Asia-Pacific Commodity Exchange Consolidation Imminent? A Study of Asian-Pacific Commodity Exchanges and Their Possible Consolidation in the Near Future

Mengyi Jiang MMSS Senior Thesis Advisor Robert McDonald

Acknowledgement

At the beginning of this paper, I'd like to dedicate special thanks to my thesis advisor,

Professor Robert McDonald, for his endless help and enlightenment. It was with his help and

advice that this challenging topic could be dealt with ease and I was able to experience the full

joy of research.

I hope I could come back to this topic 10 years later when Asia has its very own monthly

and daily publically available data, to complete this research journey.

Abstract

If the theme of financial markets in the 1970s and 1980s was financial innovation, then

the financial markets in the last decade and a half are characterized by waves of consolidations.

Numerous national commodity exchanges disappeared and international commodity exchanges

emerged. However, Asia Pacific, the region of the most population on earth, was left out of this

round of merger mania. This paper looks into the history and the current status of APAC

exchanges and answer why merger has not taken place within the region. Furthermore, this

paper assumes that exchanges try to maximize their trading volume, thus examines the key

contributors to the rise and fall of trading volume of APAC contracts, and finally conjectures

which contracts would constitute a good regional commodity exchange.

1. Introduction

The Asian speed is indeed unprecedented, not only in the economic development, but

also in the derivatives trading. Lots of the exchanges were not born until the new millennium,

and yet they stand among the most volume-heavy exchanges in the world now. In 2006, Asia

Pacific’s (APAC's) exchange-traded derivatives volume was 3,511,548,4255, accounting for

29.60% of the global volume, compared to Europe’s 22.54% and North America’s 38.92% (FIA,

2007). 4 years later in 2010, APAC’s exchange-traded derivatives volume surged to

8,865,036,759, representing 39.8% of the global volume, compared to Europe’s 19.8% and

North America’s 32.2% (FIA, 2010).

The total exchange-traded derivatives volume may not reflect well on the commodity

trading volume as it also includes equity indices, individual equities, interest rate, foreign

currency, and other contracts. But the rank of commodity contracts will provide us insight into

how much commodity trading had grown over last 4 years. In 2006, 5 APAC contracts showed

up in the list of 20 largest commodity contracts by volume—they are Corn Futures of Dalian

Commodity Exchange (DCE) in China, Soy Meal Futures of DCE in China, White Sugar Futures of

Zhengzhou Commodity Exchange (ZCE) in China, Rubber Futures of Shanghai Futures Exchange

(SHFE) in China, and Gold Futures of Tokyo Commodity Exchange (TOCOM) in Japan. Notably, all

5 contracts came from 2 countries, China and Japan, and the 4 contracts from China were

agricultural while the only contract from Japan was gold as TOCOM sits in the 24-hour world

trading zone that bridges the gap between the trading in New York and London. In 2010, not

only the volume, but the landscape of the commodity trading changed dramatically. Among the

top 20 traded contracts, 11 came from APAC, of which 10 were traded in China and the other

traded in India, spanning from agriculture to metal to energy. In other words, the footprint of

APAC commodity exchanges had significantly broadened and strengthened over the mere 4

years. If we look at the ranking of top 20 futures and options worldwide by commodity type, i.e.

metal, agriculture and energy, provided by the Futures Industry over the years, more could be

concluded. I will further discuss about the relationship between the growth of commodity

contract volume and the economic development in Section 3 of this paper.

As the APAC commodity market kept expanding during 2006 and 2010, western

commodity exchanges underwent key consolidation as the home market saturated and

exchanges seeked other means to lift profit. Table 1 lists the major mergers or penetration

attempts that took place between 2006 and 2010. This merger mania continues in 2010 and

2011. Five exchange combinations have been proposed: Deutsche Boerse-NYSE Euronext; ICE &

NASDAQ-NYSE Euronext; London Stock Exchange Group-TMX Group; Singapore Exchange-ASX

Group, and Micex-RTS (FIA, 2010).

With all the frenzy in the western commodity markets, Asia seems to have been left out

in this round of consolidation. However, the demand for consolidation within the region is

clear: Singapore Exchange (SGX) bid for Australian Stock Exchange (ASX) Group in 2010, and

also in 2010 the Japanese government started discussions on whether to combine securities,

currencies and commodities bourses by 2013. Although the SGX's takeover bid was rejected by

the Australian government in early April 2011, showing political concerns over the

consolidation, the demand of gaining competitiveness and boosting trade is rising within APAC

region and maybe the next consolidation imminent. Therefore, it would be interesting to

conjecture which commodities would be traded should an APAC commodity exchange emerge.

To answer this question, I will first survey the history and key moments of commodity

exchanges, then look into the evolution of the Asian commodity exchanges, and then restrain

ourselves to three corner stone markets: China, India and Japan, and lastly extract and weigh

the factors that contribute to the rise and fall of commodity trading volume. Finally, I conclude

which contracts together which kind of institution under what circumstances would constitute

a successful regional commodity exchange in APAC.

2. Literature Review

To understand what attributes to a good contract and a good exchange, it is important

to study the history of modern commodity trading.

Although the first regulated exchange, Domiji Rice Exchange, was organized in 18th

century Japan, the real root of modern commodity trading lies in the United States. In the early

19th century, the surplus of farmers growing grains, wheat, corn, barley, rye and oats, and the

desire of people in other regions for more and cheaper grains propelled the growth of

transportation and trading of grains in the U.S. But the "entire procedure was attended by

considerable risk and speculation," which was assumed by everyone involved in the procedure,

as the time lag between the departure and delivery results in price and sales uncertainty (Clark,

1966). So, the forward contracts stepped in. It is said that initial forward contracts were used

by merchants to secure larger loans at a lower rate from the bank (Santos, 2010). But the

underlying motivation was that the forward contracts secured sales and price for the

merchants, reduced the credit risk of the bank, and thus stabilized the system. Therefore, this

need to hedge against the risk of fluctuation in price and variation in consumption is essential

for the existence and well-being of the forward contracts, and later for the future contracts.

Working (1953) confirmed the observation above. He emphasized economy as futures trading's

major distinguishing feature, and argued that the backbone of the futures trading is the

utilization of the advantages of futures markets for hedging, rather than for speculation. When

the commodity exchange progressed into the mid-20th century, Working (1953) listed 4 reasons

for hedging with futures contracts: (1) it facilitates buying and selling decisions; (2) it gives

greater freedom for business action; (3) it gives a reliable basis for conducting storage of

commodity surpluses; (4) it reduces business risk.

