Intraday Patterns in Chinese Futures Markets

Yu Wu

B. Wade Brorsen

Pin Ren

Contact author: B. Wade Brorsen Department of Agricultural Economics Oklahoma State University Stillwater, OK 74078-6026 Phone: (405)744-6836 Fax: (405)744-8210 Email: wade.brorsen@okstate.edu

Yu Wu is an Associate Professor of Finance at the Research Institute of Economics and Management, Southwestern University of Finance and Economics, China.

B. Wade Brorsen is a Regents Professor and A.J. and Susan Jacques Chair in the Department of Agricultural Economics at Oklahoma State University.

Pin Ren is an Associate Professor of Finance at the Finance School, Southwestern University of Finance and Economics, China.

The research was partially supported by the Oklahoma Agricultural Experiment Station. This research is supported by project 211 (phase III) of the Southwestern University of Finance and Economics.

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Intraday Patterns in Chinese Futures Markets

The intraday patterns of prices and volume are determined for Chinese futures markets. All

Chinese futures markets except one relatively inactive contract are considered. Prices and

volume are observed every half second. Effective spreads are similar to those in other countries,

which indicate that Chinese markets are functioning well in terms of providing a low-cost

environment. Average effective spreads are smaller than average quoted spreads. Volatility is

highest when trading begins and after each of the two breaks in trading during the day. Volume

has a pattern similar to volatility during the trading day. Volume increases as contracts mature

and then drops in the final months of trading.

Key words: bid-ask spread, China, liquidity cost, microstructure

JEL Classification: G13, Q13, Q41

China has some of the most actively traded futures contracts in the world. Based on the

report of the Futures Industry Association (2010), in 2009, 161 million steel rebar contracts were

traded on the Shanghai Futures Exchange, 155 million soybean meal contracts were traded on

the Dalian Commodity Exchange, and 146 million white sugar contracts were traded on the

Zhengzhou Commodity Exchange. These contracts traded at Chinese futures markets were the

most actively traded metal contract and the two most actively traded agricultural futures

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contracts in the world. These large trading volumes at Chinese futures markets have aroused the

interest of traders around the world. More and more scholars have begun to study Chinese

futures markets. This past research has mostly used daily data and studied issues such as the

cointegration of Chinese and United States markets (Fung, et al. 2003, 2010; Ge et al. 2010) as

well as cointegration of cash and futures markets in China (Wang and Ke 2005), random walk

tests (Xin, Chen, and Firth 2006), and measuring daily volatility (Chan, Fung, and Leung 2004).

However, since high frequency data from Chinese futures markets are not widely

available, knowledge about the microstructure of Chinese futures markets is limited. Fabozzi et

al. (2010) argue that market microstructure is likely exchange dependent so microstructure of

China’s markets might differ from markets in other countries. The only previous study of which

we are aware is Liu, Wang, and Hong (2010), a Chinese language publication that presents some

limited intraday patterns for Chinese futures markets from April 27th, 2007 to September 28th

2007. They use only six commodities’ one-minute price data and they do not have quote

information so they cannot calculate effective spreads. Effective spreads are a key piece of

information since they measure liquidity cost.

In our study, the intraday patterns of Chinese futures markets are determined using 500

ms high frequency Chinese futures trade and quote data from December 29, 2008 to December

25, 2009. Our study includes all twenty-three commodity futures contracts traded on Chinese

futures markets in 2009. Here, we analyze the intraday patterns of effective spread, volatility,

and trading volume for futures contracts at Chinese futures markets. Twenty three actively traded

contracts are included. We also provide an explanatory regression model for effective spreads as

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well as a simultaneous equations model for the determination of volume and volatility.

A number of studies examine intraday patterns in volatilities, trading volumes, and bid-

ask spreads for different futures contracts. Both volume and volatility have usually been found to

have a U-shaped pattern with volatility and volume being higher both after the open and before

the close (Ekman 1992; ap Gwilym, McMillan and Speight 1999; Abhyankar, Copeland and

Wong 1999; Huang 2002; Copeland and Jones 2002). The pattern for effective spreads is not as

consistent. Spreads typically differ at the open and close, but can be both higher (Ferguson and

Mann 2001; Huang 2004) and lower (Abhyankar, Copeland and Wong 1999) and sometimes

follow a reverse J-pattern, where spreads are higheest at the open, decrease during the day and

then increase slightly at the close (ap Gwilym and Thomas 2002; Bryant and Haigh 2004).

