The Determinants of Price Volatility in China’s Commodity Futures
Markets 1
Yu Xin, Gongmeng Chen, and Michael Firth 2
ABSTRACT
This paper systematically investigates the main determinants of price volatility in China’s commodity
futures markets, including past price volatility, past unexpected returns, day effects, seasonality effects,
year effects, time to maturity, trading volume, open interest, expected and unexpected trading
activities, and asymmetrical effects of unexpected trading activities, especially for trading volume and
open interest and their components. Based on the empirical results, we find that (1) the copper futures
market was more mature and effective during 1999-2002 than during 1996-1998; (2) generally, there
was a significant positive effect on price volatility for trading volume and unexpected trading volume
in all four commodity futures markets, and during 1999-2002, the effect of unexpected trading volume
was higher than that of expected trading volume in the copper, aluminum, and soybean markets; (3)
the significant negative effect of open interest and expected open interest on price volatility are
observed mainly in copper, soybean, and wheat markets during 1999-2002, and we can not find any
consistent and significant effect of unexpected open interest on price volatility in all markets; (4) the
asymmetry effect of unexpected volume on price volatility mainly existed in copper and soybean
markets, and the asymmetry effect of unexpected open interest on price volatility mainly existed in the
aluminum futures market. Finally, we discuss the policy implications based on the empirical results.
Key Words: commodity futures, price volatility, determinants, trading volume and its decomposition,
open interest and its decomposition
I. INTRODUCTION
Due to the rapid economic growth after 1979, China has become one of the largest countries in the
production and consumption of many important commodities, such as soybean, copper, petroleum,
and so on. In this context, it is very important to have correct pricing for these products. However, the
1
The authors appreciate the constructive suggestions and/or comments given by Dr. Yu Weifeng (the executive editor
of China Accounting and Finance Review), the anonymous reviewer, and Dr. Fan Xinting.
2
Xin Yu, assistant professor, Department of Finance and Investment, School of Business, Zhongshan University, No.
135, Xingang West Road, Guangzhou City, Guangdong Province, China (510275); Chen Gongmeng, associate
professor, School of Accounting and Finance, The Hong Kong Polytechnic University, Kowloon, Hong Kong; Firth
Michael, chair professor, School of Accounting and Finance, The Hong Kong Polytechnic University, Kowloon, Hong
Kong.
1
current status is unsatisfactory due to there being no developed commodity futures markets to play the
role of pricing center, price discovery, and risk hedging. Thus, the development of commodity futures
markets is very important in China. Therefore, empirical research for China’s commodity futures
markets and a detailed comparison to Western mature futures markets is very helpful for us to
understand the current status and further consider the future direction.
This study contributes to the current literature, since the price behaviors of China’s commodity
futures markets have not been systematically investigated before. Specifically, we empirically
examine the determinants of futures price volatility, including past price volatility, past unexpected
return, seasonality effect, day effect (calendar anomalies), year effect, time-to-maturity, trading
volume and its components, open interest and its components, and the asymmetrical effect of
unexpected components of trading volume and open interest.
The paper proceeds as follows. Section 2 gives a brief discussion of China’s commodity futures
markets and their historical background. Section 3 describes our research design and the data. The
results are reported and discussed in section 4. The last section concludes the study and gives a policy
implication.
II. THE DEVELOPMENT OF CHINA’S COMMODITY FUTURES MARKETS
When China operated under a planned economy, there was no need for a commodity futures market to
discover prices or hedge against price fluctuations because the government controlled the prices of
almost all commodities. However, in the 1980s China began the transformation from a planned
economy to a market economy. Following the implementation of price system reform, the scope of
market-adjusted prices has increased continuously. Thus, commodity futures markets are necessary in
China. In October 1990, the China Zhengzhou Grain Wholesale Market was founded. This market,
which was based on spot transactions, introduced the mechanism of futures transactions, and was the
first commodity futures market in China. In 1991, the Shenzhen Metal Exchange was established, and
this exchange introduced the earliest standardized aluminum futures contract. Not surprisingly,
China’s futures markets expanded rapidly and irrationally in the initial stage of development. By the
end of 1993, over 50 futures exchanges had been founded in China, providing over 50 kinds of futures
products. In the meantime, there were over 1,000 futures brokerage corporations in China.
The uncontrolled growth led to a set of serious problems, including inefficient duplication of
products, market manipulation and outright fraud, and dramatic ups and downs of futures markets. To
solve these problems, the Chinese government enacted regulations at the end of 1993 and the
beginning of 1994, and the oversight and monitoring responsibilities were more clearly delineated to
the China Securities Regulatory Commision (CSRC). After the regulations came into effect, the
2
number of futures exchanges was reduced to 15 3 , the number of commodities traded was reduced to
35, the number of futures brokerage corporations was reduced to 330 due to the introduction of a
licensing system, and the exchanges became not-for-profit organizations. These measures were aimed
at improving the credibility of the markets and reducing the most rampant cases of fraud. Nevertheless,
some inefficiencies and market manipulation persisted and so the authorities introduced further
reforms at the end of 1998.
The second round of reforms were introduced in late 1998. The number of futures exchanges was
reduced to three, namely the Shanghai Futures Exchange (SHFE), the Zhengzhou Commodity
Exchange (ZCE), and the Dalian Commodity Exchange (DCE). Just twelve commodity futures
products were allowed to be traded, and only six to seven products had real transactions. In the
meantime, the number of futures brokerage corporations fell to 213 due to minimum capital
requirements and the need to pass licensing examinations. In June 1999, the State Council
promulgated the Temporary Decree on Futures Transactions (Decree) and four implementing rules,
which took effect from September 1, 1999. The enforcement of this Decree means that the legal
framework for futures markets was constructed and the legal regulation was strengthened.
Although this second powerful adjustment led to continuous decrease 4 in transactions, the reforms
also led to improvements in information disclosure, settlement procedures, and contract enforcement,
and the further standardization of contracts, which provided a better base for further development. A
recovery in activity can be observed from 2001, and the transactions increased dramatically. In 2003,
the turnover reached 10838.9 billion yuan RMB, which was higher than the previous highest turnover
in 1995 (9240.8 billion yuan RMB).
Consistent with above analysis, table 1 reports the basic information of China’s commodity futures
markets. We also list the yearly turnover of four products that will be examined in this study.
Generally, the transactions of copper and soybean futures are more active than those of aluminum and
wheat futures, although the transactions of aluminum and wheat futures still are relatively active. All
four products experienced a stable growth in transactions during recent years. In 2003, all products
experienced a dramatic growth in transactions except aluminum.
Table 1 The Basic Information of China’s Commodity Futures Markets
1995
1996
1997
1998
1999
2000
No.
of 15
14
14
14
3
3
Exchanges
No. of Products 35
35
35
35
12
12
213
175
No. of Brokers 330
326
293
279
Turnover
9240.8 8411.9 6117.1 3298.6 2234.7 1607.3
3
2001
3
2002
3
2003
3
12
163
3015.4
12
170
3948.1
12
189
10838.