The landscape of futures market has changed considerably after Working. The western

exchanges went through tremendous volume growth in the 1970s and 1980s. Of course, some

of the volume growth should attribute to the economic growth as the demand for trading

naturally increases. But more importantly, the volume growth in this wave should attribute to

the financial innovation. Allen and Gale (1994a) offered a comprehensive overview of financial

innovations in history. Contracts that are widely traded today, such as currency futures,

mortgage-backed securities, and floating rate notes, etc. came into existence in the 70s, and

financial options, currency and interest-rate swaps, zero coupon bonds and junk bonds etc. in

80s, which started to play a major role in portfolio management, government and corporate

finance almost instantly as they were introduced. The fact that this wave of volume explosion

was accompanied by a frenzy of major financial innovations was not coincidental. The financial

market is inherently incomplete—not all states of nature can be spanned, so parties are not

able to move funds freely across time and space. As a result, successful financial innovation, by

either spanning the unspanned space or allowing more flexibility in the fund flow, unleashed

the demand that was not addressed or tackled by the existing contracts, and thus boosted the

trading volume. With this effect of financial innovation in mind, it is therefore necessary to

study the cause of the rise of financial innovation.

Tufano(2003) summarized the causes of the rise in 6 parts based on numerous

researches that have been done, which I think is quite complete.

1) Inherently incomplete markets stimulate innovation. Duffie and Rahi (1995), working

under incomplete financial markets, concluded that "there are incentives to set up markets for

securities for which there are no close substitutes, and which maybe be used to hedge

substantive risks," from the spanning perspective. Black (1986), Grinblatt and Longstaff (2000),

and Allen and Gale (1998) studied the incompleteness in particular cases under empirical

setting confirmed Duffie and Rahi's (1995) proposition.

2) Inherent agency concerns and information asymmetries stimulate innovation. Allen

and Gale (1994) surveyed the literature of contracting theory and addressed the concern of

asymmetric information between informed insiders and uniformed outsiders. Haugen and

Senbett (1981) showed in a particular case that moral hazard could be mitigated by

incorporating embedded options into securities. Other mitigation methods include involving

intermediaries according to Leland and Pyle (1977) and Diamond (1984). The financial

intermediaries, an innovation itself, stand as a new type of participant in the market and thus

further generate financial innovation needs.

3) Transaction, search or marketing costs could be minimized by innovation. Merton

(1989) showed that the existence of transaction cost brought advantage to financial

intermediaries in a realistic environment with friction, as the cost is shared among a group of

individuals at the intermediary level. Gurley and Shaw (1960) and others also studied the role of

transaction costs.

4) Taxes and regulation stimulate innovation. Miller (1986), most cited for this topic,

particularly listed taxes and regulations as the major impulse to the successful financial

innovations in this period. The income tax system of virtually every country requires different

rates of tax for different sources of income, e.g. between income from capital and income from

labor; between interest and dividends; between dividends and capital gains; between personal

income and corporate income; between business income paid out and business income

retained; between income earned at home and abroad; and so on. This difference embedded in

the income tax system creates incentive for people to transform the source that is heavily taxed

to those that are less taxed.

5) Increasing globalization and perceptions of risk stimulate innovation. Companies and

others encounter new risks, e.g. foreign exchange risk, in the phase of globalization, so the new

needs generate a new unspanned space for the financial innovation to step in. Human being's

inherent risk aversion provides another space for financial innovation to combat the increasing

volatility. Smith, Smithson and Wilford (1990), and Mason, Merton Perold and Tufano (1995)

contributed to the volatility study based on asset class observations and concrete life example

respectively. Notably, volatility could also contribute to the volume as more risk and hence

opportunities emerge. Daigler and Wiley (1999) concluded that the effect at least exist in some

groups of the traders, e.g. the general public who are distant from the trading floor and

therefore without precise information on order flow.

6) Technological shocks stimulate innovation. Carter (1989) suggested that this wave of

innovations was "an integral part of a wider dynamic of capital market restructuring spawned

by fundamental technological changes in the storage, transfer, and manipulation of financial

market data." For example, the utilization of electronic wire price quote services provided

investors with timely information, and wire transfer technologies granted investors ready

access to their funds around the world, which fundamentally contributed to the upsurge in

international capital flows back in the 80s. Besides, White (2000) wrote about financial

innovation from in light of the technological change.

Now we've seen that the boom of the new contracts and volume was not necessarily

the consequence of the boom in demand, but rather the discovery of existing demand or the

discovery of new demand by means of financial innovation. If we measure an exchange's

success by its trading volume, then a successful exchange is one that is able to conglomerate

enormous trading demand from hedging and speculation. Then our next question is: since the

economy of scale is a convex parabola that has its optimal point, is there a limit to the contracts

traded by the exchange to cater the demand of the market? Is there an optimal structure for

the exchange?

First on the governance of exchanges, Hart and Moore (1996) discussed about two

possible ways: the members' cooperatives (one member, one vote) versus outside ownership

(profit maximization), and concluded neither was efficient. But outside ownership is relatively

more efficient than members’ cooperative under two circumstances: as the variation across the

membership becomes more skewed (mean does not equal median) and as the exchange faces

more competition. This finding is consistent with Chicago Mercantile Exchange, the New York

Mercantile Exchange, and the Stockholm Exchange's shift to the outside ownership.

Second on the trading volume of the exchange (profit-maximizing point of view), if an

exchange prefers a futures contract choice that maximizes the trading volume, then there are

at least three cases where introducing a new contract would raise the transaction volume: 1)

the new contract contains risk that is unhedged by the current contracts traded; 2) the new

contract is composed of the risk already traded but could offer a higher volume because of

better design etc.; 3) the new contract consists of a group of contracts that are costly to trade

individually. Under this scheme, there is no limit to how many contracts are traded on the

exchange as the risk is unlimited, but the optimal innovation of futures contracts do exist.

Duffie and Jackson (1989) found that the most successful contracts, under the premise of

volume-maximizing, would be able to hedge against the risk that is not currently hedged by

existing contracts. However, they also concluded uncertain of the existence of a set of Nash

equilibrium contracts that is Pareto-optimal, for general contract design games.

