international financial markets

INTERNATIONAL FINANCIAL MARKETS

FRONTIERS OF ECONOMICSAND GLOBALIZATION

13

Series Editors:

HAMID BELADIUniversity of Texas at San Antonio, USA

E. KWAN CHOIIowa State University, USA

FRONTIERS OF ECONOMICS AND GLOBALIZATIONVOLUME 13

INTERNATIONAL FINANCIALMARKETS

Edited by

Hung-gay FungUniversity of Missouri-St. Louis, St. Louis, MO, USA

Yiuman TseUniversity of Missouri-St. Louis, St. Louis, MO, USA

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ABOUT THE SERIES: FRONTIERS OF ECONOMICSAND GLOBALIZATION

This series is aimed at economists and nancial economists worldwideand will provide an in-depth look at current global topics. Each volume inthe series will focus on specialized topics for greater understanding of thechosen subject and provide a detailed discussion of emerging issues. Thetarget audiences are professional researchers, graduate students, andpolicy makers. It will offer cutting-edge views on new horizons and deepenthe understanding in these emerging topics.With contributions from leading researchers, each volume presents a fresh

look at todays current topics. This series will present primarily originalworks, and employ references appropriate to the topic being explored.Each volume will bring a set of highly concentrated articles that will

provide in-depth knowledge to a target audience, while the entire serieswill appeal to a wide audience by providing them with deeper knowledgeon a broad set of emerging topics in the global economy.The Frontiers of Economics and Globalization series will publish on

topics such as:

Frontiers of Trade Negotiations Frontiers of Derivative Pricing Frontiers of International Lending and Debt Problems Frontiers of Economics Integration Frontiers of Trade and Environment Frontiers of Foreign Exchange Frontiers of International Finance Frontiers of Growth of Open Economies Frontiers of Futures Pricing Frontiers of International Financial Markets Frontiers of Investment Banking Frontiers of Mergers and Acquisitions Frontiers of Government Policy and Regulations Frontiers of Multi-Sector Growth Models Frontiers of Intellectual Property Rights Frontiers of Fragmentations and Outsourcing

Hamid BeladiE. Kwan ChoiSeries Editors

ABOUT THE EDITORS

Hung-Gay Fung is Dr. Y.S. Tsiang endowed chair professor of Chinesestudies and department chair in the Finance and Legal Studies Department,College of Business Administration, University of Missouri-St. Louis.His areas of research and teaching include international nance, nancialrisk management, and banking. He has published over 150 scholarly papersin various journals including the Journal of International Business Studies,Journal of Business Ethics, Journal of International Money and Finance,Journal of Banking and Finance, Journal of Risk and Insurance, Journalof Financial Research, Financial Management, Financial Review, Journal ofFutures Markets, and Review of Economics and Statistics, among others.He also published seven books, numerous book chapters and manyteaching cases.He is currently the editor of Chinese Economy, International Journal

of Business and Economics and International Review of Accounting,Banking and Finance. He has served on several other editorial boardsand has served as president in many Chinese organizations in St. Louis,including the Mid-West Chinese American Science and TechnologyAssociation, the Chinese Culture Day at the Botanical Garden in St.Louis, Organization of Chinese Americans, and St. Louis ChineseAssociation.Yiuman Tse is Peter G. Schick professor of nance at the University of

Missouri-St. Louis. He received his BS (Engineering) from the Universityof Hong Kong, MBA from SUNY-Binghamton, and Ph.D. fromLouisiana State University. His research interests are internationalinvestments and nancial markets. He has over 80 articles published inReview of Financial Studies, Journal of Financial and Quantitative Analysis,Journal of Econometrics, Management Science, Journal of Banking andFinance, and others. He has received many teaching awards from differentuniversities, including the 2006 Presidents Distinguished AchievementAwards for Teaching Excellence at The University of Texas at SanAntonio.

ABOUT THE VOLUME

This volume will contain a comprehensive analysis of internationalnancial markets through a series of essays from leading researchers in

the eld. The volume will not only examine the effects of changes in globaleconomies, technologies and governmental actions on trading, but also theimplications of global trading on the growth and development of domesticand international markets.

Hung-gay FungYiuman Tse

Volume Editors

About the Bookviii

LIST OF CONTRIBUTORS

Kam C. Chan Department of Finance, WesternKentucky University,Bowling Green, KY, USA

Leo H. Chan Utah Valley University, Orem,UT, USA

Hung-Gay Fung College of Business Administration,University of Missouri-St. Louis,St. Louis, MO, USA

Jullavut Kittiakarasakun Department of Finance, College ofBusiness, University of Texas at SanAntonio, San Antonio, TX, USA

Hei Wai Lee College of Business, University ofMichigan-Dearborn, Dearborn,MI, USA

Valeria Martinez Charles F. Dolan School of Business,Faireld University, Faireld,CT, USA

Chi M. Nguyen National Institute of Mining-Metallurgy Science and Technology,Hanoi, Vietnam

Gary A. Patterson University of South Florida St.Petersburg, St. Petersburg, FL, USA

Yiuman Tse Department of Finance, College ofBusiness Administration, Universityof Missouri-St. Louis, St. Louis,MO, USA

Derrick Tzau Rainier Investment Management,Seattle, WA, USA

Michael Williams College of Business and PublicAdministration, Governors StateUniversity, University Park, IL, USA

Yan Alice Xie College of Business, University ofMichigan-Dearborn, Dearborn,MI, USA

Jot Yau Albers School of Business andEconomics, Seattle University,Seattle, WA, USA

Gaiyan Zhang College of Business Administration,University of Missouri-St. Louis,St. Louis, MO, USA

Lin Zhao Department of Finance,Elon University, Elon, NC, USA

x List of Contributors

CONTENTS

ABOUT THE SERIES: FRONTIERS OF ECONOMICSAND GLOBALIZATION v

ABOUT THE EDITORS vii

LIST OF CONTRIBUTORS ix

PREFACE xv

1 THE INFORMATION VALUE OF EXCESSIVESPECULATIVE TRADES ON PRICE VOLATILITY INOIL FUTURES MARKETS 1Leo H. Chan, Chi M. Nguyen and Kam C. Chan

1 Introduction 12 Literature review 43 Methods 53.1 The rationale for the speculative ratio: A numerical example 53.2 Volatility modeling 73.3 Volatility measures 9

4 Data and empirical results 105 Conclusion 22References 22

2 THE LEADING ROLE OF THE CHINESE FUTURESIN THE WORLD COMMODITY FUTURESMARKETS 25Hung-Gay Fung, Yiuman Tse, Jot Yau and Lin Zhao

1 Introduction 252 Methodology 273 Data 304 Empirical results 405 Conclusion 47References 48

3 A GLOBAL CHINESE RENMINBI BOND MARKET:THE DIM SUM BOND MARKET 51Hung-Gay Fung, Derrick Tzau and Jot Yau

1 Introduction 512 Policies that encourage the global use of RMB 532.1 RMB bilateral local currency swap programs 532.2 Offshore RMB policies 55

3 Dim sum bond market 563.1 General characteristics 563.2 Landmark issues 583.3 Credit ratings 613.4 Issuers 613.5 Top 25 dim sum bond bookrunners/managers 66

4 Concluding remarks 66References 67

4 INVESTMENT IN THE GLOBAL REAL ESTATEMARKET 69Gary A. Patterson

1 Introduction 692 Impact of real estate in the nancial crisis of 20082009 703 Size of commercial real estate market and forecastsfor growth 74

4 General real estate market 765 Direct real estate market 806 Securitized real estate markets 817 Market efciency in real estate 838 Real estate associated with Islamic banking policies 859 Conclusion 87References 88