In 1996, the Changchun United Exchange was closed due to irregular operation, and the number of exchanges
decreased to 14.
4
In 2000, the trading volume dropped to bottom with a turnover of RMB 1607.3 billion, or just seventeen per cent of
the turnover in 1995.
3
(billion
RMB)
Copper
yuan
Alumin
9
683.3
135.1
402.1
19.2
398.2
217.2
464.8
8.3
424.9
38.5
503.6
73.1
651.1
199.5
918.8
319.2
2162.2
321.3
um
Soybean
223.4
742.0
1049.8 675.5
642.2
760.6
1912.1 1925.5 3332.2
Wheat
11.0
2.3
13.3
78.6
8.7
156.1
183.5
225.2
795.3
Sources: Adapted from data in China Securities and Futures Statistical Yearbook (1998-2002), and
www.cfachina.org.
After about ten years of development, China’s soybean (listed in DCE) and copper (listed in SHFE)
futures have become the second largest in the world, being just smaller than the CBOT and LME,
respectively.
III. RESEARCH DESIGN
Understanding price volatility is important for commodity traders, market participants, exchanges and
market regulators. First, because futures prices are often used to set contract prices in a spot
commodity market, then to some extent the volatility of futures prices implies the extent of the risk of
holding the related commodity. Second, knowledge of price volatility is very helpful for exchanges in
setting margins and for regulators in monitoring futures markets. Third, to a great extent the
perceptions and transactions of market participants are affected by price volatility.
Therefore, the determinants of price volatility in futures markets has received a great deal of
attention in recent years, including time-to-maturity effect, calendar anomalies, seasonality/month
effect, year effect, ratio of speculators to hedgers, market concentration, trading volume, open interest,
market conditions, and natural conditions.
1. Sample and Data
Based on previous analysis and the availability of data 5 , four relatively active commodity futures,
copper, aluminum, soybean, and wheat are the subjects of our study. Copper futures are further
investigated by dividing them into two sub-samples to evaluate the policy effect of the second policy
adjustment by the government. 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 sub-sample of copper futures, and from January 4, 1999 to December 31, 2002 for
the second sub-sample of copper futures. All raw data are downloaded from the websites of three
5
The related websites do not provide the data before 1999 for soybean futures, before 2000 for wheat futures; the data
before 1996 for copper futures are not complete, which means that our time series procedure can not be conducted; the
yearly turnovers of aluminum futures before 1999 changed dramatically (please see table 1) and are not consistent
among different years, thus the sample of aluminum futures exclude the data before 1999.
4
futures exchanges, the Shanghai Futures Exchange, the Dalian Commodity Exchange, and the
Zhengzhou Commodity Exchange.
Following previous studies, a nearby price 6 is selected to construct a rollover time series. In
common with previous research, the returns ( Rt ) are computed as the first difference in the
logarithms of the daily closing prices. In the meantime, to obtain an aggregate measure of trading
activity in each market, volume ( ALLVOLUMEt ) and open interest ( ALLOPIN t ) are summed up
across all outstanding contracts for each trading day, respectively.
2. Detrended Adjustment for Raw Trading Volume and Open Interest Series
Since previous studies have found strong evidence of both linear and nonlinear time trends in raw
trading volume and/or open interest series (Gallant, Rossi, and Tauchen, 1992; Lee and Rui, 2001),
the raw trading volume and/or open interest series needs to be detrended to achieve stationarity. The
following regressions are conducted to detrend the raw volume and/or open interest series.
ln( ALLVOLUME )t = c + α1TRENDt + α 2TRENDt * TRENDt + ε t
(1)
ln( ALLOPIN )t = c + α1TRENDt + α 2TRENDt * TRENDt + ηt
(2)
where ln( ALLVOLUME)t is the natural log of the daily total trading volume (1000 lots) for all
contracts; ln( ALLOPIN )t is the natural log of the daily total open interest (1000 lots) for all
contracts; TRENDt is the number of observations; and the residuals of equation (1) and (2) are the
detrended trading volume series DEVOLUMEt and detrended open interest series DEOPIN t ,
respectively, which will be used in following empirical tests. Here TRENDt is used to model the
linear time trend, and TRENDt * TRENDt is used to model the nonlinear time trend. In this study,
if we find that the coefficients of TRENDt and/or TRENDt * TRENDt 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 and open interest
are listed in table 2. Generally, we find that linear and nonlinear time trends both exist in the copper
futures market, and that there is no linear and nonlinear time trend in the wheat futures market. In the
aluminum and soybean futures markets, a linear time trend exists for raw trading volume, and both
linear and nonlinear time trends exist for raw open interest.
6
A switch from the nearby contracts to the contracts next nearest to delivery is made during the delivery month of the
nearby contracts. By constructing data in this way, all price data within the delivery month are excluded to avoid the
possibility of noise during the delivery month.
5
3. The Measures of Price Volatility
Based on the previous literature (Schwert, 1990; Jones, Kaul, and Lipson, 1994; Fung and Patterson,
2001), we employ a two-pass procedure to obtain a residual-based volatility estimator. To obtain this
estimator, the following regression model is conducted:
5
15
k =1
j =1
Rt = ∑α k Dkt + ∑ β j Rt − j + ε t
(3)
where Rt is the close-to-close return (the first difference of the natural log of the nearby futures
closing price) on day t; and the Dkt variables are the five day-of-the-week dummy variables, which
are used to capture the differences in mean returns. The 15 lagged returns are employed as regressors
to estimate the short-run movement in conditional expected returns. Then, the absolute value of the
residuals ( ε t ) 7 from equation (4) is used to measure the residual-based daily price volatility (PVt).
In the meantime, due to robustness reason, we also consider the following two measures of price
volatility (GK price volatility and HL price volatility) based on the Garman and Klass (1980) and
Serletis (1991):
⎧1
2
2⎫
PVGK t = ⎨ [ln( Pht ) − ln( Plt )] − (2 ln 2 − 1)[ln( Pot ) − ln( Pct )] ⎬
⎩2
⎭
PVHLt =
0.5
[ln( Pht ) − ln( Plt )]2
4 ln 2
(4)
(5)
where Pht , Plt , Pot , Pct are the nearby high, low, opening and closing futures prices on day t ,
respectively.