Bryant and Haigh argue that commodity futures markets may have a different pattern than more

frequently studied financial futures markets. All of the Chinese markets that we analyze are

commodity futures markets.

Several equity intraday pattern theories (Amihud and Mendelson, 1987; Admati and

Pfleiderer, 1988; Foster and Viswanathan, 1990; Stoll and Whaley, 1990; Amihud and

Mendelson, 1991; Brock and Kleidon, 1992; Gerety and Mulherin, 1992) have been developed.

One of the more prominent theories is Brock and Kleidon’s market closure theory that was

supported by Daigler (1997) using data from derivatives markets.

Market makers (scalpers) and other day-traders usually close their positions before the

market closes for the day. Arbitrageurs need to match their futures trades with trades in the

underlying assets; therefore, both markets typically must be open when arbitrageurs trade. Cash

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market dealers often hedge their inventory with futures shortly before the futures markets close.

The market closure theory suggests that these activities can explain why the opening and closing

time for both the underlying cash and the futures markets will have large volume and high

volatility. Because the Chinese markets have multiple breaks during the day, Chinese markets

can provide indirect tests of the theory.

The analysis provides information that has previously been impossible to obtain. The

statistics that we report include effective spreads, volatilities, and trading volumes of futures

contracts in Chinese futures markets. Moreover, we report the relation between effective spread

and trade size, the relation between effective spread and time to maturity, and the relation

between daily trading volume and time to maturity. The intraday effective spread, volatility, and

trade number of Chinese futures markets exhibit ‘reverse-J’ shaped patterns with effective

spreads being largest at the open, decrease sharply, and then decrease gradually throughout the

trading period. Intraday trading volume exhibits a similar pattern with volume largest at the open

and then relatively flat throughout the day except for small increases after trading breaks and at

the close. Effective spreads are high when the delivery date is distant, decrease as time passes,

and increase close to maturity, which is consistent with the pattern found by Bryant and Haigh

(2004). The pattern for daily volume is roughly the opposite of that for effective spreads.

Chinese markets are always a potential target for increased regulation so knowing how well they

are functioning can be important in future policy decisions such as expanding futures contracts

on financial instruments. There are world-wide concerns about the effects of high-frequency

trading on trading costs. India has placed temporary bans in the last few years on futures trading

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in commodities such as wheat, rice, sugar, soybean oil, potatoes, rubber, and chickpeas. Our

results indicate that the Chinese futures markets provide effective spreads that are competitive

with other markets around the world. This information should provide confidence to market

users and to government regulators1 when considering the expansion of futures markets in China.

I. CHINESE FUTURES MARKETS

In 2009, when our data were collected, China had four futures exchanges: the

Shanghai Futures Exchange (SHFE), the Dalian Commodity Exchange (DCE), the Zhengzhou

Commodity Exchange (ZCE), and the just founded China Financial Futures Exchange (CFFEX).

SHFE and CFFEX are located at Shanghai City, the largest city and financial center in China.

DCE is located at Dalian City in Northeast China. ZCE is located at Zhengzhou in North China.

SHFE, DCE, and ZCE are owned by the Chinese government. However, CFFEX is owned by the

Shanghai Futures Exchange, the Zhengzhou Commodity Exchange, the Dalian Commodity

Exchange, the Shanghai Stock Exchange and the Shenzhen Stock Exchange. Each exchange has

shares in CFFEX.

Each futures market has its own member firms and has two categories of membership.

One is a brokerage membership that includes all futures commission merchants that are

registered as exchange members. The other is a proprietary membership that includes enterprises

that trade only for their own accounts. All trading orders must be submitted through, or by, an

exchange member. At present, Chinese regulations stipulate that only Chinese citizens and                                                             1 Regulators are also concerned with other issues such as price bubbles and price manipulation. Effective spreads are only one component that regulators should consider. 

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companies or organizations that are organized and registered in China are allowed to trade. All

trading is executed in the Chinese currency Yuan (RMB).

Twenty-three commodity futures contracts were traded at these four exchanges in 2009.

A stock index futures contracts began trading formally from April 16th 2010, and before that

simulated index futures contracts were traded. The commodity contract specifications are shown

in Table 1. Each exchange imposes speculative position limits but no hedge position limits.

Position limits are imposed on contracts of ordinary months and of the previous month before the

delivery month according to both the member code and the clients’ code. The position limit of

brokerage members is determined by the Exchange and considers factors such as their registered

capital, credit, capability to manage risk, trading activity during previous years, and number of

clients. Absolute limits on positions are imposed on contracts during the delivery month.