5 SOVEREIGN CREDIT DEFAULT SWAP 91Gaiyan Zhang

1 Sovereign credit default swap 911.1 History 931.2 Major market participants 941.3 Trading 951.4 Sovereign CDS of emerging economies 971.5 Sovereign CDS of the developed countries 971.6 Uses of sovereign CDS instrument 981.7 Historical trends of sovereign CDS spreads 991.8 Determinants of sovereign CDS spreads and yield curves 101

Contentsxii

2 Price discovery of sovereign CDS 1022.1 Risk issue 104

3 Conclusion 104References 105

6 MANAGING RISK IN SOVEREIGN BONDPORTFOLIOS: THE IMPACT OF SOVEREIGN ANDCALL RISKS ON DURATION 109Yan Alice Xie, Jot Yau and Hei Wai Lee

1 Introduction 1092 Empirical methodology 1113 Data 1124 Empirical results 1154.1 Impact of the sovereign and call risks on duration of bonds 1154.2 Impact of sovereign and call risks on duration of bonds

grouped by CDS prices 1195 Conclusion 123References 123

7 HEAVY-TAILED DISTRIBUTION OF COMMODITYPRICES AND THE EFFECTIVENESS OF VAR MODELS 125Jullavut Kittiakarasakun

1 Introduction 1252 The extreme value theory 1262.1 Theoretical background and hypothesis 1262.2 EVT estimation methods 128

3 Value-at-risk models 1293.1 VaR estimations 1293.2 Measuring out-of-sample performance 130

4 Data 1305 Results 1315.1 Preliminary statistics 1315.2 Tests for types of distribution 1315.3 Out-of-sample performance of VaR model 134

6 Conclusions 135References 136

8 THE IMPACT OF QUANTITATIVE EASING ON ASSETPRICE COMOVEMENT 139Michael Williams

1 Introduction 1392 Literature review 141

Contents xiii

2.1 Quantitative easing: the programs 1412.2 Quantitative easing: the outcomes 1422.3 Quantitative easing: channels of transmission 1432.4 Market comovement: contagion, ights, and decoupling 144

3 Methodology 1454 Results 1474.1 Descriptive statistics 1474.2 Unconditional comovement 1474.3 Conditional comovement: crisis only analysis 1514.4 Conditional comovement: crisis and intervention analysis 151

5 Explaining excess comovement 1586 Conclusion 159References 160

9 CARBON EMISSIONS TRADING: WHAT IT MEANSFOR INDIVIDUAL INVESTORS 165Valeria Martinez

1 Introduction 1652 Literature review and background on carbon emissions and ETNs 1672.1 ETN characteristics 168

3 Data 1684 Methods 1684.1 Volume 1694.2 Return and volatility correlations 1694.3 Investor sentiment effect 1724.4 Volatility analysis 1734.5 Price discovery 174

5 Summary of ndings 176References 177

Contentsxiv

PREFACE

In recent years, the globalization of nancial asset markets, particularlythe commodity markets, has become increasingly important and has led tocloser linkages among these markets. At the same time, emerging marketssuch as China have opened up their nancial markets for trading andforeign participation. In addition, new global investment instruments suchas sovereign credit default swaps, exchange traded funds, and bondmarkets have been created, enabling investors to ne tune their investmentportfolios to their likings. Financial investments have been furtherexpanded to include real asset investments such as real estate investments.In light of the large volatility of nancial and commodity markets,particularly after the nancial crisis, the need exists to better understandthe full range of global commodities and investment assets available andtheir inherent risks. This is necessary and important for global investors tomake proper decisions in assessing these investments in their assetallocations, and for policymakers who can provide sound policy guidanceto cope with the globalization of the nancial markets.This volume will contribute to the economic and nance research a fresh

perspective on international nancial markets and also the commoditymarkets by examining the endogenous and exogenous factors that affectinformation transmission and pricing relation in the spot and derivativesmarkets in the United States and internationally. This is especiallyimportant given that the forces behind trading in global nancial marketsappear to have changed in the wake of the 2008 nancial crisis. Thisvolume enables scholars, policymakers, and practitioners to betterunderstand the changes and dynamics of commodity and nancial assettrading following this nancial crisis. In addition, we provide someperspectives on new market instruments available to market participants.

1The Information Value of ExcessiveSpeculative Trades on Price Volatility inOil Futures Markets

Leo H. Chana, Chi M. Nguyenb and Kam C. Chanc

aUtah Valley University, Orem, Utah 84058, USA

E-mail address: [email protected] Institute of Mining-Metallurgy Science and Technology, Hanoi, Vietnam

E-mail address: [email protected] Kentucky University, Bowling Green, KY 42101, USA

E-mail address: [email protected]

AbstractIn this chapter, we apply the new measure of speculative activities(hereafter, named the speculative ratio) in Chan, Nguyen, and Chan(2013) to study the relationship between those activities and volatility inthe oil futures market. We document that the speculative ratio (tradingvolume divided by open interest) can isolate speculative elements fromtotal trading activities. Using the oil futures data and dividing the datainto two subperiods surrounding Hurricane Katrina, we nd an increasedspeculative trades in the post-Hurricane Katrina period. Our results showthat speculative activities create a more volatile oil futures market and theylower the information ow between volatility and speculative activitiesin the post-Hurricane Katrina period.

Keywords: Speculative activities, volatility, oil futures markets

1. Introduction

There is a voluminous literature on the effect of speculation on pricevolatility in the futures and spot markets (e.g., see Bessembinder & Seguin,1992, 1993; Chang, Cheng, & Pinegar, 1999; Fung & Patterson, 2001;Garcia, Leuthold, & Zapata, 1996; Mazouz & Bowe, 2006). There aretwo possible effects from increases in speculative activities in the spot and

Frontiers of Economics and Globalization r 2013 by Emerald Group Publishing Limited.Volume 13 ISSN: 1574-8715 All rights reservedDOI: 10.1108/S1574-8715(2013)0000013006

futures markets. First, if trades were executed by informed traders, theycould bring more information to the market and thus reduce the marketvolatility (Danthine, 1978). Second, if trades were executed by speculators,they could either lower the market volatility by being the counterpartyof hedgers (i.e., providing liquidity) or increase the market volatility byprimarily trading on noise rather than information (Black, 1986). Themain challenge is to identify which trades were executed by speculatorsand which trades were executed by hedgers (Johnson, 1960). Likewise,distinguishing which trades are by informed or uninformed traders is notfeasible.Bessembinder and Seguin (1993) suggest that open interest be used as a

proxy for market depth, which represents the market activities of hedgers,and that trading volume be used as a proxy for speculative activities. Theybelieve that, by incorporating open interest along with trading volume,one can shed insight into the price effects of futures market activitiesgenerated by informed and uninformed traders. Empirical studies thatfollowed (e.g., Bessembinder & Seguin, 1993; Foster, 1995; Fung &Patterson, 2001; Grima & Mougoue, 2002; Lautier & Riva, 2008;Mazouz & Bowe, 2006; Mougoue & Aggarwal, 2011; Najand & Yung,1991) show a signicant, positive feedback effect from trading volumeto price movement/volatility in the oil futures market and a mixed resultfor other futures markets (particularly in currency futures, where anincrease in trading volume tends to depress volatility).As suggested in Chan, Nguyen, and Chan (2013), the crude oil futures is

one of the most actively traded contracts and garners the most interestbecause of the potential signicant impact of oil price uctuation on theglobal economy. As oil is a primary source of energy, oil price shocks arebelieved to relate to global recessions and many short-term negative shocksthat have occurred regionally and internationally over the last fourdecades.1 Numerous studies have examined the causes and the effects of oilprice shocks.2 These studies, with the exception of Hamilton (2009), utilizeddata prior to a major turning point in the US oil market: Hurricane Katrinain August 2005. After Hurricane Katrina, the anecdotal evidence suggeststhat not only there was a short term disruption of oil supply that created asurge in oil prices, but also a surge in market activities in the oil futures

1 Aguiar-Conraria and Wen (2007) summarize the voluminous literature on the relation

between oil price shocks and economic activities. They examine the relation between the oil

prices hike and recession in the 1970s. After modeling the oil price with a multiplier

accelerator mechanism, they were able to explain the recession with oil price shocks.