Table 2 Detrended Procedure for Raw Trading Volume and Open Interest Series
Copper
Copper 1
Copper 2
Aluminum
Soybean
(96~02)
(96~98)
(99~02)
(99~02)
(99~02)
Dependent Variable: Trading Volume [ln(ALLVOLUME)t]
Independ Coefficients Coefficients Coefficients Coefficients Coefficients
ent
(T Statistics) (T Statistics) (T Statistics) (T Statistics) (T Statistics)
Variable
s
C
1.6507
1.3077
2.6706
-0.1344
4.4710
(36.009***) (16.990***) (51.673***) (-1.890*)
(73.426***)
TREND
0.0018
0.0036
0.0008
0.0035
0.0013
(14.164***) (7.511***)
(3.268***)
(10.290***) (4.630***)
TREND* -3.20E-07
-1.75E-06
5.36E-07
1.43E-07
3.71E-07
TREND
(-4.555***) (-2.767***) (2.185**)
(0.416)
(1.268)
0.4756
0.3427
0.3268
0.6570
0.3647
R2
7
Wheat
(00~02)
Coefficients
(T Statistics)
3.4942
(52.254***)
0.0005
(1.042)
7.81E-07
(1.261)
0.1080
The lags in residuals ( ε t ) will be considered as past unexpected returns (UNEXRt), which are used in the
determinants regressions.
6
Dependent Variable: Open Interest [ln(ALLOPIN)t]
Independ Coefficients Coefficients Coefficients Coefficients Coefficients Coefficients
(T Statistics) (T Statistics) (T Statistics) (T Statistics) (T Statistics) (T Statistics)
ent
Variable
s
C
3.8032
3.8107
4.3758
2.6338
5.1332
4.9286
(290.300*** (156.288*** (363.535*** (99.799***) (241.516*** (104.709***
)
)
)
)
)
TREND
0.0010
0.0007
0.0015
0.0003
0.0030
0.0002
(27.147***) (4.436***)
(25.338***) (7.123***)
(29.138***) (0.773)
TREND* 2.75E-09
6.85E-07
-3.72E-07
8.06E-07
-9.39E-07
6.16E-07
TREND
(0.137)
(3.412***)
(-6.520***) (6.306***)
(-9.195***) (1.413)
0.8750
0.5703
0.8580
0.7483
0.8737
0.0986
R2
***, **, and * means significant at the one percent, five percent, and ten percent level, respectively.
In addition, as one part of robustness test, we also calculate the trend-adjusted price volatility
(similarly for trading volume and open interest) for the three kinds of price volatility measures to
conduct the related regressions. The empirical results (not report here) do not change significantly.
4. The Decomposition of Detrended Trading Volume and Open Interest
Following the procedures of Bessembinder and Seguin (1993), we decompose the trading activities
(trading volume and open interest) into expected and unexpected components. By observing the
correlograms associated with the autocorrelation function (ACF) and the partial autocorrelation
function (PACF), we partition the detrended trading volume (DEVOLUME) using an AR(10), and
partition the detrended open interest (DEOPIN) using an AR(2). This step yields the one-step-ahead
forecast errors ( μ t ) for trading volume and open interest series respectively, which is calculated as
DEVOLUME (DEOPIN) minus the fitted value of the AR process for DEVOLUME (DEOPIN).
The two one-step-ahead forecast errors ( μ t ) for DEVOLUME and DEOPIN are then regressed
against lags in residual-based volatility (PV), lags in trading volume (DEVOLUME), lags in open
interest (DEOPIN) and days until the expiration of the next contract (TTM), to capture the predictive
power of these variables, respectively. That is,
5
5
5
j =1
k =1
m =1
μt = α + ∑ β j PVt − j + ∑ γ k DEVOLUMEt − k + ∑ϕ m DEOPINt − m + φTTM t + ν t
(6)
Finally, the unexpected component (UNEXVOL, unexpected trading volume, and UNEXOPIN,
unexpected open interest) 8 of each series is defined as
8
ν t , the residuals of the Equation (6-5); while
When we consider the unexpected trading volume (and/or open interest) as the determinants of price volatility, it is
mainly based on a logical and theoretical analysis. We do not specially emphasize the direction of causality, since we
can not employ the current unexpected trading volume (and/or open interest) to predict current price volatility. We
thank the anonymous reviewer for this point.
7
the expected component is defined as the difference between the detrended trading activities and the
unexpected components as follows:
EXVOLt = DEVOLUMEt − UNEXVOLt
(7)
EXOPIN t = DEOPIN t − UNEXOPIN t
(8)
5. The Regression Models
The following four OLS regressions are conducted in this study:
5
5
3
4
i =1
j =1
m =1
k =1
PVt = c + ∑ α i PVt − i + ∑ β jUNEXRt − j + ∑ηm S m + ∑ γ k DAYk + ∑ λnYEARn +
n
ρTTM t + ε t
(9)
5
5
3
4
i =1
j =1
m =1
k =1
PVt = c + ∑ α i PVt − i + ∑ β jUNEXRt − j + ∑ηm S m + ∑ γ k DAYk + ∑ λnYEARn +
n
ρTTM t + φ1DEVOLUMEt + ϕ1DEOPINt + ε t
(10)
5
5
3
4
i =1
j =1
m =1
k =1
PVt = c + ∑α i PVt − i + ∑ β jUNEXRt − j + ∑ηm S m + ∑ γ k DAYk + ∑ λnYEARn +
n
ρTTM t + φ1EXVOLt + φ2UNEXVOLt + ϕ1EXOPINt + ϕ 2UNEXOPINt + ε t
5
5
3
4
i =1
j =1
m =1
k =1
(11)
PVt = c + ∑ α i PVt − i + ∑ β jUNEXRt − j + ∑ηm S m + ∑ γ k DAYk + ∑ λnYEARn +
n
ρTTM t + φ1EXVOLt + φ2UNEXVOLt + φ3VOLDUMMYt * UNEXVOLt +
ϕ1 EXOPIN t + ϕ 2UNEXOPIN t + ϕ 3OPINDUMMY t *UNEXOPIN t + ε t
(12)
where PVt is the residual-based price volatility series (GK price volatility and HL price volatility in
robustness tests); UNEXRt is the unexpected return series; S1-S3 variables are the seasonality (Spring:
March, April, and May; Summer: June, July, and August; and Autumn: September, October, and
November) dummy variables; DAY1-DAY4 variables are the day dummy variables (Monday, Tuesday,
Wednesday, and Thursday);. YEAR96-YEAR01 variables are the year (1996-2001) dummy variables;
TTMt is the number of days until the expiration of the next contract to measure the time to maturity;
DEVOLUMEt is the detrended trading volume series; DEOPINt is the detrended open interest series;
UNEXVOLt is the unexpected trading volume; UNEXOPINt is the unexpected open interest; EXVOLt
is the expected trading volume; EXOPINt is the expected open interest; VOLDUMMYt is a dummy
variable, if UNEXVOLt>0, then VOLDUMMYt=1, otherwise VOLDUMMYt =0; OPINDUMMYt is a
dummy variable, if UNEXOPINt>0, then OPINDUMMYt=1, otherwise OPINDUMMYt=0.