The theory that adding new contracts could boost the trading volume and attract new

hedgers and speculators set the scene for the recent trend of exchange consolidation. As the

market starts to saturate and competition heats up, it becomes harder and harder to find a

contract that contains risk that is not obtained in currently traded contracts and is not traded

on other exchanges. Therefore, a natural solution would be to annex other exchanges and

incorporate their contracts so as to become more competitive in the market. The benefit of

exchange merger is many folds: 1) the spectrum of contracts traded broadened; 2) volume

raised; 3) cost lowered for the exchange by operating one exchange instead of two; 4) average

transaction cost lowered for traders as same or similar cost now covers more products; 5) new

traders attracted all the reasons above; … So the merged exchange enters a bust cycle in

trading volume. One of the best examples is the merger in 1998 between Deutsche

Terminbörse (DTB) and Swiss Options and Financial Futures (SOFFEX) while their major

competitors remained independent during the period. Table 2 shows the evolution of the

volume. The trading volume of Eurex, DTB and SOFFEX together, surged from 103,287,927, 7%

of world trading volume in 1997 before the merge to 364,833,663, 18.2% of the world trading

volume in 2000 after the merge. Its major competitors Chicago Board of Trade (CBOT) and

Chicago Mercantile Exchange's (CME) trade volume, on the other hand, remained stagnant

between those years, and their proportion of world volume subsided respectively.

I have been talking about exchanges in general. While most of the features between

different types of exchanges remain the same, there are features dedicated only to commodity

exchanges, namely the physical delivery and heterogeneity of contracts, which add to the

complexity of commodity markets. Unlike financial contracts, most commodity contracts

require physical settlement rather than cash settlement, and thus commodity exchanges need

warehouses to store the commodities before they are delivered. Due to the variation in

storage, transportation cost and margin requirements, price and trading volume of contracts

can greatly differ. For example, McConnell and Johnston, based on a case study, found while

that the variety of delivery options could broaden the appeal of the contract, they could also

reduce the hedging effectiveness of the contract and cause its extinction. Irwin et al. addressed

the issue of lack of convergence of the cash and futures prices of CBOT corn soybean and wheat

futures contracts, and attribute it to the storage rate change for CBOT. As another example,

silver prices tumbled 12% shortly after the market opened on May 2nd, 2010, as NYMEX,

operated by CME Group, raised margins requirements over the weekend.

Another complexity of commodity contracts lies in the heterogeneity naturally

imbedded in the physical commodity, which makes standardization of commodity contracts

much more difficult than naturally homogeneous financial products. The third issue is distinct

demand in people's consumption habits of commodity products across the world. It is often

interesting to notice different commodity exchanges offer different products to be traded in

accordance to the domestic eating or other habits. For example, chana (chickpea) and guar

seed are the top-traded products in India, while in China rice and soybean and its products are

among the hottest commodities.

So far, little literature has dedicated to the realm of exchange consolidation, let alone

commodity exchange consolidation, as it is still a novel trend, and consequences of which yet

take time to emerge. Therefore, for the rest of the paper, I will extract fundamentals from the

existent literature and study APAC commodities, commodity exchanges by applying basic

theories.

3. Data Analysis

3.1 Source

The data of commodity trading volume used in this paper comes from Futures and

Options Intelligence (FOi, http://www.fointelligence.com/), which is a provider for the global

exchange-traded derivatives market from FOW, a leading magazine and events group for the

industry. The database provides monthly trading volume and open interest data covering 11

APAC countries, 19 major commodity exchanges and 223 commodity contracts of agricultural,

energy, metal and soft commodity types. For the purpose of this study, all APAC countries,

commodity exchanges and commodity contracts were chosen, and soft and agricultural

commodities were regrouped as the agricultural commodities. The volume of contracts was

chosen as the subject matter instead of the commonly used open interest because there was

some inconsistency of open interest data within the dataset. The details, including

abbreviations, are included in Table 3.

The macro data, i.e. GDP (current US$), total value of stocks traded (current US$), are

retrieved from the World Bank database. The production, domestic consumption, import and

export data of agricultural commodities come from United States Department of Agriculture

(USDA). The metal production, import and export data attribute to the curtsey of U.S.

Department of the Interior-U.S. Geological Survey (USGS). Finally, the energy consumption,

production, import and export data were collected from U.S. Energy Information

Administration (EIA).

3.2 Overview of APAC Commodity Exchanges

It is interesting that APAC commodity exchanges have not undergone any consolidation

on the regional level, despite the merger mania in the West. The reasons are mainly two folds.

1) Most APAC commodity exchanges are young and are still vibrantly developing. As there is still

sufficient room for growth, consolidation is not yet on the agenda. The oldest among all

commodity exchanges is KANEX whose precursor Osaka Grain Exchange was established in

1952, and the youngest is SMX, created in 2008. Besides, a bulk of exchanges emerged after the

new millennium including UAE's first commodity exchanges DGCX and DME in 2005, TFEX in

2004, India's flagship commodity exchanges MCX and NCDEX in 2003, and JFX in 2000. China's

commodity exchanges started in the early 1990s. Then China's State Council, consolidated

nearly 50 local futures exchanges to create 15 larger regional exchanges in 1993, and further

consolidated the existing 15 into 3 national exchanges in 1998. Chinese commodity exchanges

are "early" in APAC, but certainly late compared with their western equivalent. 2) Commodity

exchanges in APAC are generally domestic. They are largely isolated from the international

capital flow. Some APAC commodity exchanges block foreign involvement, such as in China.

Most APAC nations impose capital control or foreign exchange (FX) control one way or another,

which deters international traders. For example, China not only imposes both capital and FX

control, but also set quota for capital inflow. As a result, the foreign fund involved is a tiny

proportion of the entire market. India is fortunate enough not to have FX control, but still

reinforces capital control. So, foreign funds cannot flow into most APAC countries freely, let

alone investing in the commodities. These particular aspects of APAC commodity exchanges

limited the possibilities of merger.

From next sub-section on, we will restrain ourselves to the trading activities in China,

India and Japan, which are big economies, big producers and consumers of commodity goods,

as well as big traders of commodity contracts. It is useful to look at the total volume of

commodity contracts (measured as the sum of the volume of all contracts) traded in each

country. The economies in APAC region, other than China, India, Japan and Malaysia, have very

low commodity contracts trading volume especially after the contracts have been introduced

for a couple of years (see Figure 1-11), suggesting that there is not much fundamental demand

for that contract, or the commodity markets are not well-developed. For either reason, the

contract should not be considered to be traded on the regional exchange should there be one.

