in China’s commodity futures markets. On the
one hand, increased trading volume that brings about increased absolute returns
may suggest that speculative activity is relatively high and that imposing further
regulation is necessary. On the other hand, increased absolute returns that lead to
greater trading volumes may suggest a more efficient market and less regulation.
Empirical Results
Contemporaneous Relationship Between Return and Trading Volume
The contemporaneous correlations between return and trading volume are reported
in panel A of Table 6. At the 5 percent significant level, based on the Pearson and
Spearman correlations no significant contemporaneous relationship exists between
close-to-close return and volume for all of the futures markets examined except for
copper (all-sample) futures. No significant contemporaneous relationship exists between settlement-to-settlement return and volume for copper (subsample 2), aluminum, and wheat futures; and a significant negative contemporaneous relationship
exists between settlement-to-settlement return and volume for copper (all-sample),
copper (subsample 1) and soybean futures in the Spearman correlation test.
The above facts mean that the short mechanism works rationally and normally
in the copper (subsample 2), aluminum, and wheat futures markets, but irratio-
104
Contemporaneous Correlation Between Return and Detrended Volume, and Between Absolute Return and Detrended
Volume
Panel A: Return vs. Detrended Volume
Copper
(all-sample)
Copper
(subsample 1)
Close-to-close return
vs. detrended volume
Settlement-to-settlement
return vs. detrended
volume
–0.050**
(0.039)
–0.039
(0.110)
–0.065*
(0.078)
–0.066*
(0.073)
Close-to-close return
vs. detrended volume
Settlement-to-settlement
return vs. detrended
volume
–0.057**
(0.019)
–0.058**
(0.017)
–0.065*
(0.076)
–0.080**
(0.030)
Copper
(subsample 2)
Aluminum
(all-sample)
Soybean
(all-sample)
Wheat
(all-sample)
Pearson Correlation
–0.025
(0.430)
0.001
(0.968)
–0.014
(0.666)
0.000
(0.997)
–0.052
(0.110)
–0.048
(0.139)
–0.036
(0.350)
–0.029
(0.442)
Spearman Correlation
–0.025
(0.439)
–0.013
(0.678)
–0.024
(0.456)
–0.009
(0.772)
–0.063*
(0.052)
–0.082**
(0.011)
–0.059
(0.118)
–0.067*
(0.079)
THE CHINESE ECONOMY
Table 6
Panel B: Absolute Return vs. Detrended Volume
Copper
(all-sample)
Absolute close-to-close
return vs. detrended
volume
Absolute settlement-tosettlement return vs.
detrended volume
Absolute close-to-close
return vs. detrended
volume
Absolute settlement-tosettlement return vs.
detrended volume
Copper
(subsample 1)
Copper
(subsample 2)
Aluminum
(all-sample)
Soybean
(all-sample)
Wheat
(all-sample)
0.461***
(0.000)
0.528***
(0.000)
Pearson Correlation
0.409***
(0.000)
0.182***
(0.000)
0.146***
(0.000)
0.477***
(0.000)
0.554***
(0.000)
0.420***
(0.000)
0.174***
(0.000)
0.147***
(0.000)
0.528***
(0.000)
0.568***
(0.000)
0.152***
(0.000)
0.272***
(0.000)
0.125***
(0.001)
0.564***
(0.000)
0.604***
(0.000)
0.158***
(0.000)
0.322***
(0.000)
0.122***
(0.001)
Spearman Correlation
0.514***
(0.000)
0.547***
(0.000)
0.011
(0.777)
MAY–JUNE 2004 105
Notes: Two-tailed p values for the related correlations are reported in brackets.
* significant at the 10 percent level; **significant at the 5 percent level; *** significant at the 1 percent level.
0.020
(0.605)
106
THE CHINESE ECONOMY
nally and abnormally in the copper (subsample 1) and soybean futures markets.
The asymmetry of trading volume exists in the copper (subsample 1) and soybean
futures markets.9 When settlement price decreases, trading volume increases significantly. The negative relationship may imply that the short mechanism works
irrationally and abnormally and dominates the long mechanism. Comparing the
results of two subsamples of copper futures, we find that during 1999–2002, the
short mechanism of copper futures worked more rationally and normally than it
did during 1996–98, which means that the asymmetry of trading volume has improved in the copper futures market during the sample period.