Hamilton (2009) suggests that such as a shock has signicant adverse effect on consumption

spending and purchases of US automobiles. Hamilton concludes that oil prices contribute to

recessions in the United States.2 Hamilton (2009) summarize that some oil shocks were caused by physical disruptions of

supply while some were due to strong demand in a stagnating world production environment.

Leo H. Chan et al.2

market. The argument follows that the increase in oil futures marketactivities could be a result of increasing interest in commodity as analternative investment.3 From hedge funds to endowment funds and evenretail investors are all part of the increases in market activities in the oilfutures market (Gilbert, 2010; Masters, 2008). Since many of these newparticipants in the oil futures market have no real need for hedging, wecontend that they are primarily speculators.Our argument is that when speculators observe a potential negative

price movement in the near term, they may engage in short positions forthe near term contracts. Likewise, when hedgers observe potential positiveprice movement in the near term, they may want more long positions tobetter cover their underlying positions by increasing the percentage ofpositions covered. Thus, hedgers trading activities can also drive up theratio between long and short positions that can even further drive up thefutures price, and vice versa when potential negative movement isobserved. Furthermore, speculative trades that are not closed out in priortrading day due to poor prices would be carried over to the next tradingday and increases the open interest of the next day. Trading volume alsotends to be in a cycle that surrounds the maturity date of the nearbycontract. It also tends to spike up prior to the maturity date and subsideduring the non-maturity date period, due to the need for rolling over ofcontracts by hedgers. Therefore, trading volume and open interest couldinclude potential trades from both hedgers and speculators. In brief, usingopen interest and trading volume separately, such as Bessembinder andSeguin (1993), to represent hedging and speculative activity intensitycontains noise.The objective of this chapter is to examine the impact of increased

speculative activities on the oil futures market volatility. More specically,we investigate if the increases in speculative trading improve informationows in the oil futures market. Unlike past studies, such as Bessembinderand Seguin (1992, 1993), that use open interest and trading volumeseparately to represent hedging and speculative activity intensity, thisstudy apply the new measure in Chan et al. (2013) to examine the researchissue. The new measure is speculative ratio, which is dened as tradingvolume divided by open interest. Intuitively, a higher speculative ratiobetween trading volume and open interest would imply higher speculativeactivities relative to hedging activities or vice versa. As illustrated laterin Section 3.1, by using the speculative ratio, one can gain a betterunderstanding of how increases in speculative activities interact with price

3 A GAO report on February 25, 2008 suggested that there had been many new hedge funds

to engage in commodity futures trading and they attracted investments from institutional

investors. Another striking example, investigated by the CFTC, is that the oil trading rm

Vitol Groups control of over 57 million barrels of crude oil, with a market value of more than

$8 billion, or 3 times the daily usage in the United States (Davis, 2008).

The Information Value of Excessive Speculative Trades 3

volatility. The research herein also uses more efcient measures ofvolatility to improve the robustness of the results.We demonstrate the application of the speculative ratio in Chan et al.

(2013) by using oil futures data in the pre- and post-Hurricane Katrinain 2005. Our ndings suggest that the speculative ratio increases in thepost-Katrina era, which is consistent with the anecdotal evidence ofthe increased in speculative activities in the oil futures market in the sameperiod. Increases in speculative activities have created a more volatilemarket and lower the information ow between volatility and speculativeactivities in the post-Katrina era.The remainder of the chapter is organized as follows: Section 2 provides

a literature review. The methodology is set out in Section 3. Datadescription and analysis are demonstrated in Section 4. Finally, weconclude our study with a summary and discussion in Section 5.

2. Literature review

The theory of speculation and hedging in commodity futures marketshas been studied systematically since Keynes (1930) and Hawtrey (1940).Later on, Danthine (1978) theorizes that hedgers are assumed to be lesssophisticated than speculators, and they mainly want to reduce their pricerisk. Thus, hedgers are willing to take a futures price that is lower than theprevailing spot price (normal backwardation). In this scenario, the hedgersare either producers or holders of the underlying asset that must be sold atsome future dates, thus always in a short position. The speculators, on theother hand, are assumed to be armed with more information about pricemovements in the future and willing to take on the price risk, for a givenpremium, and will always be in a long position. Working (1953a, 1953b)suggests that the line between a hedger and a speculator might not beas clear cut. A speculator who sees opportunities for arbitrage between spotmarket and futures market might hold inventories of the underlying assetand go short on a futures position. Johnson (1960) suggests that expectationof relative and absolute price changes in the future can affect the positionsof the speculators. In a nutshell, it is difcult to identify who are the hedgersand who are the speculators from a single trade. The activities of noisetraders in the market make it impossible for us to understand, with anydegree of precision, how the nancial market works (Black, 1986). Tokic(2011) nds that during a period of bubble in the oil futures market, truehedgers might actually condition their trades on oil price movement ratherthan information, thus contributing to even more extreme price movementin the oil futures market. The ndings in Tokic (2011) suggest that hedgerscan also contribute to futures price volatility in the futures market.Absence a clear way to separate stabilizing and destabilizing speculative

trades, Bessembinder and Seguin (1992, 1993) suggest using open interest

Leo H. Chan et al.4

as a proxy for market depth in order to isolate the speculative componentin trading volumes. Bessembinder and Seguin (1993) nd strong positiverelationship between trading volume and volatility in eight commodityfutures markets (including crude oil). Najand and Yung (1991) nd asimilar result in the T-bond futures market by using a generalizedautoregressive conditional heteroskedasticity (GARCH) model thatcaptures the time-varying nature of volatility.Karpoff (1987) set out testing the hypothesis of an asymmetric relation-

ship between volume and price changes. Foster (1995) uses GARCH andgeneral methods of moments (GMM) models and nds that both currentand lag volumes can explain the price variability in crude oil futures.The results in Foster (1995) imply that price and volatility could be drivenby the same factors, presumably information. Though the results inFoster (1995) provide empirical support for the mixture of distributionhypothesis suggested by Clark (1973) and Harris (1987), the contributionof lag volume to price volatility suggests a certain degree of marketinefciency. Such inefciency may be a result of traders conditioning theircurrent trades on previous trading volume as a market sentiment, a similarconclusion obtained by Lautier and Riva (2008).The literature typically examines the separate impact of speculation

(using trading volume) and hedging (using open interest) activities, and theresults generally conclude that at times speculative activities contribute tohigher futures price volatility. However, recent studies, such as Lautier andRiva (2008), Reitz and Slopek (2008), and Tokic (2011), have shown thathedging activities can also contribute to higher futures market volatility.By using the speculative ratio in Chan et al. (2013) as a new measure forspeculation, our research can incorporate both trading volume and openinterest together and relate them to the oil futures price volatility.