Chinese futures markets all execute orders electronically rather than by open outcry. The

computerized automatic trading system arranges orders by price priority first and time priority

second. When the highest bid price is equal to, or higher than, the lowest ask price, a transaction

is made. The exchanges report current bid prices, current ask prices, and a strike price. With

every transaction, the strike price is reset to be the transaction price. The strike also cannot be

below the lowest bid or above the highest ask. The current strike price is decided by the

following rule:

sppricestrikecurrentthecpspbpwhen ,  

cppricestrikecurrentthespcpbpwhen ,

bppricestrikecurrentthespbpcpwhen ,

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where bp is the current bid price, sp is the current ask price, and cp is the previous strike price.

Limit price orders, kill-if-not-fill orders, and other orders specified by the exchange are

the three different kinds of trading orders. Most orders are either limit price or kill-if-not-fill

orders. Limit orders are valid within the trading day. Clients may ask for alteration or

cancellation before a trade is conducted. Transactions occur by submitting a bid at the current

lowest ask or an ask at the current highest bid. ZCE allowed market orders beginning April 20,

2009, but they are limited to 200 contracts, whereas limit orders may be for 1,000 contracts.

All futures contracts are traded from 9:00 am to 11:30 am in the morning and from 1:30

pm to 3:00 pm in the afternoon. At noon is a two-hour break. In each week, people can trade

from Monday to Friday, except for Chinese legal holidays. A fifteen minute break occurs from

10:15 to 10:30. An opening auction begins five minutes before the opening of the market on each

business day. During the first four minutes, bids and asks are submitted, and during the last

minute, matching occurs. The auction price is selected to maximize trading volume. The opening

price is released at the opening of the market.

II. DATA DESCRIPTION

This study provides a statistical description of the microstructure of Chinese futures

markets. 500 ms high frequency data from December 29, 2008 to December 25, 2009 are used to

analyze the intraday patterns of effective spreads and trading volume. The exchanges do not

make such high frequency data public. The data were collected by paying a fee to the exchanges

to allow us to place a server on the floor of the exchange that recorded information every half

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second. This period included 238 trading days. This data base includes all contracts of 23

commodities in three futures exchanges, and provides information about ticker, time, strike price,

bid price, bid volume, ask price, ask volume, open interest, and cumulative trading volume in

one trading day for three futures exchanges in mainland China. The sampling frequency for each

variable is twice per second. Here, transaction price is the strike price. Trades are identified as a

change in volume during the half-second interval. Effective spread is calculated as:

(1) )______(2_ askandbidpreviousofmidpointpricentransactioSpreadEffective .

Percentage effective spreads are obtained by dividing the effective spread by the midpoint of the

previous bid and ask. There is a rich literature in estimating effective spreads using transaction

prices only (Roll 1984; Thompson and Waller 1987; Hasbrouck 2004; Frank and Garcia 2011).

These measures are not needed here since they were developed for the case of incomplete data

when only trade prices are observed. We have bid-ask spreads at every half second and so we

can directly calculate bid-ask spreads. Note that for some high-volume markets, multiple

transactions could occur within a half-second interval. Also, note that if a large order is filled at

multiple prices, our approach will record the transaction as occurring at the last price. These

limitations are not possible to overcome with the available data. Because of the voluminous data,

most results are presented as volume-weighted averages across all twenty-three contracts.

III. INTRADAY PATTERNS IN CHINESE FUTURES MARKETS

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Table 2 shows descriptive statistics of quoted spreads, effective spreads, number of

trades, and trade sizes. Gold has the smallest average percentage effective spread. No. 2 soybean

and hard white wheat have the largest average percentage effective spreads. This result is also

consistent with small trading volume of the No. 2 soybean and hard white wheat contract. Note

that effective spreads are smaller than quoted spreads, which is presumably due to negative

correlation between trading volume and bid-ask spreads. The quoted spreads are often as much

as five times greater than effective spreads. Quoted spreads must be wide when little or no

trading is taking place. This result shows the importance of using effective spreads rather than

quoted spreads to measure liquidity cost.

The number of observations (N) varies across contracts due to a differing number of

maturity months as well as differing beginning and ending dates of trading. For steel rebar, a

trade occurs 46% of the time. Undoubtedly, multiple trades occurred during some of these half-

second intervals, but we must treat them as a single trade. For several commodities, the effective

spread is only about one-and-one-half times the minimum tick size, but for others, the effective

spread is several times larger than the minimum tick size. The markets with effective spreads

near minimum tick sizes might be able to reduce effective spreads by reducing the minimum tick

sizes (Kuo et al. 2010).