So, it makes sense to exempt those countries from the rest of the study. I will put aside

Malaysia for now, as the all three contracts revolve around one commodity—palm oil, which

makes it less interesting.

But during this process, it drew my attention that while being the financial centers of

APAC, neither HK nor SG possessed a big commodity market. In fact, they are tiny. HK only

trades gold and monthly trading volume jumped around 600 between 2008 and 2010, the best

years of gold trading. SG trades more, but all the contracts, such as palm oil and rubber, that it

introduced before 2010 are in danger of dying. The new metal and energy contracts, such as

crude oil and gold, need yet to show more performance. Notably, HK and SG have one of the

biggest and most developed financial products trading platform within the region. HK is the hub

for equities and SG the hub for fixed income and currency products. There are 2 reasons why

they focused on financial products rather than commodity products. One, both of them are

small, and so lack the infrastructure, i.e. the warehouse, for physical settlement and delivery.

Two, neither of them has the physical proximity to the origin of the production of the

commodity, which is essential for commodity trading. As I have shown in Section 2, the

commodity market is very sensitive to the change in supply and demand, which means by

sitting closer to the source of the action, one could obtain the supply-demand information

more timely and thus gain better trading opportunity. In this case, HK would lose to China, SG

would of course lose to Malaysia in rubber and palm oil trading as Malaysia is one of the biggest

producers of both commodities. Commodity markets in HK and SG further prove the

fundamental relationship between the real production and consumption of the economy and

the success of commodity exchanges.

3.3 Factors Affecting Commodity Trading Volume in China, India and Japan

To select contracts to be traded on the regional exchange, it is important to find out

important factors that would contribute to the rise and fall of the volume of contracts.

Therefore, I will first look into the effect of macro factors and then dive into more specific

factors that could affect the trading volume in these three economies—China, India, and Japan.

Figure 12-14 give us a good sense of how total volume (measured as the sum of the

volume of all contracts traded) behaves in relation to GDP (measured in current US$) in the

past 3 decades. I eyeballed a very close to 1 correlation between total volume and GDP in each

country, and I also noticed that there's a decisive take-off in volume in China in 2004 and in

India in 2006 accompanied by explosion of commodity contracts. The situation in Japan is more

complicated as it is a developed economy and the exchanges have longer histories compared to

other nations—KANEX's precursor was founded in 1952, TOCOM's precursors were founded in

1951 and 1952, and TGE reopened in1952 . The volume and GDP diverged in Japan in 1995 as

the volume kept growing and the economy became stagnant. Then, however, the volume has

been declining for years after reaching the peak in 2003. This downward trend was not joined

by the Japanese macro-economy and of course not joined by the commodity boom worldwide

after 2003 either.

Figure 12-14, Source: Futures and Options Intelligence (FOi), World Bank

It is natural to ask why the volume in Japan behaved oddly relative to GDP as well as to

the global commodity boom especially after 2003. There are three major reasons which caused

huge inefficiency in trading, according to many sources. 1) The introduction of stricter rules

against soliciting business from retail investors in May 2005. The stricter rule led to the

evaporation of liquidity and caused many brokerage firms out of business. In March 2004 the

number of brokerage houses stood at 97; by the end of 2008 it had dropped to 53. 2) The out-

dated trading system. Of the three commodity exchanges this article concerns in Japan, only

TOCOM allows direct access via the Internet to place trades on its electronic system by 2009.

The rest operate with electronic systems used by member brokers, which require customers to

first contact member brokers to place orders. While the rest of the world emphasizes more and

more on time, technology and easiness to trade, it is not surprising that Japanese exchanges are

losing the ground. 3) Low concentration level of commodity exchanges. Despite the long

history, Japanese commodity market is the least concentrated even compared to the new

emerging commodity market in China and Japan. By the end of 2006, while India had 2 flagship

national commodity exchanges and China had 3, Japan had 6. And the low concentration seems

to have the root in the unwillingness to merge. In one of the press release of Reuters in June

2010, Tadashi Ezaki, TOCOM's president and CEO, told reporter that he was skeptical of the

merger of exchanges and would rather to focus on improving itself. 4) The lack of functionality

of the central clearing system which was introduced nationwide in 2005. 5) Finally, the financial

crisis in 2007 and 2008 might explain much about the steep decline of volume during that

period.

With the macro pictures in mind, I will then dive into each contract to look for some

exchange or contract specific factors in China and India. The method is that I plotted both the

volume of the contracts and the hedging demand, calculated as the sum of domestic

consumption, import and export of the commodity, on the same graph. There is nothing

alarming on most graphs, as the contract specific trend traces partially the general volume

trend of the exchange and partially the hedging demand of the commodity. However, an

interesting phenomenon emerged in the contracts traded by DCE (See Figure 15-20). The

trading of DCE No.2 Soybean Future should be taken out of the picture because due to the fact

that No.1 Soybean Future was a close substitute of No.2 Soybean Future, the trading volume

shifted to No.1 Soybean which was more frequently traded, and the trading of No.2 Soybean

became virtually zero after 2007. Keep in mind that the Chinese GDP has gone through

tremendous growth from 2006 to 2010, and there was no sign that the hedging demand was

dropping on average, and yet the trend of volume across all DCE contracts reversed in 2008 and

went on a downturn in 2009. Looking into other factors of the exchange conduct, I found

everything was the same except for that DCE raised the margin on March 24, 2008 from 5% to

7%, meaning keeping the contracts more expensive. Looking more closely at the monthly

volume of all contracts in DCE, I found this sharp plummet of volume in July 2008 following the

margin shock, which did not happen in 2007 or in 2009 (See Figure 21). Therefore, margin hike

was probably the key killer of volumes on DCE since 2008.

Figure 21

Therefore, on the macro basis, some of the factors that could significantly affect the

volume are: GDP, hedging demand of the commodity (including production, domestic

consumption, import and export), introduction of new regulations, adoption of modern

technology, mergers, infrastructure such as the central clearing system, financial crisis, and

margins. Note that during the same period, the western commodity exchanges also went

through a volume surge. Also note that the commodity market is one form of capital market, so

its development should be affected by the development of the capital market as well. For this

reason, I would also include the total value of all stocks traded on the exchange (current US$)

as a measure of the development of the capital market. Other shocks, such as change in

warehouse or delivery options, could drastically change the trading volume as I have shown in

Section 2. However, because there was either no shock or the shocks did not show up

disturbing the volumes, I would not include them in the next phase of the study.