3. Methods

3.1. The rationale for the speculative ratio: A numerical example

To see the rationale behind the speculative ratio in Chan et al. (2013), weillustrate with a hypothetical example. Consider a normal trading day inwhich there are 100,000 contracts in open interest. Since open interestrepresents the number of contracts outstanding, the number of contractstraded the next day exceeding the open interest would represent tradingactivities beyond the normal contracts changing hands.4 Suppose that

4 Theoretically, the speculative ratio could be as high as 1 in the absence of speculators if all

the hedgers decided to turnover their position in any given day. But that is a highly unlikely

scenario.


the number of contracts traded on that day is 70,000 and that 50,000 ofthose were executed by hedgers and 20,000 contracts were executed bytrades from speculators. The speculative ratio, according to the denitionof trading volume divided by the open interests, is 0.7. In this case, the truehedging portion of the trading activities is 0.5 and the speculative portionis 0.2. In any given trading day, no one knows the portion of hedging andspeculative activities among the futures contracts traded. Participants withlarge positions are encouraged to self-report their positions on a weeklybasis. However, self-reporting of positions is on a volunteer basis and it isonly being done once a week. Thus, when using daily data, we have no wayof identifying trades.Suppose that the next day the open interest is again 100,000 contracts,

that is, no new position is established, and the trading volume is 120,000contracts. The speculative ratio is 1.2 on this trading day. Since whichtrades were executed by hedgers or which trades were executed byspeculators is unknown, the only way to approximate the trades executedby the hedgers is to use the same portion of 0.5, or 50,000 contracts, as theprevious day. It is possible to justify this relatively stable portion of thetrades attributed to hedgers because they will have a price to execute inmind and are willing to wait for the right price before they make theirtrades. Consequently, the speculative portion of the speculative ratio isnow 0.7, a sharp increase from 0.2 from the previous day. Speculatorstrade based on perceived price movements and do not want to holdinventory overnight unless the price is extremely unfavorable.We now suppose that 20,000 contracts of the 70,000 contracts traded

today are new positions. Again, whether those contracts are created bynew hedgers or speculators is unknown in the market. However, these newpositions will show up in the open interest of the next trading day sinceopen interest represents net positions outstanding. Therefore, the openinterests for the next trading day will include both true hedgers andspeculators newly established positions, which will add up to 120,000contracts. For the sake of simplicity, assume that the newly establishedcontracts are all from speculators. The speculators would want to executethose carryover contracts from the previous trading day because they donot want to hold the inventory for another day and be subject to anotherdays price risk.5 If successful in executing those contracts, the tradingvolume of the following day would increase by 20,000 contracts regardless

5 There are evidences that well-funded speculators and hedge funds were able to further

increase the trading volumes by buying in the spot market when price goes up. During the

sharp declines of the oil price in late 2008, some hedge funds were buying oil futures taking

delivery then storing the commodity due to low tanker costs. They then sold them when the

price went up later. The CFTC had investigated and led civil suits against cases of market

manipulations by hedge funds (Davis, 2008; Kruss, 2011).

Leo H. Chan et al.6

of what the normal activity would be. Furthermore, if we assume a normallevel of trading activities for the true hedgers, 50,000 contracts would beexecuted on the next day, the addition of the 20,000 contracts wouldincrease the number of contracts traded to 70,000. Adding to the normallevel of speculative activities, the total trading volume would be 90,000contracts. The speculative ratio under this scenario is 0.75. Through thisexample, it is clear to see that the increases in trading volume as a result ofspeculators not being able to close out their position from the previousday, or from the increases of newly established position, can be capturedby the speculative ratio. In fact, any speculative elements in tradingactivities should increase the speculative ratio.

3.2. Volatility modeling

Nonlinear dynamics in crude oil futures price volatility and unidirectionaleffect from volume to price movement/volatility is well documented(e.g., Foster, 1995; Moosa & Silvapulle, 2000). To determine if thespeculative ratio of trading volume over open interest of oil futurescontracts affects the volatility of the oil futures prices, we investigate theconditional correlation between the speculative ratio and a volatilitymeasure, as well as the linearity in their conditional variances. The range-based volatility measures (to be outlined in details in the next section) areused as proxies for the volatility, Volat and the speculative ratio is denotedas Ratit. The dynamics of these two variables are modeled as:

Volat m1 u1t (1)

Ratit m2 u2;t (2)

of which u1t and u2t are decomposed into

u1t 1th1t

p(3)

u2t 2th2t

p(4)

where the standardized innovations (e1t), (e2t)BN (0,1) and the conditionalvariances h1t and h2t of the volatility measure and the ratio are strictlypositive.To capture the effect of the speculative ratio Ratit on the volatility Volat,

we follow Chan et al. (2013) to use the dynamic conditional correlation(DCC) model of Engle (2002) of which the conditional correlation r12;t ofVolat and Ratit is time varying as

r12;t Et1u1tu2t

Et1u21tEt1u22tq Et11t2t (5)


As it can be seen, this correlation is basically the conditional covarianceof the innovations e1t and e2t that, of course, depends on the dynamicsof the conditional variances h1t and h2t through Equations (3) and (4),respectively.We use two different models to specify the dynamics of h1t and h2t.

Model 1 allows the disturbances (u1t) and (u2t) to follow GARCH(1,1),that is, DCC-GARCH(1,1) of Engle (2002), where the conditionalvariances evolve over time as

h1t c1 a1u21;t1 b1h1;t1 (6)

h2t c2 a2u22;t1 b2h2;t1 (7)

In Model 1, the impact of the ratio on the volatility is simply channeledonly through the correlation, thereby leaving the conditional variance ofthe volatility intact. One may, however, wonder if there is any additionaldirect effect of the speculative ratio on the volatility. As a high ratio oftrading volume over open interest indicates an increasing speculation of oilfutures contracts that, in turn, can increase price volatility, the speculativeratio may have a nontrivial signicant effect on the conditional variance ofthe volatility. Thus, we propose Model 2 that modies Equation (6) asfollows

logh1t c1 a121;t1 b1 logh1;t1 g logRatit1 (8)where the sign and the signicance of the coefcient g is the main interest.g measures how last trading days speculative trades interact with the nexttrading days volatility. A higher value for g implies a higher degree ofinformation ow from the previous days speculative trades or vice versa.Note that by taking the logarithm of h1t as formulated in Model 2, we canavoid the complication caused by including the ratio Ratit1 into theregression of the conditional variance h1t and relax a number ofrestrictions on the coefcients to only one, that is, jb1jo1.Hereinafter, we denote t 1t; 2t0 as the vector of standardized

innovations, and Qt Et1t0t as the conditional covariance matrix of(et) and S Et0t as the unconditional covariance matrix. The dynamicof Qt can be expressed, according to the DCC model of Engle (2002), inthe following specication

Qt S 110 A B A t10t1 B Qt1 (9)

where A a11

a21

a12

a22

!, B

b11

b21

b12

b22

!are symmetric 2 2 coefcient

matrices, 1 is a vector of 1s, and is the Hadamard product of twocompatible matrices which is the element-by-element multiplication.

Leo H. Chan et al.8

To guarantee the positive deniteness of the covariance matrix Qt. Dingand Engle (2001) show that at least one of the three matrices A, B, and

110 A B should be positive denite, and the rest should be positivesemi-denite. If we let q11;t; q12;t; q22;t be the elements of Qt, that is,

Qt q11;t

q12;t

q12;t

q22;t

! then the conditional correlation of the volatility

measure and the ratio will be

r12;t q12;tq11;tq22;t

p (10)

The conditional correlation set out in Equation (10) is a measure ofinformation ow between the speculative ratio and volatility. If the valueof r12,t is larger (smaller), there is more (less) information owing fromvolatility to speculative trades respectively. Informed traders should beable to distill information from noise. A small value for the conditionalcorrelation (r12,t) means that there is more noise in the market, introducedby uninformed traders.