Kurov (p. 1083, 2005) calculated percentage effective spreads for market orders at

Chicago Mercantile Exchange of .0003 for live cattle and .0006 for lean hogs. Assuming $65/cwt

for hogs and $90/cwt for cattle, Frank and Garcia’s (2011) percentage estimated spreads range

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from .0002 to .0011 for lean hogs and range from .0001 to .0007 for live cattle. Assuming a price

of $8/bushel for wheat at the Kansas City Board of Trade in 2008, Shah and Brorsen’s

percentage estimated spreads in the electronic market range from .0003 to .0010. Thus, the

percentage effective spreads calculated in Table 2 for Chinese futures markets are similar in

magnitude to those in United States futures markets.

Figure 1 and Figure 2 show the intraday patterns of effective spreads averaged across all

contracts. The figures show average dollar and percentage effective spreads in each 15-minute

interval. Both dollar and percentage effective spreads decline during the day and then increase

slightly at the close. Overall, intraday spreads of Chinese futures markets exhibit ‘reverse-J’

patterns. This result is similar to the findings of ap Gwilym and Thomas (2002) and Bryant and

Haigh (2004) for futures markets in other countries.

Figure 3 shows how absolute returns (a measure of volatility) for Chinese futures markets

vary by fifteen minute intervals. Overall, volatility in Chinese futures markets declines during

the day. The pattern of intraday volatility for Chinese futures markets is similar to that of other

futures markets. Two breaks in Chinese futures markets make three time periods of trading. At

the open of each time period of trading, the volatility is high and then declines. High volatility at

the open reflects the information accumulated during the break. The longer the break, the more

information accumulated, and thus the higher volatility. Figure 3 shows that at 9:00, volatility is

highest, and volatility at 13:30 is higher than volatility at 10:30. Thus, the results are consistent

with market closure theory.

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Previous studies (Ekman, 1992; Abhyankar, Copeland and Wong, 1999; ap Gwilym,

McMillan and Speight, 1999) find that intraday trading volume and volatility of futures markets

have similar U-shaped intraday patterns. Brock and Kleidon (1992) argue that transactions

demand is greater at the open and close and less elastic than at other times of the trading day. In

response, a market maker may effectively price discriminate by charging a higher price to

transact at these periods of peak demand. They predict that at the open and close, volume is high

and spreads are wide. In our study, the pattern of intraday trading volume is similar to the pattern

of intraday volatility. Figure 4 shows that the total trades in 15-minute intervals is high at the

open, declines with time, and finally increases at the close. Similar to intraday volatility, at the

start of each time period of trading, Figure 5 shows that the average contracts per trade is high at

the open, declines with time, and finally increases at the close. Moreover, intraday trade

frequency exhibits a W-pattern. Figure 6 shows that the trade number declines with time in the

morning, and after the two hour break, the trade number is high and then declines with time and

finally increases to a high level at the close. The intraday pattern of trade volume and bid-ask

spreads is consistent with the theory of Brock and Kleidon (1992). More frequent trades reduce

the cost to scalpers and larger trades may take out multiple limit orders.

Figure 7 shows that the percentage effective spread is positively related to trade size.

Black (1971) describes that in a liquid market, an investor can trade a large block of stock

immediately at a premium or discount that depends on the size of the block. This result means

that the larger the trade size, the larger the transaction cost. Kyle’s (1985) study also supports a

positive relationship between price change and trading size. Amihud (2002) discusses that for

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standard-size transactions, the price impact is within the bid–ask spread, whereas larger

transactions induce a greater impact on prices. Lin, Sanger, and Booth (1995) show a positive

relation between the effective spread and trade size for NYSE trades. Our result is consistent

with these studies.

Figures 8 and 9 show the relationship between time to maturity, volume, and percentage

effective spreads. Separate graphs are used for contracts that are traded for 12-month and 18-

months since they have very different patterns. Both show that spreads are inversely related to

volume. Volume is lowest in the first and last months of trading.

Daily volume initially increases as the maturity date approaches, but decreases when time

to maturity is less than two months for the 12-month maturity contracts and begins to drop

sooner for the 18-month maturity contracts.