3.4 Weighing on the Importance of Each Factor, the case of China and Japan

With all the factors included in the paragraph above, plus the volume of each contract

traded in China, India and Japan from 1980 to 2010, I will run regressions of contract or groups

of contracts on each factor. However, unfortunately, only annual consumption, production,

import and export data was available for some of the commodity goods traded in China and

Japan; as for India, there was essentially no available data. Therefore, I dropped India for the

purpose of the research and preceded running regressions on China and Japan.

3.4.1 Variable Specification:

CHN: China

JP: Japan

CHNTotVol: the sum of the volume of all contracts traded in the three commodity exchanges in

China, i.e. DCE, SHFE, ZCE.

JPTotVol: the sum of the volume of all contracts traded in the three commodity exchanges in

Japan, i.e. KANEX, TGE, TOCOM.

GDPcountry: GDP of one country, measured in current US$, where country= {CHN, JP}

Time: the de-trend variable that takes value {1,2,3,4…}

CHNCommodityi: trading volume of specific contract, where Commodityi is a commodity

contract traded in one of the commodity exchanges in China.

JPCommodityi: trading volume of specific contract, where Commodityi is a commodity contract

traded in one of the commodity exchanges in Japan.

CHNProductionCommodityi: Production of Commodityi in China.

CHNImportCommodityi: Import of Commodityi in China.

CHNConsumptionCommodityi: Consumption of Commodityi in China.

JPProductionCommodityi: Production of Commodityi in Japan

JPImportCommodityi: Import of Commodityi in Japan.

JPConsumptionCommodityi: Consumption of Commodityi in Japan.

TOCOMOIL: Trading volume of the Crude Oil contract on TOCOM.

Dummy: takes value on every month: equals 1 from April to September, 0 otherwise.

FinCrisis: dummy for the financial crisis in 2007 and 2008: equals 1 for every month in 2007 and

2008, 0 otherwise.

3.4.2 China:

First, I looked at the macro-aspect by total volume on GDP to see if I could conclude a

robust result in general. Since the rise in total volume we have seen earlier could be due to the

time trend, I employed the de-trended model to derive the coefficients.

( ) ( )

(t=-0.90) (1.15) (11.36)

The result of this regression is rather interesting: while the R-squared skyrocketing to

0.9418 and the only significant variable being the de-trend variable Time, the entire total

volume trend was shown to be picked up by the time trend instead of the volume. This result is

counter-intuitive. The main explanation is that by restricting to the annual data between 2000

and 2009 in the regression, only ten observations were included, which did not provide enough

detail for the movement in volume to be picked up by independent variables, i.e. GDP, on the

right-hand side.

Regressions ran on specific commodity traded on the exchange (see below), using the

sum of production and import of the specific commodity as a proxy for consumption, yield

statistically significant 's. However, simple as it is, no sophisticated conclusion could be made

from the regression.

( )

Therefore, it was concluded by this stage that the monthly data (at least) were needed

in order to run more sophisticated regressions. Since China lacked all the monthly data, it had

to be dropped for the next phase of the study.

3.4.3 Japan:

The same regressions were run for Japan:

( ) ( )

(t=-5.85) (7.60) (1.92)

With more annual observations, from 1980 to 2009, the regression on Japan offers a

more intuitive result. The time trend with a small coefficient of .034607 has a t-statistic of 1.92,

which is no longer statistically significant at the 5% level, but still statistically significant at 10%

level. LOG (GDP) now has a t-statistic of 7.6, statistically significant at 5% level, and a coefficient

of 2.264, which says that a 1% increase in GDP in Japan would lead to a 2.26% increase in the

trading volume in Japan.

Contract specific regressions (see below) were run to gauge the effect of consumption

as well as production change on the trading volume. In the Japan case, most 's in regression 1,

proxy for consumption, are not statistically significant, while the 's in regression 2 are

statistically significant.

( )

2. ( )

Given the fact that Japan is a more established economy with longer history,

fortunately, the consumption, production and import data of Japan's crude oil is publically

available. Therefore, next section will concentrate on the crude oil contract in Japan and the

discovery of variables that affect the trading volume.

3.5 Focusing on Japanese Crude Oil Contract

A little background would be very helpful in understanding the consumption pattern of

crude oil in Japan. Japan is heavily dependent on the crude oil import due to its scarce crude oil

reserve and costly domestic exploration. It imports close to 100% of its crude oil consumption.

Ever since the first oil crisis in 1973, the Japanese government has been trying to reduce the

economy's dependence on oil, for security reasons, by 1) shifting the consumption of oil to

other sources of energy, such as coal and natural gas; 2) improving the capacity utilization ratio.

The action was quite successful. The dependence of oil has thus decreased from 77.4% of all

energy use in 1973 to 46.4% in 2008 (Figure 21).

It is important to notice that in the regressions above and below, consumption is run as

the major independent variable while other independent variables being the controlling

variable. The reason stems from the Working's (1953) paper and the theory of hedging being

the cornerstone of the prosperity of the commodity market. Since the hedging demand mainly

comes from consumption demand, it is natural to use consumption as a proxy for hedging

demand.

Observing the pattern of total petroleum consumption pattern in Japan (Figure 22), I

found a strong seasonality trend, where the consumption peaks in winter seasons, mostly in

January, and bottoms up in summer seasons, mostly in June. Therefore, the regression of crude

oil trading volume on the consumption includes an interaction term dummy*JPConsumptionoil,

where the dummy equals 1 from April to September (the summer season), and 0 otherwise.

(t= -4.35) (7.02) (3.96)

The results are statistically significant at 5% level both for consumption and the

interaction term. More importantly, the interaction term is positive, meaning that the market

reacts more excitedly to the increase in consumption in the summer than in the winter. One

possible explanation for this is that there's more variation in possible consumption in summer

than in winter, which should be revealed in the standard deviation of the return to oil price.