3.3. Volatility measures

This chapter utilizes more efcient volatilitymeasures for futures price (eachconstructed under the assumption of Geometric Brownian motion) asproposed by Parkinson (1980), Garman and Klass (1980), and Rogers andSatchell (1991). Range-based measures for nancial data are found to bemore effective than utilizing closing price data alone, as shown in Corwinand Schultz (2012). Details of each volatility measure and the statisticalmodel are described as follows. Consider a trading day period, denoted by t.Let Ot, Ct, Ht, and Lt denote, respectively, the opening, closing, high, andlow futures prices at day t. The simplest measure of volatility is the range,Rt,dened as the difference between the high and low prices (in logarithms)

Rt lnHt lnLt lnHt=Lt (11)Gallant, Hsu, and Tauchen (1999) and Alizadeh, Brandt, and Diebold

(2002) both nd the range to be a better, that is, information richer, proxyfor the true volatility.Assuming an underlying Geometric Brownian motion with no drift for

the futures price, the joint density function of Ht and Lt can be derived.Based upon this density function, Parkinson (1980) proposes the followingvolatility measure

VP;t 0:361R2t 0:361lnHt lnLt2 0:361lnHt=Lt2 (12)As a true volatility proxy, it has been demonstrated that VP,t could be as

much as 8.5 times more efcient than log-squared returns. To incorporate


the opening and closing prices, Garman and Klass (1980) suggest thefollowing measure

VGK;t 1

2lnHt lnLt2 2ln2 1lnCt lnOt2 (13)

Both measures are unbiased when the sample data are continuouslyobserved with VGK,t being more efcient than VP,t. In reality, the sampledata are discretely observed and thus both measures incur downwardbiases. The size of the bias depends upon the observation frequency.Correction methods are available but they require certain parameters thatare not empirically available (Yang & Zhang, 2000).For most nancial data, it is likely that the drift term is not zero. In this

case, neither the Parkinson nor the GarmanKlass estimator is the mostefcient estimator. Yang and Zhang (2000) demonstrate this property intheir simulation studies. Rogers and Satchell (1991, 1994) propose analternative measure that is drift independent

VRS;t lnHt lnOtlnHt lnCt lnLt lnOtlnLt lnCt

(14)

When applied to the actual data, this measure is also subject to adownward bias problem. This chapter chooses to report only the resultsfrom the simple log range volatility and RogersSatchell measure that ismore appropriate for nancial data.

4. Data and empirical results

We obtain the NYMEX crude oil futures data that span from September1991 to September of 2011. There are a total of 5,024 daily observations.During the period, there were many potential events, such as theOperation Desert Storm in 1991, the Iraq War in 2003, Hurricane Katrinain 2005, and the 2008 nancial crisis. Of these four major events, the threethat could cause potential major supply shortages are the OperationDesert Storm, the Iraq War, and Hurricane Katrina.To demonstrate the application of the speculative ratio, we chose

Hurricane Katrina in 2005 given the ample anecdotal evidence of changesin oil futures price volatility in the pre- and post-Katrina periods. Potentialmajor supply shortages should prompt potential hedgers and informedspeculators to increase their participation in the oil futures market. Weobserve in Figure 1 that there were exactly two days in which thespeculative ratio was higher than 1 prior to Hurricane Katrina in 2005(a span of over 14 years of data before Hurricane Katrina). During thebuildup to Hurricane Katrina, there was a spike in trading volume alongwith a sharp increase in open interest as well. As a result, the speculativeratio was below 1 surrounding the time of Hurricane Katrina.

Leo H. Chan et al.10

In contrast, there have been more than 50 days in which the speculativeratio has been higher than 1 since Hurricane Katrina. In fact, the majorityof the trading days in which the speculative ratio is higher than 1 have beenaround the run up of the oil price to above $146 a barrel on July 14, 2008,and the subsequent collapse of the oil price to $38 on December 24, 2008.From the descriptive statistics in Table 1, the second and third Panels,notice that there is a vast difference between the speculative ratio beforeand after Hurricane Katrina in 2005. Not only the maximum andminimum values are both higher after Hurricane Katrina, but also theaverage is signicantly higher as well. There is a signicant change in thebehavior of the speculative ratio after Hurricane Katrina. Moreimportantly, this change in behavior seems to be the driving force behindthe sharp increases and decreases of oil futures price around the time of thenancial crisis in 2008.Masters (2008) points out that increases in commodity futures price lead

to increases in participation by speculators. In Figure 1, the left columndisplays the log range, RogersSatchell measure and the speculativeratio. All three of them show a sharp spike in value before the oil pricehit an all time high. In addition, the right column shows the estimatedkernel densities of these three series which clearly present their asymmetricnon-normality and longer right tails. We notice that the bulk of the valuesafter the Hurricane Katrina, for example, the medians and the means, lie

0.2 40

30

20

10

0

40

2.5

2

1.5

1

0.5

00 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

0 0.05 0.1 0.15

30

20

10

00.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

0.15

0.1

0.05

0

1

0.5

0

2.5

2

1.5

1

0.5

0

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

Fig. 1. The volatility measures (the log range volatility and the RogersSatchell volatility measure), and the ratio of trading volume and openinginterests. The rst row shows the series of the range and its kernel density.The second row shows the series of the RogersSatchell and its kerneldensity. Finally, the last row shows the series of the ratio and its density. Thered vertical line marks the date of Hurricane Katrina, August 23, 2005.


Table1.

Descriptive

statistics

Descriptive

(1)

(2)

(3)

Thewholesample

Before

thehurricane

After

thehurricane

Statistics

Range

volatility

Rogers

Satchell

volatility

Speculative

ratio

Range

volatility

Rogers

Satchell

volatility

Speculative

ratio

Range

volatility

Rogers

Satchell

volatility

Speculative

ratio

Min

0.0023

0.0041

0.0168

0.0023

0.0041

0.0168

0.0079

0.0013

0.0621

Max

0.1921

1.1775

2.3380

0.1042

0.4866

1.2861

0.1921

1.1775

2.3380

Firstquartile

0.0160

0.0094

0.3574

0.0144

0.0075

0.3259

0.0211

0.0163

0.4513

Median

0.0226

0.0180

0.4706

0.0203

0.0145

0.4333

0.0279

0.0292

0.5664

Thirdquartile

0.0312

0.0346

0.5998

0.0281

0.0273

0.5492

0.0390

0.0511

0.7230

Mean

0.0258

0.0310

0.4931

0.0227

0.0225

0.4433

0.0330

0.0506

0.6069

Std

0.0150

0.0491

0.1984

0.0119

0.0273

0.1620

0.0186

0.0753

0.2254

Skew

ness

2.27

7.57

1.12

1.61

5.17

0.48

2.25

5.64

1.26

Kurtosis

12.55

105.67

6.47

7.69

54.55

3.34

10.81

54.56

6.62

Jarque-Bera

23363

2252600

3563

4709

402180

150

5146

176890

1232

ADFt-test*

60.35

62.00

37.25

51.76

49.28

35.68

32.75

32.23

17.06

Wepresentdescriptivestatisticsfortherangevolatility,RogersSatchellvolatility,andspeculativeratio(tradingvolumedivided

byopen

interest).TheADF

testincludes

aconstantandatimetrend.Weuse

AIC

withmaximum

10lags(i.e.,2weeksofworkingdays)to

choose

theoptimalnumber

oflagsforADF

t-tests.ThebreakpointofthedataisthedateofHurricaneKatrina,August23,2005.Subsample1includesdatafromtherstdayofthewholesampleto

the

daybefore

theHurricaneKatrinahappened.Subsample2includes

therestofthedata.


on the right side of the sample distribution. Thus, Figure 1 gives graphicalevidence of a regime switching in the oil futures market with the milestoneof the Hurricane Katrina. Although the price of oil has risen graduallysince the start of the Iraq War, trading volumes and increases inspeculative trading did not persist until after Hurricane Katrina. Thus, wedemonstrate the application of the proposed speculative ratio using the fullsample, a pre- and a post-Hurricane Katrina subsamples.For more precise measures of the relationship between the volatility and

the speculative ratio, we turn to our models. Tables 2 and 3 report theresults from Model 1 with log range volatility measure. Recall that Model1 considers only the contemporary effect of the current speculative ratio onthe current volatility through their conditional correlations. In Table 2,Column 2 reports the result from the entire sample, whereas Columns 3and 4 report the results from pre- and post-Hurricane Katrina in 2005,respectively. The coefcients of the conditional variance equations statedin Panels 1 and 2 are higher during the post-Katrina period for bothvolatility and speculative ratio. In other words, the two series contain morenoise during post-Katrina than pre-Katrina.In Table 2 Panel 3, all elements of the conditional correlation matrices,

A and B, and pre-Hurricane Katrina are signicantly different from zeros.