IV. REGRESSION EQUATIONS

In this study, we also investigate what factors can influence the daily average effective

spreads, volume, and volatility. Based on previous studies, the daily average effective spread

may be related to volatility, daily trading number, average trade size, and time to maturity.

Moreover, the levels of effective spread can be different for different commodities. Therefore, an

econometric model is used to test the relation between the effective spread and these factors:

) ,,,,,,,,( 2 yaltickDTMDTMFrequencyTradeSizeVolatilityfPES (1)

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(1)

, , ,

where is the daily average percentage effective spread of commodity i, maturity m, and

day t, is the daily volatility for each contract, is the daily average

trade size, is the number of trades, DTM is the days to maturity of, tick is the

relative tick size (tick divided by the price), , are commodity fixed, , are day random effects,

and , ~ 0, and the two random terms are distributed independently. Volume is split

into two components since trade size is expected to increase spreads, but trading frequency is

expected to reduce spreads. Note that since the absolute tick size is held constant for each

commodity, the time series variation in relative tick size is all due to changes in the futures price.

The model is estimated using restricted maximum likelihood using random effects for date and

letting variances differ by commodity. Standard errors are calculated using cluster standard

errors, sometimes called a sandwich estimator, which is the cross-section time-series analogue

for heteroskedasticity-robust standard errors.

The regression models estimated for volatility and volume are similar to that estimated

for effective spreads. Anderson (1996) argues that volume reflects information flows and thus

offers a theory of why volume and volatility might be simultaneously determined. Some research

(Norden 2009; Chou and Wang 2006) has considered models with spreads, volume, and

volatility being simultaneously determined. We consider volume and volatility to be exogenous

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to the spread and so we do not include the spread in either the volume or volatility equation. The

equation estimated for log of volume is

(2) , , ,

where is the natural logarithm of number of contracts traded for commodity i, maturity m,

and day t, , are commodity fixed, , are day random effects, and , ~ 0, and the

two random terms are distributed independently. Similarly, the equation estimated for volatility

is

(3) , , ,

where , are commodity fixed, , are day random effects, and , ~ 0, and the two

random terms are distributed independently. Instrumental variables are used for the right hand

side endogenous variables. Similar to Norden (2009), the set of instruments that we use include

the exogenous variables, interactions of days to maturity and commodity, and lags of volatility,

trade size, frequency, percentage spreads, and relative tick size.

The regression results for effective spread are shown in Table 3. From this table, daily

volatility and the average trade size have positive impacts on average effective spread, and the

total trade number in one day has a negative impact on the average effective spread. These

results are consistent with the view that futures markets are a natural monopoly (Fabozzi et al.

2010) and thus effective spreads would decrease with frequency of trading. Large trades require

more liquidity and volatility increases the risk of inventory holding for scalpers so both of these

increase effective spreads. Effective spreads increase with relative tick spread sizes, which is

consistent with empirical work with stock markets that shows effective bid-ask spreads

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decreased when tick sizes were reduced (Wu, Krehbiel, and Brorsen 2011). Moreover as

expected based on figure 8, the relation between the effective spread and the day to maturity is

not linear, since DTM2 is positively related to the effective spread. Also effective spreads differ

by commodity beyond the factors explained by the other regressors. None of the elasticities

(shown in the note to Table 3) are huge, but they are all large enough to be economically

meaningful, with the 0.15 elasticity for relative tick size being the largest.

Most past research has found a simultaneous and positive relationship between volume and

volatility. As table 4 shows, our finding is that volume increases with volatility, but volatility is

not significantly influenced by volume. Volume increases as contracts approach maturity. The

elasticities (given in the note to Table 4) show that volume is more responsive to days to

maturity than any of the other factors. Volatility has a nonlinear relationship with volatility

increasing in the last 160 days of trading. Volume decreases as relative tick sizes increase, which

is consistent with results by Norden (2009). This reduced volume could be due to less high

frequency trading as relative tick sizes increase. Norden argues that reducing tick sizes can be a

revenue enhancing strategy for commodity exchanges since most of their revenues are fees per

contract traded.