Dividing up the price data in the same fashion as I did for the consumption, I obtained the

standard deviation of TOCOMOIL in summer to be 0.03741, and in winter to be 0.026679, which

reconciles with the observation. If we look at the same standard deviation of the return to oil price in

NYMEX sweet crude oil, the difference is a lot smaller with summer at0.031697 and winter at 0.031272.

This smaller difference could be explained by the fact that NYMEX is a much more global trading

platform with more diverse demand, e.g. demand for the south hemisphere, which would close the gap

between the differences.

Further eyeballing the graph of oil consumption and the TOCOM crude oil trading volume

(Figure22&23), I found their pattern similar. One natural question arose was that whether the

consumption data affect the trading activity and whether the trading volume predicted the consumption

volume. I first checked whether the consumption data had an effect on the trading volume by running

the trading volume on consumption, its one-month and twelve-month lag, and controlling for the

production and its one- and two-month lags. The reason for including the one-month and twelve-month

consumption lag was that the statistics of time n's consumption should be highly correlated with time

n+1's consumption. Also, when trading time n's consumption, the traders are likely to refer to the same

time last year, namely time n-12, for some possible information of consumption at time n. The

regression results come as follows:

(t=-6.01) (2.14) (2.85)

(2.82) (-0.67)

(-0.38) (0.47) (2.33)

We could conclude that current month's consumption does have statistically significant

positive effect on the trading volume and the effect is bigger in summer seasons than in winter

seasons. Furthermore, the regression confirms that prediction that previous month's

consumption would have a positive effect on current month's trading volume. Lastly, the fact

that the coefficient of the two-month lag of Japanese domestic oil production is statistically

significant shows that 1,000 barrels/day increase of oil production (measured in 1000

barrels/day), i.e. about 30,000 barrels/month increase, would lead to an increase of 25,625.28

contracts traded. This close to one-to-one effect possibly results from the ultra-low domestic

production level in Japan and the fact that domestic production is the marginal supply of oil on

the market. Note as is introduced earlier, the import of crude oil in Japan is close to 100% and

thus production represents a tiny percentage of oil supply. Thus the coefficient on the

production lag very likely reveals the marginal effect on trading volume by the change of oil

supply.

Realizing that the trading volume tanked between 2007 and 2008 (Figure 23) and then

quickly picked up since 2009, I therefore included the financial crisis dummy for the 2007 and

2008 crisis, which equals 1 for every month in 2007 and 2008 and 0 otherwise. Below is the

result:

(t=-6.18) (2.04) (2.99)

(2.91) (-0.66)

(-0.19) (0.56) (2.52)

(-3.97)

As is seen from the result that by adding financial crisis dummy to the regression, the

statistical significance of variables previous run does not change and the coefficents value

almost stay the same, and meanwhile the financial crisis in 2007 and 2008 decrease the

monthly trading volume by 42866.37, which is statistically significant at 5% level.

4. Discussion

Due to the time constraint of the study, limited data and the complexity of the

regression, more conclusive results entail further research and more ample data. Had the time

and data be present, similar regressions and analysis applied to the crude oil contract traded on

Tokyo Commodity Exchange would be conducted on every single contract so as to be able to

generalize the theory that consumption matters statistically significantly and thus we could look

into the demand of specific commodities form the region in order to pick potentially successful

contracts that could be traded on the regional commodity exchange in APAC.

5. Concluding Remarks

The motivation of writing this article is two folds: 1) the APAC commodity markets are

drastically booming, hand in hand with their GDP; 2) the western exchanges went through a

major round of regional consolidation in the last decade or so, and yet the APAC exchanges

remained national, rather than international. The answer to the question why there was no

regional consolidation is that APAC commodity exchanges are young and many APAC countries

impose capital controls which made any merger virtually impossible. Under the premise that

exchanges maximize volume, we then went through the history and key moments (e.g. financial

innovation) of the development of western exchanges, and identified major elements

contributing to the development and thus the volume of contracts. Next, we looked into APAC

region and confirmed the importance of natural hedging demand to the trading volume. Then

we focused on the commodity exchanges in China, India and Japan, and came up with a list of

factors that cause the rise and fall of commodity contracts, including GDP, change of

regulations, technology upgrade, change in margin, to name a few. Finally, we focused on the

crude oil contract of TOCOM, which was the only contract in APAC that has the monthly data of

consumption, production and import for the corresponding commodity. Some interesting

results were found, for example of the seasonality of contract trading volume and summer and

winter's effect on trading volume respectively. Although overall consumption pattern and effect

could not be revealed by the currently available data, the route is clear and once the data

available, results should be conclusive. By then, the contracts to be traded on the regional

platform in APAC would be clear to all.

References Santos, Joseph, 2010, "A History of Futures Trading in the United States." Clark, John. G, 1966, "The Grain Trade in the Old Northwest," Urbana: University of Illinois Press. Working, H., 1953, "Futures Trading and Hedging," American Economic Review, 43:314-343. Working, H., 1953, "Hedging Reconsidered," Journal of Farm Economics, 35:544-561. Carter, Michael, 1989, "Financial Innovation and Financial Fragility," Journal of Economic Issues, 23(3):779-793. Miller, Merton H., 1986, "Financial Innovation: The Last Twenty Years and the Next," The Journal of Financial and Quantitative Analysis, 21(4):459-471. Irwin, S.H., P. Garcia, D.L. Good, and E.L. Kunda, 2009, "Poor Convergence Performance of CBOT Corn, Soybean and Wheat Futures Contracts: Causes and Solutions," Marketing and Outlook Research Report 2009-02, Department of Agricultural and consumer Economics, University of Illinois at Urbana-Champaign. Duffie, D., M.O. Jackson, 1989, "Optimal Innovation of Futures Contracts," The Review of Financial Studies, 2(3):275-296. Johnson, E.T., J.J. McConnell, 1989, "Requiem for a Market: An Analysis of the Rise and Fall of a Financial Futures Contract," The Review of Financial Studies, 2(1):1-23. Tufano, Peter, 2003, "Financial Innovation," Handbook of the Economics of Finance, ed. By George Constantinides and Rene Stulz, pp. 308-331. Allen, F., D. Gale, 1994a, "Financial Innovation and Risk Sharing", MIT Press, Cambridge, MA. Black, D.G., 1986, " Success and failure of futures contracts: theory and empirical evidence," Salomon Brothers Center for the Study of Financial Institutions Monograph Series in Finance and Economics 1986:1 (Graduate School of Business Administration, New York University). Allen, F., D. Gale, 1988, "Optimal Security Design," Review of Financial Studies, 1(3):229-263. Grinblatt, M., F.A. Longstuff, 2000, "Financial innovation and the role of derivative securities: and empirical analysis of the Treasury STRIPS program," Journal of Finance, 55(3):1415-1436. Haugen, R.A., L.W. Senbett, 1981, "Resolving the agency problems of external capital through options," Journal of Finance, 36(3):629-647.