Table 2. Estimated coefcients of the conditional variances and thecorrelations of log range volatility and the speculative ratio (Model 1)

Coefcients (2) (3) (4)

Full sample Before the hurricane After the hurricane

For the conditional variance of the volatility measure

m1 0.0236(0.0002) 0.0218(0.0002) 0.0275(0.0003)c1 4.2E-06(4.5E-07) 1.1E-05(1.1E-06) 3.7E-06(9.3E-07)

a1 0.0582(0.0034) 0.0662(0.0056) 0.0747(0.0079)b1 0.9165(0.0046) 0.8587(0.0107) 0.9080(0.0094)

For the conditional variance of the ratio

m2 0.4673(0.0025) 0.4385(0.0029) 0.5550(0.0052)c2 0.0095(0.0008) 0.0069(0.0011) 0.0173(0.0014)

a2 0.3871(0.0246) 0.1906(0.0231) 0.6159(0.0534)b2 0.3571(0.0335) 0.5459(0.0535) 0.0843(0.0417)

For the conditional correlation of the volatility measure and the ratio

a11 0.1876(2.5E-05) 0.1773(0.0001) 0.1911(0.0157)

b11 0.4145(0.0001) 0.5823(1.7E-05) 0.0507(0.0019)

a22 0.1329(2.0E-05) 0.1013(0.0002) 0.0701(0.0304)

b22 0.6030(0.0001) 6.0E-07(4.6E-10) 0.0352(0.0281)*

a12 0.1210(1.9E-05) 0.0972(5.9E-08) 0.1157(0.0078)

b12 0.6092(5.3E-05) 0.0829(2.2E-05) 0.3485(0.0167)

This table presents the results of the estimated coefcients of Model 1 in Equations (6)

and (7). The value in parenthesis is the standard error of the corresponding parameter.*Denote statistically not signicant at 10%.


In contrast, it is not the case for those elements in the post-Katrina period.Thus, the contemporary relation between the volatility and the speculativeratio seems to be weakened after Hurricane Katrina. This conclusion isfurther fortied by the statistics of the conditional correlations reportedin Table 3 because we observe decreases in the median and the mean,together with an increase in the standard deviations of these correlations inpost-Katrina. This implies that the information between these two series inpost-Katrina is not as strong as that in pre-Katrina.Figures 2 and 3 illustrate our ndings, displaying the dynamics of the

conditional correlations over time, as well as the basic features of theirdistributions (the box plot with the rst quartile, median and thirdquartile, and the estimated density). These gures clearly show that thestatistical properties of the condition correlations for the pre- and post-Katrina periods are different.Tables 4 and 5 report the results of Model 2 for the log range volatility

measure. The results are qualitatively similar to Model 1 in Table 3. As wemention in the methodology section of this chapter, Model 2 accounts fornot only the contemporary impact of the speculation on the price volatilityof oil futures contracts, but also the yesterday effects. The coefcient ofinterest in Model 2 is g. As g measures the magnitude that one percentagechange in yesterday speculative ratio can affect the change in todaysconditional variance, a larger value of g would mean previous tradingdays speculative activity provides more information for the volatility ofcurrent trading day. The result shows there is a sharp decline in the valueof g in post-Katrina. In other words, the speculative ratio provides lessinformation in post-Katrina. For the contemporary impact, the condi-tional correlations reported in Table 5 tell a similar story: a wider rangeand a lower mean of r12,t during the post-Katrina period. Again, Figures 4and 5 show the differences of the statistical properties of the conditionalcorrelation between the two subperiods.Since nancial time series are likely time varying, the RogersSatchell

measure provides a more robust result. Tables 6 and 7 report the results of

Table 3. Conditional correlation of the log range volatility and thespeculative ratio (Model 1)

r12,t Full sample Before the hurricane After the hurricane

Min 0.1048 0 0.1618Max 0.7571 0.6817 0.7795

Median 0.5021 0.5325 0.3298

Mean 0.4869 0.5153 0.3318

Standard deviation 0.1120 0.0751 0.1027

This table presents the descriptive statistics of the condition correlation between the log range

volatility and the speculative ratio based on Equations (6) and (7).


0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

0.1

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

0.1

0 1000 2000 3000 4000 5000 1 0.2 0 0.2 0.4 0.6 0.8 1

4.5

4

3.5

3

2.5

2

1.5

1

0.5

0

Fig. 2. The conditional correlations of the range and the ratio for thewhole sample from September 3, 1991 to September 20, 2011 (Model 1).The rst graph presents the estimated conditional correlations. The secondgraph is the box plot of these correlations. And the last graph is its estimated

kernel density.

0.6

0.5

0.4

0.3

0.2

0.1

00 500 1000 1500 2000 2500 3000 3500

6

5

4

3

2

1

00.2 0.60.40.20 0.8

7

6

5

4

3

2

1

00.60.40.20 0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

1

0.6

0.8

0.4

0.2

0

0.20 500 1000 1500

0.6

0.8

0.4

0.2

0

0.21

Fig. 3. The conditional correlations of the range and the ratio before andafter the Hurricane Katrina on August 23, 2005 (Model 1). The rst rowand the second row present the estimated correlations series, its box plot andits kernel density before and after the date of the Hurricane Katrina,respectively. The red mark indicates outliers. For the pre Katrina period, theoutliers are mostly in the lower end of the value. For the post-Katrinaperiod, the outliers are about evenly distributed on the upper end and the

lower end of the value.


Model 1. Note that the coefcients for Equations (1), (2), (6), and (7) tell asimilar story as the coefcients obtained by using the log range volatilitymeasure reported in Tables 2 and 3. The conditional correlations of theRogersSatchell measure and the speculative ratio have a sharper declinein mean and wider increases in ranges than those that are obtained bythe log range volatility measure. In fact, the conditional correlation isnot statistically different from the unconditional correlation during

Table 4. Estimated coefcients of the conditional variances and thecorrelations of range and ratio (Model 2)

Coefcients Full sample Before the hurricane After the hurricane


m1 0.0286(0.0002) 0.0269(0.0002) 0.0332(0.0003)c1 0.2300(0.0083) 2.7033(0.1603) 0.5575(0.0371)a1 0.1135(0.0047) 0.2496(0.0227) 0.3223(0.0210)b1 0.9711(0.0011) 0.6366(0.0211) 0.9416(0.0043)g 0.0342(0.0028) 0.1995(0.0161) 0.0318(0.0102)For the conditional variance of the ratio

m2 0.4673(0.0025) 0.4385(0.0029) 0.5550(0.0052)c2 0.0095(0.0008) 0.0069(0.0011) 0.0173(0.0014)

a2 0.3871(0.0246) 0.1906(0.0231) 0.6159(0.0534)b2 0.3571(0.0335) 0.5459(0.0535) 0.0843(0.0417)


a11 4.7E-08(4.2E-10) 0.1650(8.8E-07) 1.4E-06(3.3E-13)

b11 1(0.0006) 0.2426(5.6E-07) 0.9956(4.4E-17)

a22 0.0426(0.0011) 0.2297(2.5E-08) 0.0012(4.0E-20)

b22 0.9574(3.1E-07) 0.7703(2.1E-09) 0.9988(2.0E-15)

a12 4.2E-05(0.0001) 2.9E-28(4.8E-17) 3.1E-07(6.4E-09)b12 0.0255(5.9E-05) 0.4995(6.3E-08) 0.0010(9.71E-17)This table presents the results of the estimated coefcients of Model 1 in Equation (8). The

value in parenthesis is the standard error of the corresponding parameter. *Denote

statistically not signicant at 10%.