V. CONCLUSION

The percentage effective spreads are within the range of spreads found in other world

markets, which suggests that Chinese futures markets are performing well in terms of providing

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a low-cost trading environment. The intraday effective spread, volatility, and trade number of

Chinese futures markets exhibit ‘reverse-J’ shape patterns. These findings are consistent with

previous studies of futures markets in other countries. Chinese futures markets have two breaks

during the day, and volatility is higher following each of the breaks. Effective spreads are lower

than quoted spreads, presumably due to bid-ask spreads being lower when markets are active. In

addition, daily effective spread is higher when the delivery date is distant, decreases as time

passes, and increases near to the maturity day. Contrary to the daily effective spread, daily

volume is low when the delivery date is distant, then increases, and finally decreases as maturity

nears. The findings are similar to those for markets in other countries, which indicate that

Chinese futures markets are functioning well. This information is important in providing

assurance to regulators in continuing to allow futures trading and to consider expanding futures

trading.

Regressions showed that effective bid-ask spreads decreased with trade frequency, but

increased with trade size. Past research that did not separate volume into frequency and trade size

missed this differential effect. Spreads also increased with volatility and relative tick size.

Volume went up with volatility, but volatility was unaffected by volume. Volume decreased as

relative tick sizes increased, which suggests that decreasing tick sizes can be a revenue

increasing strategy for commodity exchanges.

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Markets." Food Research Institute Studies 20(2):141-163.

Wang, H. H. and B. Ke, 2005, Efficiency Tests of Agricultural Commodity Futures Markets in

China. The Australian Journal of Agricultural and Resource Economics, 49, 125–141.

Wu, Y., T. Krehbiel, and B.W. Brorsen, 2011, Impacts of Tick Size Reduction on Transaction

Costs. International Journal of Economics and Finance, 3, 57-65.

Xin, Y., G. Chen, and M. Firth, 2006, The Efficiency of the Chinese Commodity Futures

Markets: Development and Empirical Evidence. China & World Economy, 14(2), 79-92.

22  

  

Table 1. Chinese Futures Exchange Contract Specifications

Exchange Commodity (Ticker) Price Limit Quotation Unit Trade Unit Tick size Maturity Months Margin

(% of contract value)

SHFE Aluminum (al) 4% Yuan/ton 5 Ton/lot 10 Yuan/ton All 5%

Copper cathode (cu) 4% Yuan/ton 5 Ton/lot 10 Yuan/ton All 5%

Zinc (zn) 4% Yuan/ton 5 Ton/lot 5 Yuan/ton All 5%

Natural rubber (ru) 4% Yuan/ton 5 Ton/lot 5 Yuan/ton All 10%

Steel rebar (rb) 5% Yuan/ton 10 Ton/lot 1 Yuan/ton All 7%

Steel wire rod (wr) 5% Yuan/ton 10 Ton/lot 1 Yuan/ton All 7%

Fuel oil (fu) 5% Yuan/ton 10 Ton/lot 1 Yuan/ton All 8%

Gold (au) 5% Yuan/gram 1 K/lot .01 Yuan/gram All 7%

DCE No. 1 soybeans (a) 4% Yuan/ton 10 Ton/lot 1 Yuan/ton F,H,K,N,U,X 5%

No. 2 soybeans (b) 4% Yuan/ton 10 Ton/lot 1 Yuan/ton F, H, K, N, U, X 5%

Soybean meal (m) 4% Yuan/ton 10 Ton/lot 1 Yuan/ton F, H, K, N, Q, U, X, Z 5%

Soybean oil (y) 4% Yuan/ton 10 Ton/lot 2 Yuan/ton F, H, K, N, Q, U, X, Z 5%

Corn (c) 4% Yuan/ton 10 Ton/lot 1 Yuan/ton F, H, K, N, U, X 5%

LDPE (l) 4% Yuan/ton 5 Ton/lot 5 Yuan/ton All 5%

RBD palm oil (p) 4% Yuan/ton 10 Ton/lot 2 Yuan/ton All 5%

Polyvinyl chloride (v) 4% Yuan/ton 5 Ton/lot 5 Yuan/ton All 5%

ZCE Strong gluten wheat (WS) 3% Yuan/ton 10 Ton/lot 1 Yuan/ton F, H, K, N, U, X 5%

Hard white wheat (WT) 3% Yuan/ton 10 Ton/lot 1 Yuan/ton F, H, K, N, U, X 5%

Cotton (CF) 4% Yuan/ton 5 Ton/lot 5 Yuan/ton F,H, K, N, U, X 5%

White sugar (SR) 4% Yuan/ton 10 Ton/lot 1 Yuan/ton F,H, K, N, U, X 6% 

Pure terephthalic acid (PTA) 4% Yuan/ton 5 Ton/lot 2 Yuan/ton All 6% 

Rapeseed oil (RO) 4% Yuan/ton 5 Ton/lot 2 Yuan/ton F,H, K, N, U, X 5% 

Rice (ER) 3% Yuan/ton 10 Ton/lot 1 Yuan/ton F,H, K, N, U, X 5%

Note: LDPE is low density polyethylene and RBD is refined bleached deodorized. The maturity months are January (F), March (H), May (K), July (N), August (Q), September (U), November (X), and December (Z).