Leland, H.E., D.H. Pyle, 1977, "Informational asymmetries, financial structure, and financial intermediation," Journal of Finance, 32:371-387. Diamond, D., 1984, "Financial intermediation and delegated monitoring," Review of Economic Studies, 51:393-414. Gurley, J.G., E.S. Shaw, 1960, "Money in a Theory of Finance," Brookings Institution, Washington, D.C. Merton, R.C., 1989, "On the application of the continuous time theory of finance to financial intermediation and insurance," Geneva Papers on Risk and Insurance, 14(July):225-262. White, L.J. 2000, "Technological change, financial innovation, and financial regulation in the U.S.: the challenge for public policy," in: P. Harker and S. Zenios, eds., Performance of Financial Institutions, (Cambridge University Press, Cambridge, UK) pp. 388-415. Hart, O., J. Moore,1996, "The Governance of Exchanges: Members' Cooperatives Versus Outside Ownership," Oxford Review of Economic Policy, 12(4). FIA, 2007, "Volume Surges Again," Financial Industry, issue of March/April. FIA, 2010, "Annual Volume Survey," Financial Industry, issue of March. Daigler, R.T., M.K. Wiley, 1999, "The Impact of Trader Type on the Futures Volatility-Volume Relation," Journal of Finance, 54(6).

Table 1

Year Merger

2006 Chicago Mercantile Exchange (CME), the world's largest derivatives exchange, agreed to purchase Chicago Board of Trade (CBOT) for $8 billion to create the world's largest publicly traded exchange by market capitalization.

2007 Atlanta-based Intercontinental Exchange (ICE) acquired the New York Board of Trade (NYBOT), ChemConnect (a chemical commodity market), and Canada's Winnipeg Commodity Exchange for $1.1 billion.

2008 CME Group ratified the equity stake of about 10 percent in Brazilian Mercantile & Futures Exchange SA (BM&F), the world's fourth-largest futures exchange. BM&F acquired about 1.2 million shares of CME Group common stock.

2008 NYSE Euronext purchased a 5 percent stake in India's Multi Commodity Exchange (MCX) for $55 million, aiming to obtain a slice of the commodities boom in India

2008 CME Group, the parent company of the CME and CBOT, finalized a deal to buy NYMEX for about $9.4 billion

Source: Reuters, http://www.reuters.com/article/2008/03/18/nymex-cme-idUSN1840775320080318

Table 2, Source: United Nations Conference on Trade and Development (UNCTAD)

Rank

2000

Exch

ange

Co

untry

19

97

Volu

me

1998

Volu

me

1999

Volu

me

2000

Volu

me

1997

Prop

ortio

n of

wor

ld vo

lum

e

1998

Prop

ortio

n of

wor

ld vo

lum

e

1999

Prop

ortio

n of

wor

ld vo

lum

e

2000

Prop

ortio

n of

wor

ld vo

lum

e

1EU

REX

Germ

any

109,

287,

927

187,

263,

716

313,

955,

123

364,

833,

663

7%11

.20%

18%

18.2

0%

2CB

OTUS

242,

698,

919

281,

189,

436

254,

561,

215

233,

528,

558

15.7

0%16

.80%

14.6

0%11

.50%

3CM

EUS

200,

714,

428

226,

618,

831

200,

737,

920

231,

114,

296

13%

13.5

0%11

.50%

11.4

0%

4KS

EKo

rea

7,78

0,48

450

,204

,404

97,1

37,0

0721

3,49

5,58

80.

50%

3%5.

60%

10.5

0%

5Pa

risBo

urse

Fran

ce78

,144

,177

60,0

20,2

8411

8,82

2,82

514

7,06

5,64

35%

3.60

%6.

80%

7.20

%

6LIF

FEUK

205,

129,

701

191,

086,

246

116,

438,

648

125,

569,

936

13.3

0%11

.40%

6.70

%6.

20%

7NY

MEX

US83

,851

,346

95,0

18,6

8510

9,35

8,83

110

4,07

5,23

85.

40%

5.70

%6.

30%

5.10

%

8BM

&FBr

azil

122,

179,

393

87,0

15,0

5055

,931

,098

82,9

45,2

777.

90%

5.20

%3.

20%

4.10

%

9LM

EUK

57,3

72,5

0053

,075

,081

61,5

97,5

5766

,445

,247

3.70

%3.

20%

3.50

%3.

30%

10TO

COM

Japa

n30

,178

,349

43,5

89,7

2348

,442

,161

50,8

51,8

821.

90%

2.60

%2.

80%

2.50

%

11CB

OEUS

71,2

12,2

4568

,358

,848

56,2

69,9

1847

,440

,139

4.60

%4%

3.20

%2.

30%

12SF

EAu

stra

lia28

,409

,539

29,9

27,2

4929

,793

,333

31,2

99,0

211.

80%

1.80

%1.

70%

1.50

%

13Eu

rone

xtBr

usse

lsBe

lgiu

m2,

125,

118

1,81

0,14

86,

881,

821

31,2

13,5

860.

10%

0.10

%0.

40%

1.50

%

14Si

ngap

oreE

xcha

nge

Sing

apor

e24

,090

,285

27,8

61,1

6225

,863

,140

27,5

71,9

631.

60%

1.60

%1.

50%

1.40

%

15IP

EUK

14,7

33,3

4219

,442

,867

23,0

42,8

3325

,491

,139

0.90

%1.

20%

1.30

%1.