Past price volatility, past unexpected return, seasonality dummy variables, day dummy variables,
year dummy variables and the time-to-maturity variable are included in all four equations as control
8
variables. In addition, equation (10) includes the trading activities variables of trading volume and
open interest; equation (11) includes expected and unexpected trading activities; equation (12)
includes expected and unexpected trading activities and the interactions between unexpected trading
activities and dummy variables, which are used to examine the asymmetrical effect of unexpected
components. The analysis for equations (10), (11) and (12) is based on the trading activities (that is,
trading volume and open interest) and their components.
To control for the autocorrelation and/or heteroskedasticity in the error terms, we employ the
Newey-West T-test to measure statistical significance in models (9)~(12).
IV. EMPIRICAL RESULTS
Table 3 reports the descriptive statistics for the raw trading volume, raw open interest, return, and the
residual-based price volatility; table 4 reports the empirical results of model (9); table 5 presents the
empirical results of models (10), (11) and (12); and table 6 provides the results of the robustness tests
for models (10), (11) and (12).
Based on the descriptive statistics in table 3, we find that not all the four variables are normally
distributed. It should be noted that the return and price volatility for wheat futures are characterized by
especially high kurtosis and skewness, which indicates that the empirical distributions of these two
variables of wheat futures have very significant left skewness and fat tails. 9 This reminds us that we
should pay more attention to wheat futures market when we conduct our analyses. 10
Table 3 Descriptive Statistics for the Raw
Residual-based Price Volatility
Copper
Copper 1
(96~02)
(96~98)
Raw Trading Volume: ln(ALLVOLUME)t
Mean
9.746786
9.233036
Median
9.846970
9.328745
Skewness
-0.744105
-0.711223
Kurtosis
4.055427
3.826696
Observations
1706
736
Raw Open Interest: ln(ALLOPIN)t
Mean
11.53493
11.09216
Median
11.51004
11.13854
Skewness
-0.201178
-0.169547
Kurtosis
2.127802
1.686283
Observations
1706
736
Return: Rt
Mean
-0.000289
-0.000755
Trading Volume, Raw Open Interest, Return, and the
Copper 2
(99~02)
Aluminum
(99~02)
Soybean
(99~02)
Wheat
(00~02)
10.13660
10.17214
-0.447733
3.866615
970
8.506592
8.484255
-0.424454
2.442881
960
12.14479
12.19407
-0.206824
2.639685
964
10.68839
10.74948
-0.411815
3.044584
694
11.87089
11.88377
-0.305950
2.159766
970
10.22369
10.09963
-0.190398
3.366237
960
13.17991
13.22439
-0.324223
1.883263
964
12.01947
12.06117
-0.658355
2.722679
694
5.67E-05
-1.13E-05
0.000254
0.000217
9
In addition, the return and price volatility for soybean futures are characterized by especially high kurtosis.
As to the measures of price volatility, the skewness and kurtosis of PVGKt and PVHLt for wheat futures have been
significantly improved (not reported here and can be provided on request) relative to the residual-based price volatility.
Thus, when we analyze the empirical results for the determinants of price volatility in the wheat futures market, the
results of the robustness tests for PVGKt and PVHLt maybe more relevant and more meaningful.
10
9
Median
0.000000
-0.000619
Skewness
-0.091497
-0.207091
Kurtosis
5.152027
4.844702
Observations
1705
735
Residual-based Price Volatility: PVt
Mean
0.005967
0.006267
Median
0.004131
0.004258
Skewness
1.772188
1.674400
Kurtosis
6.635651
5.813588
Observations
1690
720
0.000000
0.064257
5.341264
969
0.000000
-0.447846
5.734238
959
0.000000
0.326912
22.31398
963
0.000000
10.66680
204.2419
693
0.005745
0.004080
1.833050
7.247704
954
0.003794
0.002553
1.917170
7.648505
944
0.006604
0.003994
4.540317
37.01051
948
0.007418
0.003692
13.51471
244.1428
678
1. Past Price Volatility and Past Unexpected Return
By observing the significance of past price volatility in table 4, we find that the effects of past price
volatility on current price volatility are significant in all futures markets except for the copper
(sub-sample 2) futures market. Generally, the empirical results provide consistent support for the
argument that in the documenting of contemporaneous determinants of volatility past volatility shocks
must be accommodated. By comparing the empirical results between copper (sub-sample 2) and
copper (sub-sample 1), we find that past price volatility does not predict current price volatility in the
sub-sample 2 period (1999-2002); this indicates that the market is more mature and effective in the
sub-sample 2 period for copper futures 11 .
By observing the significance of past price volatility in table 4, we find that the effects of past
unexpected returns on current price volatility are significant only for copper sub-sample 1 futures
(1996-1998) 12 , but not for copper (sub-sample 2), aluminum, soybean, and wheat futures. The
estimated coefficients on lagged unexpected return shocks are significantly negative for the copper
sub-sample 1 futures market, which indicates negative return shocks (price decrease) have a larger
effect on subsequent price volatility. Since this effect does not exist for the copper sub-sample 2
futures, we claim that the effectiveness of copper futures improved during the period 1999-2002.
Table 4 The Determinants of Price Volatility (Dependent Variable: PVt)
Copper (1996~2002)
Copper 1 (1996~1998)
Determinants Coefficients Prob.
Coefficients Prob.
C
0.001110
0.2071
0.001675
0.2226
PVt-1
0.026557
0.2778
0.040525
0.2911
PVt-2
0.061442
0.0346**
0.150373
0.0002***
0.058275
0.0282**
0.080603
0.0961*
PVt-3
0.072388
0.0200**
0.073486
0.1314
PVt-4
0.074369
0.0106**
0.071407
0.0791*
PVt-5
UNEXR t-1
-0.009662
0.6045
-0.031156
0.3191
UNEXR t-2
-0.055344
0.0027***
-0.069021
0.0102**
Copper 2 (1999~2002)
Coefficients Prob.
0.002046
0.0625*
-0.020346
0.5498
-0.038331
0.3345
0.011103
0.7166
0.039637
0.2479
0.049941
0.2172
0.013795
0.5383
-0.036616
0.1351
11
We have a relatively broad definition for effectiveness/efficiency. It is mainly related to (1) China’s commodity
futures markets have played the role to appropriately respond to information; (2) China’s commodity futures markets
have similar market responses as Western mature futures markets; or (3) the market response of China’s commodity
futures markets is consistent with the related futures theories.
12
For the copper futures market, we analyze the empirical results for the two sub-samples separately.