23  

  

Table 2. Descriptive Statistics for Bid-Ask Spreads in Chinese Future Markets

Commodity (Ticker) N Spread Percentage

Spread Trades Trade Size

Effective Spread

Percentage Effective Spread

Aluminum (al) 9792293 19.34 0.0015 2728322 7.3 7.05 0.0005 Copper cathode (cu) 25681766 84.28 0.0021 6954529 11.3 15.07 0.0004 Zinc (zn) 15011016 42.90 0.0030 3938156 7.7 7.04 0.0005 Natural Rubber (ru) 15976225 46.92 0.0026 6016819 14.3 6.81 0.0004 Steel rebar (rb) 10753413 5.33 0.0013 4904052 30.6 1.37 0.0003 Steel wire rod (wr) 3005132 21.05 0.0053 317094 3.3 4.18 0.0011 Fuel Oil (fu) 11621182 7.63 0.0021 4320713 10.5 1.37 0.0004 Gold (au) 7763638 1.79 0.0085 1085141 2.9 0.04 0.0002 No. 1 soybeans (a) 7920436 4.39 0.0012 3666520 11.3 1.27 0.0004 No. 2 soybeans (b) 258969 47.88 0.0131 12642 2.5 25.91 0.0071

Soybean meal (m) 10932612 3.42 0.0012 6817872 21.6 1.13 0.0004 Soybean oil (y) 10331555 16.59 0.0024 5165518 17.5 2.39 0.0003 Corn (c) 4352437 1.57 0.0009 1441575 11.2 1.06 0.0006 LDPE (l) 8150608 39.51 0.0041 3252737 13.4 6.78 0.0007 RBD palm oil (p) 8657351 19.84 0.0034 3661729 11.5 2.80 0.0005 Polyvinyl chloride (v) 3079135 23.48 0.0032 1232269 13.8 7.17 0.0010 Strong gluten wheat (ws) 2925820 2.65 0.0012 898516 7.5 1.14 0.0005 Hard white wheat (wt) 147170 24.47 0.0127 8674 2.3 12.45 0.0065 Cotton (cf) 3217806 14.44 0.0010 949530 7.6 6.17 0.0004 White sugar (sr) 16197108 2.40 0.0006 5887009 22.9 1.25 0.0003 Pure terephthalic acid (pta) 9859782 26.33 0.0037 3696715 13.5 2.71 0.0004 Rapeseed oil (ro) 5452358 22.18 0.0029 1791149 6.0 2.74 0.0004 Rice (er) 862828 3.09 0.0015 298877 5.6 1.28 0.0006

 

24  

  

  Figure 1. The mean dollar effective spread in Chinese Futures Markets by 15-minute interval.     

Figure 2. The mean percentage effective spread in Chinese Futures Markets by 15-minute interval.

Intraday Effective Spread Pattern

01234567

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25  

  

Figure 3. The volatility in Chinese Futures Markets by 15-minute interval.

Figure 4. The volume in Chinese Futures Markets by 15-minute interval.

Intraday Volatility Pattern

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Intraday Volume Pattern

010002000300040005000600070008000900010000

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26  

  

Figure 5. Lot size in Chinese Futures Markets by 15-minute interval.

 

Figure 6. The trade number in Chinese Futures Markets by 15-minute interval.

Contracts Per Trade

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108110112114116118120122124126128130

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Figure 7. Relation between trade size and percentage effective spread.

Percentage Effective Spread

0

0.0002

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<100 <250 <500 <750 <1000 <2000 <3000 <4000

Contract/Trade

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28  

  

 

Figure 8. Changes in percentage effective spread and log daily volume with time to maturity for 12

month contracts.

Percentage Effective Spread

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0

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Logarithm of Daily Trading Volume

  

29  

Figure 9. Changes in percentage effective spread and log daily volume with time to maturity for 18

month contracts.