Table 5. Conditional correlation of the range and the ratio (Model 2)


Min 0 0 0

Max 0.4025 1.0920 0.1656

Median 0.2597 0.3094 0.1588

Mean 0.2611 0.3224 0.1584


P{jrjW1} 0 0.0003 0This table presents the descriptive statistics of the condition correlation between the log range

volatility and the speculative ratio based on Equation (8).


0.25

0.3

0.35

0.4

0.2

0.15

0.1

0.05

0

0.25

0.3

0.35

0.4

0.2

0.15

0.1

0.05

0

12

10

8

6

4

2

00 0.1 0.2 0.3 0.4

010

0020

0030

0040

0050

00 1

Fig. 4. The conditional correlations of the range and the ratio for the wholesample from Sept 3, 1991 to Sept 20, 2011 (Model 2). The rst graphpresents the estimated conditional correlations. The second graph is the boxplot of these correlations. And the last graph is its estimated kernel density.

0.8

1

0.6

0.4

0.2

00 500 1000 1500 2000 2500 3000 3500

100

80

60

40

20

00.160.140.120.1 0.18

6

5

4

3

2

1

00.60.40.20 0.8 1

0.8

1

0.6

0.4

0.2

0

1

0.17

0.145

0.15

0.155

0.16

0.165

0.17

0.145

0.15

0.155

0.16

0.165

200 400 600 800 100012001500 1

Fig. 5. The conditional correlations of the range and the ratio before andafter the Hurricane Katrina on August 23, 2005 (Model 2). The rst rowand the second row present the estimated correlations series, its box plot andits kernel density before and after the date of the Hurricane Katrina,respectively. After taking into account the previous trading days speculativeratio, the distribution of the conditional correlation is vastly different in thepost-Katrina period. Whereas the statistical properties in the pre Katrinaperiod is similar to the entire data sample, the statistical properties in the

post-Katrina period is completely different that the complete sample.


the post-Katrina period. Again, the result indicates less information owbetween these two series after the Hurricane Katrina.Meanwhile, taking into account the previous trading days speculative

activities, reported in Tables 8 and 9, the conclusion is not signicantlydifferent from those obtained by using the log range volatility measure.

Table 6. Estimated coefcients of the conditional variances and thecorrelations of RogersSatchell volatility measure and the ratio (Model 1)



m1 0.0210(0.0003) 0.0192(0.0003) 0.0313(0.0005)c1 0.0002(3.9E-06) 0.0003(7.1E-06) 0.0002(1.1E-05)

a1 0.2644(0.0075) 0.3107(0.0113) 0.3436(0.0187)b1 0.6571(0.0076) 0.3835(0.0155) 0.6564(0.0145)


m2 0.4673(0.0025) 0.4385(0.0029) 0.5550(0.0052)c2 0.0095(0.0008) 0.0069(0.0011) 0.0173(0.0014)

a2 0.3871(0.0246) 0.1906(0.0231) 0.6159(0.0534)b2 0.3571(0.0335) 0.5459(0.0535) 0.0843(0.0417)


a11 0.0094(1.4E-05) 0.0103(0.0072) 0.3632(4.8E+07)*

b11 0.0329(7.4E-08) 0.9897(0.0123) 0.5809(1.4E+08)*

a22 0.4287(3.3E-08) 0.0269(0.0049) 0.0000(6.4E+08)*

b22 9.0E-06(1.3E-08) 0.7579(0.0022) 1.0000(8.8E+08)*

a12 0.0638(3.0E-06) 0.0167(0.0360)* 0.1961(1.1E+08)*

b12 0.0489(4.9E-08) 0.9291(0.0172) 0.6963(4.8E+08)*

This table presents the results of the estimated coefcients of Model 1 in Equations (6) and (7)

using RogersSatchell volatility measure. The value in parenthesis is the standard error of the

corresponding parameter. *Denote statistically not signicant at 10%.

Table 7. The conditional correlations of RogersSatchell volatility measureand the ratio (Model 1)


Min 0 0 0.75783Max 0.7923 0.7275 1.6795

Median 0.4104 0.4169 0.2927

Mean 0.3967 0.4407 0.2820


P{jrjW1} 0 0 0.0013This table presents the descriptive statistics of the condition correlation between the Rogers

Satchell volatility and the speculative ratio based on Equations (6) and (7).


In general, the previous trading days speculative activities contribute farless information to the innovation of volatility in current trading in thepost-Katrina period. The conditional correlation also points to a lowerdegree of information ow between these two series. Our overall ndingsare robust to different range-based volatility measure. Figures 69 exhibitsimilar patterns as Figures 25.

Table 8. The conditional correlations of the RogersSatchell volatilitymeasure and the ratio (Model 2)



m1 0.0402(0.0008) 0.0354(0.0007) 0.0558(0.0013)c1 0.3288(0.0109) 0.3370(0.0174) 0.5891(0.0329)a1 0.2894(0.0085) 0.1383(0.0139) 0.5825(0.0361)b1 0.8911(0.0022) 0.8810(0.0038) 0.8663(0.0059)g 0.3361(0.0072) 0.3670(0.0112) 0.1523(0.0170)


m2 0.4673(0.0025) 0.4385(0.0029) 0.5550(0.0052)c2 0.0095(0.0008) 0.0069(0.0011) 0.0173(0.0014)

a2 0.3871(0.0246) 0.1906(0.0231) 0.6159(0.0534)b2 0.3571(0.0335) 0.5459(0.0535) 0.0843(0.0417)


a11 4.8E-01(5.5E-11) 0.0499(9.5E-56) 7.40E-20(8.5675)*b11 4.6E-02(1.9E-10) 1.7E-17(4.0E-39) 0(127.57)

*

a22 0.0001(1.4E-13) 0.0521(5.2E-56) 0.0999(67.955)*

b22 0.8223(1.9E-08) 0.0065(9.2E-57) 1.4E-17(72.423)*

a12 9.2E-05(7.3E-09) 0.0510(2.9E-57) 0(178.54)*

b12 0.0010(1.8E-11) 0.0010(3.8E-53) 8.3E-04(72.144)*

This table presents the results of the estimated coefcients of Model 1 in Equation (8) using

RogersSatchell volatility measure. The value in parenthesis is the standard error of the

corresponding parameter. *Denote statistically not signicant at 10%.

Table 9. The descriptive statistics of the conditional correlations of theRogersSatchell volatility measure and the ratio (Model 2)


Min 0 0 0.0445

Max 0.2235 0.3880 0.0860

Median 0.2131 0.1692 0.0839

Mean 0.2084 0.1713 0.0822


This table presents the descriptive statistics of the condition correlation between the Rogers

Satchell volatility and the speculative ratio based on Equation (8).


0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

9

8

7

6

5

3

4

2

1

00.5 0 0.5 10 1000 2000 3000 4000 5000 1

Fig. 6. The conditional correlations of the RogersSatchell volatilitymeasure and the ratio for the whole sample from September 3, 1991 toSeptember 20, 2011 (Model 1). The rst graph presents the estimatedconditional correlations. The second graph is the box plot of these

correlations. And the last graph is its estimated kernel density.