10
-0.031384
0.0843*
-0.081553
0.0012***
-0.000125
0.9946
0.010786
0.7088
0.001217
0.9470
-0.016801
0.5251
-0.000596
0.1400
-0.001128
0.0782*
0.000175
0.6873
-4.21E-05
0.9526
-0.000233
0.5896
-0.000891
0.1992
0.000659
0.1378
0.001162
0.1240
0.000172
0.6889
9.75E-05
0.8804
0.000436
0.2975
0.000326
0.6414
0.000164
0.6830
-0.000407
0.5078
0.001105
0.0671*
0.000180
0.7509
0.000303
0.5488
-0.000476
0.3354
0.000904
0.0626*
0.000923
0.0507*
-0.000154
0.7003
0.000962
0.0405**
5.51E-05
0.0029***
5.46E-05
0.0602*
0.063135
0.123727
1685
715
Aluminum (1999~2002)
Soybean (1999~2002)
Determinants Coefficients Prob.
Coefficients Prob.
C
0.000951
0.1975
0.003868
0.0022***
PVt-1
0.057208
0.1151
0.087064
0.0456**
PVt-2
0.095426
0.0040***
0.002979
0.9094
0.057867
0.0650*
-0.003117
0.9102
PVt-3
0.067863
0.0860*
0.049994
0.0548*
PVt-4
0.057697
0.1148
-0.014231
0.6547
PVt-5
UNEXR t-1
-0.007964
0.7752
0.000516
0.9887
UNEXR t-2
0.025463
0.2562
-0.002918
0.8887
0.035760
0.1251
-0.028165
0.2490
UNEXR t-3
-0.010000
0.7115
-0.030469
0.2910
UNEXR t-4
0.022147
0.4001
0.032335
0.1647
UNEXR t-5
S1
-0.000741
0.0785*
-0.001708
0.0841*
S2
-0.001005
0.0100**
-0.001495
0.0966*
-0.000236
0.5395
-0.001476
0.1594
S3
DAY1
0.000730
0.0858*
0.001517
0.1606
DAY2
-0.000161
0.6707
5.66E-05
0.9497
-0.000155
0.6559
-0.001249
0.1334
DAY3
-0.000212
0.5536
-0.001035
0.2195
DAY4
YEAR99
0.000638
0.1258
-0.000145
0.8581
YEAR00
0.000112
0.7309
-0.000181
0.8287
0.000455
0.1982
0.000845
0.3029
YEAR01
TTM
4.01E-05
0.0133**
5.42E-05
0.0243**
R2
0.090677
0.047906
Observations 939
943
***, **, and * means significant at the one percent, five percent, and ten
The Newey-West T-test is employed to test for statistical significance.
UNEXR t-3
UNEXR t-4
UNEXR t-5
S1
S2
S3
DAY1
DAY2
DAY3
DAY4
YEAR96
YEAR97
YEAR98
YEAR99
YEAR00
YEAR01
TTM
R2
Observations
0.016954
-0.005482
0.024575
-0.000612
-0.000189
-1.45E-05
0.000375
0.000167
0.000488
0.000459
0.4768
0.8127
0.3570
0.2707
0.7384
0.9790
0.4736
0.7693
0.3603
0.3830
0.001408
0.0128**
-0.000119
0.7989
0.001526
0.0054***
6.14E-05
0.0106**
0.045924
949
Wheat (2000~2002)
Coefficients Prob.
-0.005749
0.0657*
0.254438
0.1242
0.017667
0.7296
-0.053572
0.0093***
0.007117
0.7354
0.006596
0.8020
-0.176971
0.2980
-0.042915
0.3334
0.021290
0.1944
-0.025255
0.1377
-0.029825
0.3959
-0.000895
0.5429
-0.000680
0.6150
0.000974
0.6482
0.004120
0.0103**
0.001059
0.3461
0.003864
0.1636
0.000313
0.7600
0.004426
0.000892
0.000140
0.089280
673
percent level,
0.0317**
0.5559
0.0045***
respectively.
2. Seasonality, Calendar Anomalies, Year Effect, and Time-to-Maturity Effect
Based on table 4 and at the five percent significant level, a significant seasonality effect can be
observed in the aluminum futures market. In the aluminum futures market, we find that the price
11
volatility in summer is significantly lower than in other seasons. In addition, we can not find
significant seasonality effects in copper, copper sub-sample 1, copper sub-sample 2, soybean, and
wheat futures markets.
At five percent level, a significantly positive Monday effect can be observed in the wheat futures
market, which implies that the effect of the trading gap leads to higher price volatility on Monday,
since Monday follows a two-day exchange-and-business holiday, and thus more information needs to
be absorbed on Monday. However, this significant Monday effect can not be observed in copper,
aluminum, and soybean futures markets.
At five percent level, the year effects are significant for the copper (sub-sample 2) and wheat
futures markets, but not for the copper (sub-sample 1), aluminum and soybean futures. To some extent,
the year effect reflects the impact of macroeconomic conditions on the supply and demand situation of
commodities, which leads to an inconsistent trend of price volatility in the different years.
The positively significant time-to-maturity effect can be found in all futures markets (marginally
significant for the copper sub-sample 1 futures market). That is, when a futures contract approaches
maturity, the related price volatility will decline accordingly. This empirical evidence provides
support for the argument of Rutledge (1976).
3. Trading Volume and Open Interest
The above analysis for the determinants of futures price volatility is based on the control variables (in
table 4); next, we will analyze tables 5 and 6, which focus on trading volume and open interest and
their components.
Consistent with previous empirical evidence, a significantly positive relationship between trading
volume and price volatility can be found in all futures markets 13 . When trading volume increases, it
increases the probability that prices will move into higher or lower regions, and that their volatility
will be greater than before. On the other hand, trading volume can be considered as a proxy for
information. A greater amount of information, embedded in volume, will then yield greater volatility.
Consistent with previous empirical evidence (Bessembinder and Seguin, 1993; Ragunathan and
Peker, 1997), a significantly negative relationship between open interest and price volatility can be
found in copper (sub-sample 2), soybean and wheat futures markets 14 . Open interest is a good proxy
for market depth because open interest reflects the current willingness of futures traders to risk their
capital in the futures position, which indicates the level of market depth (Bessembinder and Seguin,
1993). A high level of open interest could help to create market conditions that would reduce pressure
from prices when trading provides new information. The relationship between price volatility and
13
However, the positive relationship is not significant for wheat futures when we use residual-based price volatility,
but it is significant for wheat futures when we use PVGKt and PVHLt. Please see footnote 10 and table 6.
14
It is not significant for wheat futures when we employ the residual-based price volatility, but it is significant for
wheat futures when we employ PVGKt and PVHLt. Please see footnote 10 and table 6.
12
open interest would then be negative. That is, deeper markets (a high level of open interest) have
relatively less price volatility. Low depth may make it more difficult for large trades to occur
smoothly and may lessen the speed at which information is transmitted to the market.
However, we cannot find a significant negative effect of open interest for copper (sub-sample 1)
and aluminum futures. This fact means that the effectiveness of copper futures market improved
during the second sub-sample period (1999-2002) and this is due to a regulatory adjustment to the
futures market. The effectiveness of copper futures during 1996-1998 and of aluminum is not
satisfactory.