Lead-lag Relationship Between Return and Trading Volume
The results of the Grange causality test between return and trading volume are
reported in Table 7 and Table 8, and are summarized in Table 11. Generally, we
cannot find any consistently significant linear causality following from volume to
return or from return to volume in all examined futures markets for all three methods. This fact implies that volume has no linear information content to help generate abnormal profits, and does not provide any evidence that technical analysis
will be effective.
Contemporaneous Relationship Between Absolute Return and Trading
Volume
The contemporaneous correlations between absolute return and trading volume
are reported in panel B of Table 6. Based on the Pearson and Spearman correlations and at the 5 percent significant level, the contemporaneous correlation between absolute return and trading volume is significantly positive in all of the
futures markets that were examined except for wheat futures in the Pearson correlation test. Generally, this empirical result is consistent with models such as the
supply and demand model, the dispersion model, and the information asymmetry
model.
Comparing the correlations between absolute return and trading volume between two subsamples of copper futures, we find that the correlations decline during the sample period. Based on Wang’s information asymmetry model (Wang
1994), this fact implies that information asymmetry decreases and market efficiency improves during the sample period in the copper futures market because
the correlation between absolute return and trading volume increases with information asymmetry. Comparing the correlations between copper and aluminum,
and between soybean and wheat futures, we find that the information asymmetry
of copper and soybean futures is higher than that of aluminum and wheat futures,
which can be attributed to the presence of more participants with heterogeneous
information.
Table 7
Linear Granger Causality Test Between Return and Detrended Volume Based on the AIC, FPE, and SC Critiques
Panel A: Based on the AIC (Akaike Information Criterion) and FPE (Final Prediction Error) Critiques
Close-to-close return vs. detrended volume
Settlement-to-settlement return vs. detrended volume
From volume
to return
From return
to volume
Lags
From volume
to return
From return
to volume
All-sample
(1996–2002)
Subsample 1
(1996–98)
Subsample 2
(1999–2002)
9
0.77658
0.71172
9
1.02359
0.74387
3
0.23126
0.45165
3
0.40932
0.16574
5
1.74482
1.95862
5
1.10875
2.19396
Aluminum
All-sample
(1999–2002)
9
1.68818
1.38694
9
1.94979**
1.24111
Soybean
All-sample
(1999–2002)
4
1.46251
0.20551
3
2.31126
0.05960
Wheat
All-sample
(2000–2)
4
1.31421
1.06880
4
1.84630
1.09706
Copper
(continued)
MAY–JUNE 2004 107
Lags
108
Panel B Based on the SC (Schwarz Information Criterion) Critique
Close-to-close return vs. detrended volume
Settlement-to-settlement return vs. detrended volume
Lags
From volume
to return
From return
to volume
Lags
From volume
to return
From return
to volume
All-sample
(1996–2002)
Subsample 1
(1996–98)
Subsample 2
(1999–2002)
2
0.11879
0.52392
2
0.67580
0.31613
1
0.29594
0.16680
1
0.04183
0.17476
1
0.15588
1.13674
1
0.01609
0.38303
Aluminum
All-sample
(1999–2000)
4
1.36525
1.23794
4
0.67359
1.80331
Soybean
All-sample
(1999–2002)
2
2.26118
0.42868
2
3.35798**
0.20228
Wheat
All-sample
(2000–2)
1
2.93492
1.42261
1
2.41891
1.16154
Copper
Notes: F statistics are reported here; * significant at the 10 percent level (not reported here); ** significant at the 5 percent level; *** significant at the 1 percent level.