30%

Tota

l1,

543,

064,

206

1,67

0,69

7,40

51,

737,

494,

607

2,02

2,13

8,94

010

0%10

0%10

0%10

0%

Sub

Tota

l1,

277,

907,

753

1,42

2,48

1,73

01,

518,

833,

430

1,78

2,94

1,17

682

%85

%87

%88

%

Top

15 W

orld

Futu

res a

nd O

ptio

ns Ex

chan

ges

Volu

me

by C

alen

der Y

ear (

Rank

ed b

y 200

0 Vol

ume*

)

*Exc

ludi

ng o

ptio

ns o

n in

divi

dual

equ

ities

Table 3 Country & Region

APAC Exchanges that Trade Commodity Contracts

Australia (AUS) ASX Ltd (ASX), ASX 24 (SFE)

China (CHN) Dalian Commodity Exchange (DCE), Shanghai Futures Exchange (SHFE), Zhengzhou Commodity Exchange (ZCE)

Hong Kong (HK) Hong Kong Exchanges & Clearing Limited (HKEX)

India (IND) Multi Commodity Exchange of India Ltd (MCX), National Commodity & Derivatives Exchange (NCDEX)

Indonesia (INA) Jakarta Futures Exchange (JFX)

Japan (JP) Kansai Commodities Exchange (KANEX), Tokyo Grain Exchange (TGE), Tokyo Commodity Exchange (TOCOM)

Korea (KOR) Korea Exchange (KOFEX)

Malaysia (MAS) Bursa Malaysia Derivatives Berhad (MDEX)

Singapore (SG) Singapore Exchange Derivatives Trading (SGX-DT), Singapore Commodity Exchange (SICOM), Singapore Mercantile Exchange (SMX)

Taiwan (TW) Taiwan Futures Exchange (TAIFEX)

Thailand (THA) Thailand Futures Exchange (TFEX)

Figure 1-11, Source: Futures and Options Intelligence (FOi)

020000000400000006000000080000000

100000000120000000140000000160000000180000000200000000

Jan

-80

Oct

-81

Jul-

83

Ap

r-8

5

Jan

-87

Oct

-88

Jul-

90

Ap

r-9

2

Jan

-94

Oct

-95

Jul-

97

Ap

r-9

9

Jan

-01

Oct

-02

Jul-

04

Ap

r-0

6

Jan

-08

Oct

-09

CHINA TOTAL VOLUME

0

200

400

600

800

1000

1200

1400

1600

1800

Jan

-80

Au

g-8

1

Mar

-83

Oct

-84

May

-86

De

c-8

7

Jul-

89

Feb

-91

Sep

-92

Ap

r-9

4

No

v-9

5

Jun

-97

Jan

-99

Au

g-0

0

Mar

-02

Oct

-03

May

-05

De

c-0

6

Jul-

08

Feb

-10

HK TOTAL VOLUME

0

200000

400000

600000

800000

1000000

1200000

1400000

1600000

Jan

-80

Oct

-81

Jul-

83

Ap

r-8

5

Jan

-87

Oct

-88

Jul-

90

Ap

r-9

2

Jan

-94

Oct

-95

Jul-

97

Ap

r-9

9

Jan

-01

Oct

-02

Jul-

04

Ap

r-0

6

Jan

-08

Oct

-09

TW TOTAL VOLUME

0

2000000

4000000

6000000

8000000

10000000

12000000

14000000

Jan

-80

Oct

-81

Jul-

83

Ap

r-8

5

Jan

-87

Oct

-88

Jul-

90

Ap

r-9

2

Jan

-94

Oct

-95

Jul-

97

Ap

r-9

9

Jan

-01

Oct

-02

Jul-

04

Ap

r-0

6

Jan

-08

Oct

-09

JP TOTAL VOLUME

0

5000

10000

15000

20000

25000

30000

Jan

-80

Sep

-81

May

-83

Jan

-85

Sep

-86

May

-88

Jan

-90

Sep

-91

May

-93

Jan

-95

Sep

-96

May

-98

Jan

-00

Sep

-01

May

-03

Jan

-05

Sep

-06

May

-08

Jan

-10

KOR TOTAL VOLUME

0

5000000

10000000

15000000

20000000

25000000

Jan

-80

Sep

-81

May

-83

Jan

-85

Sep

-86

May

-88

Jan

-90

Sep

-91

May

-93

Jan

-95

Sep

-96

May

-98

Jan

-00

Sep

-01

May

-03

Jan

-05

Sep

-06

May

-08

Jan

-10

IND TOTAL VOLUME

0

50000

100000

150000

200000

250000

300000

350000Ja

n-8

0

Au

g-8

1

Mar

-83

Oct

-84

May

-86

De

c-8

7

Jul-

89

Feb

-91

Sep

-92

Ap

r-9

4

No

v-9

5

Jun

-97

Jan

-99

Au

g-0

0

Mar

-02

Oct

-03

May

-05

De

c-0

6

Jul-

08

Feb

-10

SG TOTAL VOLUME

0

100000

200000

300000

400000

500000

Jan

-80

Au

g-8

1

Mar

-83

Oct

-84

May

-86

De

c-8

7

Jul-

89

Feb

-91

Sep

-92

Ap

r-9

4

No

v-9

5

Jun

-97

Jan

-99

Au

g-0

0

Mar

-02

Oct

-03

May

-05

De

c-0

6

Jul-

08

Feb

-10

MAS TOTAL VOLUME

0

1000

2000

3000

4000

5000

6000

7000

Jan

-80

Au

g-8

1

Mar

-83

Oct

-84

May

-86

De

c-8

7

Jul-

89

Feb

-91

Sep

-92

Ap

r-9

4

No

v-9

5

Jun

-97

Jan

-99

Au

g-0

0

Mar

-02

Oct

-03

May

-05

De

c-0

6

Jul-

08

Feb

-10

INA TOTAL VOLUME

0

20000

40000

60000

80000

100000

120000

140000

160000Ja

n-8

0

Sep

-81

May

-83

Jan

-85

Sep

-86

May

-88

Jan

-90

Sep

-91

May

-93

Jan

-95

Sep

-96

May

-98

Jan

-00

Sep

-01

May

-03

Jan

-05

Sep

-06

May

-08

Jan

-10

THA TOTAL VOLUME

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

Jan

-80

Au

g-8

1

Mar

-83

Oct

-84

May

-86

De

c-8

7

Jul-

89

Feb

-91

Sep

-92

Ap

r-9

4

No

v-9

5

Jun

-97

Jan

-99

Au

g-0

0

Mar

-02

Oct

-03

May

-05

De

c-0

6

Jul-

08

Feb

-10

AUS TOTAL VOLUME

Figure 15-20

Figure 21

Source: Ministry of Economy, Trade and Industry

Figure 22

Figure 23