4. Expected and Unexpected Trading Volume
A significantly positive effect on price volatility for unexpected trading volume can be observed in all
futures markets 15 , while the significant positive effect on price volatility for expected trading volume
only exists for copper (sub-sample 1), soybean and wheat futures16 . As with Bessembinder and Seguin
(1993), and Ragunathan and Peker (1997), the estimated coefficients associated with volume shocks
are uniformly higher than those associated with expected volume in copper (sub-sample 2), aluminum,
and soybean futures. This empirical evidence means that to some extent the effects of the different
components of trading volume can be distinguished in China’s commodity futures (except for wheat
futures) markets as they are in the mature futures markets of the West.
Comparing the ratios of the unexpected volume coefficient to the expected volume coefficient
between the two sub-samples of copper futures, we find that the ratio uniformly increases from 1.36
(0.83, 0.80) to 1.86 (2.00, 4.15) 17 . This fact provides support for the argument that the market
effectiveness of copper futures improved during the second sub-sample period (1999-2002) due to the
regulatory adjustment, since the importance of unexpected volume significantly increased in the
copper (sub-sample 2) futures market.
5. Expected and Unexpected Open Interest
A significantly negative effect on current price volatility for expected open interest can be observed in
the copper (sub-sample 2)
18
, soybean 19 , and wheat 20 futures markets, while we can not find a
15
It is not significant for wheat futures when we employ the residual-based price volatility, but it is significant for
wheat futures when we employ PVGKt and PVHLt. Please see footnote 10 and table 6.
16
It is not significant for soybean and wheat futures when we use residual-based price volatility, but it is significant for
soybean and wheat futures when we use PVGKt and PVHLt. Please see footnote 10 and table 6.
17
The numbers in brackets are computed based on the results of robustness tests.
18
It is marginally significant at the ten percent level for copper (sub-sample 2) futures market when we use the
residual-based price volatility. It is not significant for copper (sub-sample 2) futures market when we use PVHLt.
19
It is marginally significant at the ten percent level for soybean futures market when we use PVGKt. It is not
significant for soybean futures market when we use PVHLt.
20
It is not significant for wheat futures when we use the residual-based price volatility, but it is significant for wheat
futures when we use the PVGKt and PVHLt. Please see footnote 10 and table 6.
13
consistent 21 effect on price volatility for expected open interest in copper (sub-sample 1) and
aluminum futures markets. Similarly, this fact means that the effectiveness of the copper futures
market improved during the second sub-sample period (1999-2002), and the effectiveness of
aluminum is not satisfactory. The consistent 22 significant effect on price volatility for unexpected
open interest can not be observed for all samples.
Table 5 The Determinants of Price Volatility based on Trading Volume and Open Interest (Dependent
Variable: PVt)
Copper (1996~2002)
Copper 1 (1996~1998)
Copper 2 (1999~2002)
Determinants
Coefficient Prob.
Coefficient Prob.
Coefficient Prob.
s
s
s
DEVOLUME
0.004824
0.0000*** 0.004795
0.0000*** 0.004571
0.0000***
DEOPIN
-0.003907
0.0004*** -0.001330
0.2895
-0.006573
0.0006***
R2
0.264478
0.326087
0.225589
Observations
1685
715
949
EXVOL
0.003955
0.0000*** 0.003644
0.0001*** 0.002514
0.0842*
UNEXVOL
0.004939
0.0000*** 0.004962
0.0000*** 0.004668
0.0000***
EXOPIN
-0.002721
0.0382**
-0.000477
0.7463
-0.003857
0.0654*
UNEXOPIN
-0.021476
0.0017*** -0.002950
0.7448
-0.047960
0.0000***
0.271472
0.327862
0.257987
R2
Observations
1685
715
949
EXVOL
0.005797
0.0000*** 0.004766
0.0000*** 0.005336
0.0008***
UNEXVOL
-0.000416
0.6098
0.000882
0.0532*
-0.001581
0.2563
VOLDUMMY*
UNEXVOL
0.010793
0.0000*** 0.008294
0.0000*** 0.012684
0.0000***
EXOPIN
-0.002790
0.0219**
-0.000567
0.6814
-0.003553
0.0363**
UNEXOPIN
-0.031907
0.0007*** -0.009372
0.3514
-0.063846
0.0002***
OPINDUMMY *
UNEXOPIN
0.036242
0.0419**
0.020574
0.3708
0.056686
0.0316**
0.364094
0.379010
0.390625
R2
Observations
1685
715
949
Aluminum (1999~2002) Soybean (1999~2002)
Wheat (2000~2002)
Determinants
Coefficient Prob.
Coefficient Prob.
Coefficient Prob.
s
s
s
DEVOLUME
0.000992
0.0000*** 0.002175
0.0035*** 0.000413
0.6780
DEOPIN
-0.000376
0.6026
-0.004979
0.0020*** 0.002075
0.4168
R2
0.121330
0.072733
0.091157
Observations
939
943
673
EXVOL
-0.000293
0.3323
0.001773
0.1573
0.001563
0.3550
UNEXVOL
0.001997
0.0000*** 0.002282
0.0355**
3.51E-05
0.9851
EXOPIN
0.001432
0.0773*
-0.004347
0.0127**
0.001905
0.3748
UNEXOPIN
-0.000355
0.9421
-0.019065
0.1061
-0.042817
0.0081***
0.158202
0.075038
0.096722
R2
Observations
939
943
673
EXVOL
-0.000285
0.3339
0.002454
0.0949*
0.002137
0.3055
21
Here “consistent” means that we find the variable has a significant (at the ten percent level) effect on at least two
measures of price volatility.
22
Here “consistent” means that we find the variable has a significant (at the ten percent level) effect on at least two
measures of price volatility.
14
UNEXVOL
0.000677
0.0356**
-0.002585
0.3321
-0.006065
0.2185
VOLDUMMY*
UNEXVOL
0.001896
0.0420**
0.009693
0.0065*** 0.011157
0.1034
EXOPIN
0.001677
0.0342**
-0.005202
0.0070*** 0.001486
0.4045
UNEXOPIN
-0.017657
0.0037*** -0.021433
0.3408
-0.053176
0.1085
OPINDUMMY *
UNEXOPIN
0.056683
0.0007*** 0.019711
0.6197
0.024994
0.5946
0.205889
0.102672
0.103722
R2
Observations
939
943
673
The coefficients and significance of past price volatility (PVt-1 ~PVt-5), past unexpected return
(UNEXR t-1~UNEXR t-5), seasonality (S1 ~S3), day effect (DAY1 ~DAY4), year effect (YEAR96 ~YEAR01),
and time-to-maturity are not reported in this table, since they are control variables. These empirical
results can be provided on request. ***, **, and * means significant at the one percent, five percent,
and ten percent level, respectively. The Newey-West T-test is employed to test for statistical
significance.