THE CHINESE ECONOMY
Table 7 (continued)
Table 8
Linear Granger Causality Test Between Return and Detrended Volume Based on the Lags 5, 10, and 15
Close-to-close return vs.
detrended volume
From volume
to return
Copper
All-sample
(1996–2002)
Subsample 1
(1996–98)
Subsample 2
(1999–2002)
All-sample
(1999–2002)
Soybean
All-sample
(1999–2002)
Wheat
All-sample
(2000–2)
0.331393
0.730975
0.642631
0.455184
0.951632
0.787862
1.744816
1.089396
1.031048
1.093063
1.498299
1.564683
1.192698
1.103504
0.956662
1.121400
1.338079
1.275280
0.518260
0.641688
0.559328
0.449333
0.491967
1.190331
1.958619
1.586119
1.593152
1.024649
1.297273
1.690346**
0.821008
0.767453
0.754352
1.034057
0.803234
1.448009
From volume
to return
0.333296
0.934696
0.856847
0.814817
1.357071
1.202592
1.108755
0.768905
0.882532
0.501442
1.705495
1.872231**
1.384029
1.199662
1.244629
1.439820
1.048911
1.044612
From return
to volume
0.494593
0.682297
0.571612
0.141576
0.375215
1.111652
2.193961
1.633980
1.498138
1.457517
1.222410
1.433560
0.581047
0.676898
0.771268
1.074678
0.808346
1.462815
Notes: F statistics are reported here; * significant at the 10 percent level (not reported here); ** significant at the 5 percent level; *** significant at the 1 percent level.
MAY–JUNE 2004 109
Aluminum
Lag 5
Lag 10
Lag 15
Lag 5
Lag 10
Lag 15
Lag 5
Lag 10
Lag 15
Lag 5
Lag 10
Lag 15
Lag 5
Lag 10
Lag 15
Lag 5
Lag 10
Lag 15
From return
to volume
Settlement-to-settlement return
vs. detrended voume
110
Table 9
Panel A: Based on the AIC (Akaike Information Criterion) and FPE (Final Prediction Error) Critiques
Absolute close-to-close return vs.
detrended volume
Absolute settlement-to-settlement
return vs. detrended voume
Lags
From volume
to absolute return
From absolute
return to volume
Lags
From volume
to absolute return
From absolute
return to volume
All-sample
(1996–2002)
Subsample 1
(1996–98)
Subsample 2
(1999–2002)
15
1.41590
3.41268***
15
1.54781
2.16539***
15
1.35710
2.23250***
10
2.38515***
1.30630
15
1.62951
1.89981**
18
1.26516
1.15656
Aluminum
All-sample
(1999–2002)
14
0.80317
1.29669
10
0.83407
1.05332
Soybean
All-sample
(1999–2002)
15
0.88238
1.90765**
13
1.20139
1.61199
Wheat
All-sample
(2000–2)
18
2.10944***
2.13255***
8
2.17910**
1.54223
Copper
THE CHINESE ECONOMY
Linear Granger Causality Test Between Absolute Return and Detrended Volume Based on the AIC, FPE, and SC Critiques
Panel B: Based on the SC (Schwarz Information Criterion) Critique
Copper
All-sample
(1996–2002)
Subsample 1
(1996–98)
Subsample 2
(1999–2002)
8
1.59592
5.05444***
8
2.09405**
2.91796***
4
1.82997
4.64379***
4
3.31771**
2.08900
5
1.90217
3.62245***
5
2.28007**
2.68641**
Aluminum
All-sample
(1999–2002)
5
1.28713
1.43727
4
1.41608
0.81084
Soybean
All-sample
(1999–2002)
4
0.53205
4.25630***
5
1.61581
2.54040**
Wheat
All-sample
(2000–2)
2
1.01046
4.94000***
2
1.76113
3.81457**
Notes: F statistics are reported here; * significant at the 10 percent level (not reported here); ** significant at the 5 percent level; *** significant at the 1 percent level.
MAY–JUNE 2004 111
112
Table 10
Absolute close-to-close return vs.