Percentage Effective Spread

0

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

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Logarithm of Daily Trading Volume

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30  

  

Table 3. Regression Estimates to Explain Percentage Effective Spreads

Regressor Coefficient t-Statistic Intercept 0.4996 18.58

Volatility 0.0416 12.12

Trade Size 0.0022 5.39

Frequency(100/ day) -0.0013 -31.00

DTM (100 days) 0.0224 4.41

DTM2 0.0077 7.09

Relative tick size 34.4766 9.17

Aluminum (al) -0.4811 -20.94

Gold (au) -1.6853 -10.35

Copper cathode (cu) -0.4912 -20.09

Fuel oil (fu) -0.1957 -7.57

Steel rebar (rb) -0.3618 -13.60

Natural rubber (ru) -0.2593 -10.86

Steel wire rod (wr) 0.0236 0.75

Zinc (zn) -0.2049 -8.19

Cotton (cf) -0.5080 -20.74

Rice (er) -0.4207 -13.72

Rapeseed oil (ro) -0.1471 -4.76

White sugar (sr) -0.6545 -27.57

Pure terephthalic acid (pta) 0.1144 3.63

Strong gluten wheat (ws) -0.6160 -24.72

Hard white wheat (wt) 0.0265 0.92

No. 1 soybeans (a) -0.3446 -14.52

No. 2 soybeans (b) 0.5962 13.51

Corn (c) -0.5350 -20.33

LDPE (l) 0.1103 3.78

Soybean meal (m) -0.2017 -6.99

RBD palm oil (p) 0.2589 7.96

Soybean oil (y) -0.0543 -1.78

Note: Parameters are estimated using restricted maximum likelihood with random effects for date and

variances differing by commodity. The standard errors are cluster standard errors. The number of

observations is 44,145. The means of variables used in the regression are percentage effective spread:

0.50%, volatility 1.04 hundredths, trade size: 6.2 contracts/trade, frequency: 15.6 hundred trades/day,

days to maturity (DTM): 1.94 hundred days, and relative tick size: 0.00475. The mean elasticities are

volatility .09, trade size .03, frequency -0.04, days to maturity 0.04, and relative tick size 0.16.

   

31  

  

Table 4. Regression Estimates to Explain Volume and Volatility in Chinese Futures Markets

Logarithm of Daily Volume Volatility Regressor Coefficient t-Statistic Coefficient t-Statistic Intercept 8.1754 102.54 1.1794 14.95 Volatility 0.1491 5.66

Log volume -0.0043 -0.92

DTM (100 days) -0.8108 -27.84 -0.0290 -2.73

DTM2 -0.1161 -18.45 0.0091 4.43

Relative tick size -51.9426 -7.07 -8.4224 -0.65

Aluminum (al) -0.1614 -2.27 -0.2247 -3.58

Gold (au) -0.9681 -3.00 0.2370 0.41

Copper cathode (cu) 0.5187 8.32 0.3429 3.84

Fuel oil (fu) -1.1268 -17.79 0.0098 0.13

Steel rebar (rb) 0.5023 4.09 -0.1489 -2.20

Natural rubber (ru) -0.3912 -6.27 0.3434 4.22

Steel wire rod (wr) -2.2771 -30.57 -0.0821 -1.21

Zinc (zn) -0.7605 -11.65 0.2826 3.47

Cotton (cf) -0.3827 -4.72 -0.6205 -9.01

Rice (er) -1.0023 -8.72 -0.6494 -9.91

Rapeseed oil (ro) -0.9038 -12.15 -0.0393 -0.50

White sugar (sr) 2.8994 37.66 -0.2737 -3.59

Pure terephthalic acid (pta) -1.7825 -20.03 0.0777 1.10

Strong gluten wheat (ws) 0.4322 4.97 -0.8183 -12.56

Hard white wheat (wt) -3.9518 -44.24 -0.6393 -9.53

No. 1 soybeans (a) 0.3912 5.47 -0.3187 -4.99

No. 2 soybeans (b) -3.6578 -49.89 0.0148 0.21

Corn (c) -0.1117 -1.48 -0.6800 -9.58

LDPE (l) -1.2566 -18.24 0.2730 3.90

Soybean meal (m) -0.0270 -0.35 -0.0190 -0.28

RBD palm oil (p) -1.1052 -15.93 0.2714 3.59

Soybean oil (y) -0.6759 -8.81 0.0637 0.90

Note: Parameters are estimated using restricted maximum likelihood with random effects for date,

variances differing by commodity, and instrumental variables. The standard errors are cluster standard

errors. The elasticities for the logarithm of daily volume are: volatility 0.03, days to maturity 0.19, and

relative tick size -0.05.