15
Table 6 Robustness Test (Dependent Variable: PVGKt and PVHLt)
Copper (1996~2002)
Copper 1 (1996~1998)
Determinants
PVGKt
PVHLt
PVGKt
PVHLt
Coefficient Coefficient Coefficient Coefficient
s
s
s
s
DEVOLUME
0.001640* 1.89E-05* 0.001798* 2.34E-05*
**
**
**
**
DEOPIN
-0.000783
-7.87E-06
-0.000421
-9.48E-06
**
EXVOL
0.001583* 1.87E-05* 0.002098* 2.79E-05*
**
**
**
**
UNEXVOL
0.001650* 1.89E-05* 0.001739* 2.24E-05*
**
**
**
**
EXOPIN
-0.000728
-7.05E-06
-0.000621
-1.21E-05
*
UNEXOPIN
-0.001307
-3.10E-05
-0.001591
-7.02E-05
EXVOL
0.001884* 2.36E-05* 0.002350* 3.28E-05*
**
**
**
**
UNEXVOL
0.000610*
0.000771*
1.86E-06
6.06E-06*
**
**
VOLDUMMY*
0.002192* 3.60E-05* 0.002083* 3.57E-05*
UNEXVOL
**
**
**
**
EXOPIN
-0.000707
-6.87E-06
-0.000690
-1.37E-05
*
UNEXOPIN
0.000208
-4.09E-06
0.001063
-1.11E-05
OPINDUMMY *
UNEXOPIN
-0.001063
-2.24E-05
-0.005049
-0.000120
Aluminum (1999~2002) Soybean (1999~2002)
PVHLt
PVGKt
PVHLt
Determinants
PVGKt
Coefficient Coefficient Coefficient Coefficient
s
s
s
s
DEVOLUME
0.000549* 4.67E-06* 0.003187* 5.87E-05*
**
*
**
**
DEOPIN
-0.00288*
-6.0E-05**
0.000136
5.64E-06
**
*
EXVOL
0.001699* 3.70E-05*
**
*
4.46E-05
-7.21E-07
UNEXVOL
0.000931* 8.83E-06* 0.003621* 6.49E-05*
**
*
**
**
EXOPIN
-0.001672
0.000713
1.22E-05
-3.66E-05
*
UNEXOPIN
-0.000387
0.003159
3.59E-05
-0.008536
*
EXVOL
0.001978* 4.11E-05*
**
**
5.06E-05
-4.89E-07
UNEXVOL
0.002010* 2.41E-05*
0.000364* 1.80E-06
**
*
VOLDUMMY*
0.003326* 7.96E-05*
UNEXVOL
0.000815
1.18E-05
**
**
EXOPIN
-0.002034
0.000812
1.32E-05
-4.23E-05*
**
UNEXOPIN
-0.004364
-3.06E-05
-0.005084
-0.000449
*
16
Copper 2 (1999~2002)
PVGKt
PVHLt
Coefficient Coefficient
s
s
0.001436* 1.37E-05*
**
**
-0.00191*
-1.62E-05*
**
0.000755*
*
3.59E-06
0.001512* 1.49E-05*
**
**
-0.00172*
-1.42E-05
**
-0.001763
-9.48E-06
0.001099*
**
8.62E-06
0.000387*
-2.12E-06
*
0.002366* 3.57E-05*
**
**
-0.001606
-1.23E-05
**
-0.001385
-1.16E-05
0.002910
6.28E-05
Wheat (2000~2002)
PVGKt
PVHLt
Coefficient Coefficient
s
s
0.001757* 3.48E-05*
**
**
-0.001588
-4.62E-05*
**
*
0.001486* 3.60E-05*
*
*
0.001909* 3.53E-05*
**
**
-0.001506
-4.58E-05*
**
*
-0.001691
0.001442*
*
-0.000239
3.49E-05*
*
-0.000106
0.003165*
*
-0.001297
*
-0.028331
**
-6.15E-06
6.47E-05*
-4.15E-05*
-0.000794
**
OPINDUMMY *
0.024561* 0.000218*
0.048223*
UNEXOPIN
-0.003090
0.000269
0.001002*
**
**
*
The coefficients and significance of past price volatility (PVt-1 ~PVt-5), past unexpected return
(UNEXR t-1~UNEXR t-5), seasonality (S1 ~S3), day effect (DAY1 ~DAY4), year effect (YEAR96 ~YEAR01),
and time-to-maturity are not reported in this table, since they are control variables. These empirical
results can be provided on request. ***, **, and * means significant at the one percent, five percent,
and ten percent level, respectively. The Newey-West T-test is employed to test for statistical
significance.
6. Asymmetrical Effects of Unexpected Trading Volume and Unexpected Open Interest
The asymmetrical effect of unexpected trading volume on current price volatility is represented in the
estimated coefficient of VOLDUMMYt* UNEXVOLt in model (12). We find consistent, very
significant, and positive asymmetrical effect of unexpected trading volume on price volatility in
copper (sub-sample 1), copper (sub-sample 2), and soybean futures markets. That is, the relationship
between unexpected volume shocks and contemporaneous volatility is asymmetric, and positive
volume shocks (actual volume > expected volume) are associated with higher levels of volatility than
negative shocks (actual volume <expected volume). This empirical evidence is consistent with
Bessembinder and Seguin (1993), and Ragunathan and Peker (1997).
The asymmetrical effect of unexpected open interest on current price volatility is represented in the
estimated coefficient of OPINDUMMYt* UNEXOPINt in model (12). We can find consistent, very
significant, and positive asymmetrical effect of unexpected open interest on price volatility in
aluminum futures market, but this effect can not be observed in other futures markets.
7. The Explanatory Ability of Trading Volume and Open Interest and Their Components to
Price Volatility
Table 7 summarizes the R2 of model (9)~(12) 23 . In general, we find that for the two most active
products, that is, copper and soybean futures, the introduction of trading volume and open interest and
their components can make the explanatory power of the model improve significantly. This fact
implies that market depth is quite important as a determinant of price volatility. For the two relatively
inactive products, that is, aluminum and wheat futures, the introduction of trading volume and open
interest and their components are generally not significant.
Table 7 The Explanatory Ability of Trading Volume and Open Interest and Their
Price Volatility
Model (9)
Model (10)
Model (11)
Copper
Residual-based
price
0.063135
0.264478
0.271472
volatility
PVGKt
0.178816
0.325934
0.326003
23
We reach the same conclusions for adjusted R2.