detrended volume
From volume
to absolute return
Copper
Aluminum
From absolute
return to volume
Absolute settlement-to-settlement
return vs. detrended voume
From volume
to absolute return
From absolute
return to volume
All-sample
(1996–2002)
Lag 5
Lag 10
Lag 15
2.235291**
1.439310
1.415902
6.373156***
3.855749***
3.412683***
3.299978***
1.828203
1.547810
3.402339***
2.386911***
2.165390***
Subsample 1
(1996–98)
Lag 5
Lag 10
Lag 15
1.585168
1.573586
1.357098
4.361544***
2.520393***
2.232500***
2.631581**
2.385145***
1.777910**
1.909160
1.306304
1.186552
Subsample 2
(1999–2002)
Lag 5
Lag 10
Lag 15
1.902174
1.991423**
1.629514
3.622450***
2.439831***
1.899812**
2.280074**
2.068806**
1.492020
2.686410**
1.698677
1.312642
All-sample
(1999–2002)
Lag 5
Lag 10
Lag 15
1.287133
1.033229
0.796915
1.437273
0.775732
1.187260
0.936202
0.834067
0.758732
1.539644
1.053324
1.014161
THE CHINESE ECONOMY
Linear Granger Causality Test Between Absolute Return and Detrended Volume Based on the Lags 5, 10, and 15
Soybean
All-sample
(1999–2002)
Wheat
All-sample
(2000–2002)
Lag 5
Lag 10
Lag 15
Lag 5
Lag 10
Lag 15
1.047262
0.728783
0.882377
0.696865
1.395079
1.409867
3.291560***
2.288313**
1.907646**
2.623888**
2.227784**
1.619870
1.615806
0.990241
1.143549
1.136712
1.848521**
1.451986
2.540399**
1.892346**
1.498381
1.641238
1.774947
1.472116
Notes: F statistics are reported here; * significant at the 10 percent level (not reported here); ** significant at the 5 percent level; *** significant at the 1 percent level.
MAY–JUNE 2004 113
114
Table 11
Return vs. detrended volume
AIC and FPE
SC
AIC and FPE
SC
Lags 5, 10, 15
C
NO
NO
NO
AR → VOL
AR → VOL
AR → VOL
C
NO
NO
NO
AR → VOL
AR → VOL
AR → VOL
C
NO
NO
NO
AR → VOL
AR → VOL
AR → VOL
Aluminum All-sample
(1999–2002)
C
NO
NO
NO
NO
NO
NO
Soybean
All-sample
(1999–2002)
C
NO
NO
NO
AR → VOL
AR → VOL
AR → VOL
Wheat
All-sample
(2000–2)
C
NO
NO
NO
AR → VOL
AR → VOL
AR → VOL
Copper
All-sample
(1996–2002)
Subsample 1
(1996–98)
Subsample 2
(1999–2002)
S
NO
NO
NO
AR → VOL
AR → VOL
AR → VOL
S
NO
NO
NO
VOL → AR
VOL → AR
VOL → AR
S
NO
NO
NO
NO
AR → VOL
VOL → AR
Copper
All-sample
(1996–2002)
Subsample 1
(1996–98)
Subsample 2
(1999–2002)
Lags 5, 10, 15
Absolute return vs. detrended volume
THE CHINESE ECONOMY
Comments on the Empirical Results of Tables 7, 8, 9, and 10
Aluminum All-sample
(1999–2002)
S
VOL → R
NO
NO
NO
NO
NO
Soybean
All-sample
(1999–2002)
S
NO
VOL → R
NO
NO
AR → VOL
AR → VOL
Wheat
All-sample
(2000–2)
S
NO
NO
NO
VOL → AR
AR → VOL
NO
Notes: Five percent significant level is employed in this table; C means close-to-close; S means settlement-to-settlement; VOL means
detrended volume; R means return; AR means absolute return; NO means no causality relationship; → means the direction of the related
causality.
MAY–JUNE 2004 115
116
THE CHINESE ECONOMY
Lead-lag Relationship Between Absolute Return and Trading Volume
The results of the Grange causality test between absolute return and trading volume are reported in Table 9 and Table 10, and are summarized in Table 11. Generally, for close-to-close absolute return, consistently significant causality followed
for all three methods from absolute return to trading volume in all of the futures
markets that were examined except for aluminum futures; the reverse causality
relationship does not consistently hold for these markets; and there is no significant relationship of causality between absolute return and trading volume in the
aluminum futures market.