17
Components to
Model (12)
0.364094
0.347238
PVHLt
0.086813
0.170739
0.171012
0.195197
Copper 1
Residual-based
price
0.123727
0.326087
0.327862
0.379010
volatility
PVGKt
0.206528
0.395223
0.396517
0.412919
0.115086
0.219194
0.221981
0.239185
PVHLt
Copper 2
Residual-based
price
0.045924
0.225589
0.257987
0.390625
volatility
PVGKt
0.102865
0.226020
0.229233
0.258408
0.040229
0.097845
0.101823
0.136447
PVHLt
Aluminum
Residual-based
price
0.090677
0.121330
0.158202
0.205889
volatility
PVGKt
0.275251
0.297458
0.312253
0.331563
0.209652
0.221691
0.231053
0.242196
PVHLt
Soybean
Residual-based
price
0.047906
0.072733
0.075038
0.102672
volatility
PVGKt
0.106534
0.254640
0.265633
0.276893
0.113821
0.206206
0.217132
0.229865
PVHLt
Wheat
Residual-based
price
0.089280
0.091157
0.096722
0.103722
volatility
PVGKt
0.130783
0.171213
0.171715
0.187749
0.141277
0.165691
0.167068
0.175728
PVHLt
Model (9) is based on control variables; model (10) is based on control variables + trading volume +
open interest; Model (11) is based on control variables + expected trading volume + unexpected
trading volume + expected open interest + unexpected open interest; model (11) is based on control
variables + expected trading volume + unexpected trading volume + asymmetrical effect of
unexpected trading volume + expected open interest + unexpected open interest + asymmetrical effect
of unexpected open interest. The table shows the R-squares of the specific model.
8. An International Comparison of Empirical Results
Table 8 lists the empirical results of several typical studies about the effect of trading volume and
open interest and their components on price volatility. We then compare these Western results to the
Chinese results of this study. From table 8 we find that as to the effect of trading volume and open
interest and their components on price volatility, the response of China’s commodity futures markets
is basically consistent with that of Western mature futures markets 24 , especially for the two active
products, copper and soybean. The reason can be attributed to the internationalization of these two
markets, and the high correlation for copper futures price changes between SHFE and LME, and for
soybean futures price changes between DCE and CBOT. In addition, consistent with Watanabe (2001),
we find that an effective change in regulation mechanism improves the price behaviors (this is shown
by comparing the empirical results between copper (sub-sample 1) and copper (sub-sample 2) futures
markets).
24
Please see the words with bold and italic version in table 8.
18
V. CONCLUSIONS AND POLICY IMPLICATIONS
This paper systematically investigates the main determinants of price volatility in China’s commodity
futures markets. Based on the empirical results, we find that:
(1) There is a significant effect of past price volatility on current price volatility except for copper
(sub-sample 2) futures; a significant effect of past unexpected return on current price
volatility exists for copper (sub-sample 1) futures; a significant seasonality effect exists in
aluminum futures market; a positive Monday effect exists in wheat futures; a year effect can
be observed in copper (sub-sample 2) and wheat futures; a significantly positive time to
maturity effect can be observed in all futures markets, which implies that when a futures
contract approaches maturity, the related price volatility will decline accordingly.
(2) There is a significant positive effect on price volatility for trading volume and unexpected
trading volume in all four commodity futures markets. During 1999-2002, the effect of
unexpected trading volume was higher than that of expected volume in the copper, aluminum,
and soybean futures markets.
(3) The significant negative effect of open interest and expected open interest on price volatility
is observed in the copper, soybean, and wheat futures markets during 1999-2002, and we do
not find any consistent and significant effect of unexpected open interest on price volatility in
all futures markets.
(4) The asymmetry effect of unexpected volume on price volatility mainly exists in copper and
soybean futures markets, and the asymmetry effect of unexpected open interest on price
volatility mainly exists in aluminum futures market.
Based on the empirical results and the analysis of explanatory power and international comparison,
we conclude that the copper futures market was more mature and effective during 1999-2002 than
during 1996-1998, which implies that the measures taken by CSRC from the fourth quarter of 1998
played a very important and positive role in improving market maturity and effectiveness. During
1999-2002 two active trading futures, copper and soybean, were more mature and effective than the
two relatively inactive trading futures, aluminum and wheat. We conclude that the basic status of
China’s commodity futures markets was satisfactory during the recent years, since active transactions
did not lead to over-speculation and market manipulation. On the other hand, the inactive traded
futures products may face several problems. Thus, the regulatory authority needs to seriously consider
ways to increase the transactions of China’s commodity futures markets. Initiatives could include the
introduction of new futures products and the elimination of restrictions on investment.
Table 8 An International Comparison of Empirical Results
Bessembinder and Ragunathan and
19
Watanabe
This Study
Countries and
Futures Products
Sample Period
(year, month)
Seguin （1993）
U.S.
Mark, Yen, Gold,
Silver, Cotton,
Wheat, Treasury
Bond, Treasury
Bill
198205-199003
Peker （1997）
Australia
Treasury Bill,
Treasury Bond,
Stock Index
（2001）
Japan
Nikkei 225 Stock
Index
199201-199412
199008-199712
Two
Sub-samples:
China
Copper,
Aluminum,
Soybean, Wheat
Copper:
199008-199402
199601-200212
Two
Sub-samples:
199402-199712
199601-199812
199901-200212
Aluminum:
199901-200212
Soybean:
199901-200212
Wheat:
Trading Volume
Expected Trading
Volume
+ For All Samples
except Treasury
Bond and Cotton
+ For Treasury
Bill
No Significant
Effect for Two
Sub-samples
Unexpected
Trading Volume
+ For All Samples
+ For Second
Sub-sample
Comparison
between the
Coefficients of
Expected and
Unexpected
Trading Volume
Asymmetrical
Effect of
Unexpected
Trading Volume
Open Interest
Unexpected Coef.
> Expected Coef.
for All Samples
+ For Stock
Index, Treasury
Bill, and Treasury
Bond
Unexpected Coef.
> Expected Coef.
for All Samples
+ For All Samples
except Treasury
Bill and Wheat
No Significant
Effect
+ For Second
Sub-sample
Expected Open
Interest
− For All Samples
No Significant
Effect
− For Second
Sub-sample
20
Unexpected Coef.
> Expected Coef.
for the Second
Sub-sample
200001-200212
+ For All Samples
+ For Copper
(Sub-sample 1),
Soybean and
Wheat
+ For All
Samples
Unexpected Coef.
> Expected Coef.
for Copper
(Sub-sample 2),
Aluminum, and
Soybean
+ For Copper
(Two
Sub-samples),
and Soybean
− For Copper
(Sub-sample 2),
Soybean and
Wheat
− For Copper
(Sub-sample 2),
Soybean and
Wheat
Unexpected Open
Interest
Asymmetrical
Effect of
Unexpected Open
Interest
− For All Samples
except Treasury
Bill, Gold, and
Silver
+ For Yen,
Cotton, Gold, and
Silver
− For Stock
Index, Treasury
Bonds
No Significant
Effect
No Significant
Effect
No Significant
Effect
No Significant
Effect
+ For Aluminum
21
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