The evidence of a significant causality from absolute return to trading volume
contradicts the mixture of distributions hypothesis and supports the sequential information arrival hypothesis. Thus, in China’s copper, soybean, and wheat futures markets, new information is absorbed sequentially, and the intermediate informational
equilibrium is reached before the final equilibrium is found; while in China’s aluminum futures market, the mixture of distributions hypothesis is supported, which means
that the dissemination of information is contemporaneous, and the final equilibrium
is immediately reached in the aluminum futures market.
For absolute settlement-to-settlement returns, we cannot find consistent results
among the three methods in the copper (subsample 2), soybean, and wheat futures
markets, which may be due to the special characteristics of settlement price.10 We
also find the same empirical results as in close-to-close absolute return in the aluminum and copper (all-sample) futures markets.
One interesting finding relates to the copper (subsample 1) futures market. There
is a consistently significant causality following from trading volume to absolute
settlement-to-settlement return in this market, which is definitely different from
the results based on close-to-close absolute return. This relationship of causality
from volume to absolute settlement-to-settlement return holds only for the first
subsample (1996–98), and not for the second subsample (1999–2002). The fact
that increased trading volume causes increased absolute return may suggest that
speculative activity was relatively high during the first subsample period. Then,
because of the effectiveness of the second more powerful adjustment by the authorities, this relationship of causality disappeared and speculation was reduced
during the second subsample period.
Conclusions
Based on the empirical results, the basic conclusions of this article are as follows:
(1) Generally, the contemporaneous correlations between return and trading
volume are not significantly different from zero, due to the absence of trading
cost asymmetry and the effectiveness of short mechanisms in China’s
commodity futures markets, except for the Spearman correlations between
settlement-to-settlement return and trading volume in the copper (subsample
MAY–JUNE 2004 117
1) and soybean futures markets. For the copper futures market, the asymmetry of trading volume improved during the sample period.
(2) There is no linearly significant causality following from trading volume to
return or from return to trading volume, so there is no indication that technical analysis can be used to earn superior returns.
(3) The contemporaneous correlations between absolute return and trading
volume are significantly positive in all futures markets, and the information
asymmetry decreases during the sample period for copper futures.
(4) The evidence of a significant relationship of causality following from
absolute return to trading volume contradicts the mixture of distributions
hypothesis and supports the sequential information arrival hypothesis in all of
the futures markets examined except for aluminum futures. Thus, in China’s
copper, soybean, and wheat futures markets, new information is absorbed
sequentially and the intermediate informational equilibrium is reached before
the final equilibrium is found. In China’s aluminum futures market, however,
the mixture of distributions hypothesis is supported, the dissemination of
information is contemporaneous, and the final equilibrium is immediately
reached.
(5) There is significant causality following from trading volume to absolute
settlement-to-settlement return in the copper (subsample 1) futures market,
but not in the copper (subsample 2) futures market. This may suggest that the
speculative activity is relatively high during the first subsample period.
Speculation then declined during the second subsample period because of the
effectiveness of the second powerful adjustment.
(6) Generally, market efficiency improved significantly during the second
subsample period in the copper futures market.
One interesting question is: Why does the aluminum futures market (mixture of
distributions hypothesis is supported) seem more efficient than the copper futures market
(sequential information arrival hypothesis is supported) in the context that copper futures are more active than aluminum futures and copper has been considered one of
successful futures products by the professionals in China? Conducting this investigation needs the behavioral finance interpretation and more detailed microstructure
information about the two futures markets, and we leave it for future studies.
This study examines the relationship of causality relying on the restrictive assumption of linearity. However, the issue of testing for a relationship of nonlinear
causality between two time series has recently received a considerable amount of
attention in the literature (for example, Hiemstra and Jones 1994; Fujihara and
Mougoue 1997; Ciner 2002). Therefore, nonlinear causality can be investigated in
a future study, since the univariate time series of futures prices and trading volumes are likely to be generated by nonlinear processes.
118
THE CHINESE ECONOMY
Notes
1. Starting from an initial position of equilibrium, a price change occurs due to the
change in demand. The related adjustment induces transactions to react to the change in
demand until a new equilibrium is reached. Thus, trading volume increases as price changes,
regardless of the direction of the changes.
2. Trading volume is positively related to the extent to which traders disagree when
they revise their price expectations, and an increase in the extent to which traders disagree
is associated with a larger absolute price change.
3. Investors are heterogeneous in their information and private investment opportunities. As the asymmetry of information increases, uninformed investors require a higher
discount in price when they buy contracts from informed investors to cover the risk of
trading against private information. Therefore, trading volume is always positively related to absolute return, and the correlation increases with the level of information asymmetry.
4. For a review of the development of the ZCE see Williams et al. (1998).
5. It should be noted that if the coefficients of TREND and/or TREND*TREND are not
significant, they will not be included in the detrending procedure.
6. Close-to-close Return is the first difference of the LOG(NCLOSEP); Settlement-tosettlement Return is the first difference of the LOG(NSETTLEP); Absolute Close-to-close
Return is the absolute value of Close-to-close Return; Absolute Settlement-to-settlement
Return is the absolute value of Settlement-to-settlement Return.
7. In this study, the AIC and FPE critiques have the same lags for all of the futures
markets examined.
8. For this method, a relationship of causality exists only if at least two of three F
values with 5, 10, 15 lags are significant at the 5 percent level.
9. This effect may also exist in wheat futures, since the Spearman correlation between
settlement-to-settlement return and volume are marginally and negatively significant in the
wheat futures market.
10. Settlement price is determined based on the weighted volume for the whole day, and
much noisier trading may be reflected in the settlement price series. However, the closing
price is determined by the last transaction; thus, the information on that day will have been
sufficiently absorbed in the closing price series.
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Appendix 1
Detailed Contract Information on Copper, Aluminum, Soybean, and Wheat Futures Products
Copper
Aluminum
SHFE
SHFE
Trading unit
5 tonnes/lot
5 tonnes/lot
Quotation unit
RMB/ton
RMB/ton
Tick size
RMB10/ton
RMB10/ton
Daily price limit
3 percent above or below the previous day’s
settlement price
3 percent above or below the previous day’s settlement
price
Contract months
January, February, March, April, May, June, July,
August, September, October, November, December
January, February, March, April, May, June, July,
August, September, October, November, December
Trading hours
9:00–11:30 A.M., 1:30–3:00 P.M., Monday to Friday
9:00–11:30 A.M., 1:30–3:00 P.M., Monday to Friday
Last trading day
15th of the delivery month
(postponed in the case of legal holidays)
15th of the delivery month
(postponed in the case of legal holidays)
Delivery period /
last delivery day
16th–20th of the delivery month
(postponed in the case of legal holidays)
16th–20th of the delivery month
(postponed in the case of legal holidays)
Transaction margin
5 percent of the contract value
5 percent of the contract value
Transaction fee
Less than 0.02 percent of the trading value
(including risk reserve payment)
Less than 0.02 percent of the trading value
(including risk reserve payment)
(continued )
MAY–JUNE 2004 121
Exchange
122
Appendix 1 (continued)
Soybean
Wheat
Exchange
DCE
ZCE
Trading unit
10 tonnes/lot
10 tonnes/lot
Quotation unit
RMB/ton
RMB/ton
Tick size
RMB1/ton
RMB1/ton
Daily price limit
3 percent above or below the previous day’s
settlement price
3 percent above or below the previous day’s
settlement price
Contract months
January, March, May, July, September, November
January, March, May, July, September, November
Trading hours
9:00–11:30 A.M., 1:30–3:00 P.M., Monday to Friday
9:00–11:30 A.M., 1:30–3:00 P.M., Monday to Friday
Last trading day
10th trading day of the delivery month
(postponed in the case of legal holidays)
7th last trading day of the delivery month
(postponed in the case of legal holidays)
Delivery period /
last delivery day
7th day after last trading day
(postponed in the case of legal holidays)
From the first trading day to the last trading day
during the delivery month
(postponed in the case of legal holidays)
Transaction margin
5 percent of the contract value
5 percent of the contract value
Transaction fee
RMB4/contract
RMB2/contract (including risk reserve payment)
Sources: China Securities and Futures Statistical Yearbook (1998–2002); www.shfe.com.cn; www.dce.com.cn; and www.czce.com.cn.
THE CHINESE ECONOMY
Detailed Contract Information on Copper, Aluminum, Soybean, and Wheat Futures Products