0.6

0.8

0.4

0.2

0

0.6

0.8

0.4

0.2

00

500

1000

1500

2000

2500

3000

3500

2

1.5

1

0.5

01 21.510.500.5

5

4

3

2

1

00.60.40.20 0.81

2

0.5

1

0

0.5

1

1.5

2

0.5

1

0

0.5

1

1.5

0 600 1000 1500 1

Fig. 7. The conditional correlations of the RogersSatchell volatilitymeasure and the ratio before and after the Hurricane Katrina on August 23,2005 (Model 1). The rst graph presents the estimated conditionalcorrelations. The second graph is the box plot of these correlations. Andthe last graph is its estimated kernel density. These gures show a cleardifference in statistical properties of the conditional correlations between

these two subperiods.


0.25

0.2

0.15

0.1

0.05

0

0.25

0.2

0.15

0.1

0.05

0

50

45

40

35

30

25

15

20

10

5

00 0.05 0.1 0.15 0.2 0.250 1000 2000 3000 4000 5000 1

Fig. 8. The conditional correlations of the RogersSatchell volatilitymeasure and the ratio for the whole sample from September 3, 1991 toSeptember 20, 2011 (Model 2). The rst graph presents the estimatedconditional correlations. The second graph is the box plot of these

correlations. And the last graph is its estimated kernel density.

0.3

0.4

0.2

0.1

0

0.6

0.8

0.4

0.2

00

500

1000

1500

2000

2500

3000

3500

200

150

100

50

00.090.080.04 0.05 0.06 0.07

35

20

25

30

15

10

5

00.30.20.10 0.41

0.09

0.04

0.05

0.06

0.07

0.08

0.09

0.04

0.05

0.06

0.07

0.08

0 600 1000 1500 1

Fig. 9. The conditional correlations of the RogersSatchell volatilitymeasure and the ratio before and after the Hurricane Katrina on August 23,2005 (Model 2). The rst graph presents the estimated conditionalcorrelations. The second graph is the box plot of these correlations. Andthe last graph is its estimated kernel density. Again, the statistical propertiesof the conditional correlations between these two subperiods are vastly

different.


5. Conclusion

The role of speculative activities in the futures market is of great interest toboth regulators and participants. In this chapter, we utilize more efcientvolatility measures and apply the new measure of speculative activities(the speculative ratio) in Chan et al. (2013) to isolate speculative elementsfrom total trading activities to investigate the impact of increasedspeculative activities on the information ow in the oil futures market.We demonstrate the application of the new speculative ratio. The resultsshow that the speculative activities create a more volatile market, and theylower the information ow between volatility and speculative activities inpost-Katrina. Overall, the speculative ratio works well in describing theincrease in speculative activities in the post-Katrina period.

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2The Leading Role of the Chinese Futures inthe World Commodity Futures Markets

Hung-Gay Funga, Yiuman Tseb, Jot Yauc and Lin Zhaod

aCollege of Business Administration & Center for International Studies,

University of Missouri-St. Louis, One University Blvd, St. Louis, MO 63121, USA

E-mail address: [email protected] of Finance, College of Business Administration, University of Missouri St. Louis,

One University Blvd, St. Louis, MO 63121, USA

E-mail address: [email protected] School of Business and Economics, Seattle University, 901 12th Avenue, Seattle,

WA 98122, USA

E-mail address: [email protected] of Finance, Martha and Spencer Love School of Business, Elon University,

Elon, NC 27244, USA

E-mail address: [email protected]

AbstractThis study explores the price linkage between the Chinese commodityfutures market and other dominant futures markets, and examinesthe forces behind the price linkages. The contribution by the tradinghour innovations in the United States (or United Kingdom) market to theovernight price changes in the Chinese market is larger in scale thanthe contribution by the daytime information from the Chinese market to theovernight returns of the corresponding US (or UK) market. Several futureshave signicant interactions of the domestic and foreign factors in the pricelinkages while the Chinese domestic factors explain better the global marketprice linkage in some futures (aluminum, gold, and corn), demonstratingthe leading role of the Chinese futures markets in these world markets.

Keywords: Price linkage, price discovery, Chinese commodity futures

1. Introduction

The futures markets in China have experienced rapid growth in recent years.There are currently four futures exchanges in China: Zhengzhou Commodity

Frontiers of Economics and Globalization r 2013 by Emerald Group Publishing Limited.Volume 13 ISSN: 1574-8715 All rights reservedDOI: 10.1108/S1574-8715(2013)0000013007

Exchange (ZCE), Dalian Commodity Exchange (DCE), Shanghai Com-modity Exchange (SHFE), and China Financial Futures Exchange(CFFEX). In terms of the number of contracts traded, the three of thefour commodity futures exchanges of China are now among the topderivatives exchanges in the world, while futures on these exchanges are alsoamong the most heavily traded metal and agricultural futures in the world(see Acworth, 2011/2012). Different from the well-developed futuresmarkets, futures trading in China faces market impediments such as restri-ctions on currency and capital ows. Given the voracious trading intensityof Chinese futures and relatively restricted market conditions, an investi-gation into the price linkage between the Chinese market and major worldfutures markets has important implications to policymakers and investors.The purpose of this chapter is to explore the linkages between price

settings in the Chinese commodity futures market and the forces fromother dominant futures markets in the world. In particular, we areinterested in guring out whether information generated in the Chinesecommodity market is effectively incorporated into the correspondingforeign market with similar futures, and vice versa. An understanding ofthe inter-market price linkage is important because it sheds light on therole of one market in another market. Particularly, we examine what rolethe futures markets in China play in the global futures markets. Inaddition, we investigate which underlying determinant factors drive thedetected price linkages.Daily futures data were collected and aggregated into weekly frequency

for the empirical analysis. Data of 14 Chinese commodity futures, includingaluminum, copper, zinc, gold, early long-grain nonglutinous rice, whitesugar, hard white wheat, strong gluten wheat, cotton, No. 1 soybean, No. 2soybean, soybean meal, soybean oil, and corn were collected from theearliest date available in the database to the end of 2011. Each Chinesefutures contract was then paired with a comparable futures contractwith the same or similar underlying product from the most active marketoutside China. After matching, the foreign futures are found to be eitherfrom the US or UK market. Located in different time zones, the trading(nontrading) hours in the Chinese market are contemporaneous with thenontrading (trading) hours in the US or UK market overseas. The inter-market linkage is thus investigated by constructing a variable whichmeasures the extent to which information released during the tradinghours of the Chinese (or foreign) market is incorporated into the price ofthe foreign (or Chinese) market when the cross market is closed. Weprovide summary statistics of the cross-market price contribution factorand further examine how the cross-market linkage is affected by tradingactivities (i.e., trading volume and open interest) and price volatility. Theimpact of the recent global nancial crisis is also considered in the analysis.We contribute to the literature in several ways. First, we employ an

extensive sample that covers the majority of the futures contracts traded in

Hung-Gay Fung et al.26

the Chinese market, enabling us to describe an overall picture of the link-ages between the Chinese market and other major markets in the world.Second, we use a sample period that covers the recent global nancialcrisis, enabling us to explore the potential impact of extreme marketturmoil on the inter-market linkages of the Chinese futures market.Third, we explore the underlying factors that drive the dynamics of theinter-market linkage.Several results from this study are noteworthy. First, the contribution

by the trading hour innovations in the US (or UK) market to the overnightprice changes in the Chinese market is larger in scale than the contributionby the daytime information from the Chinese market to the overnightreturns of the corresponding US (or UK) market. Thus, the US (or UK)market has taken a relatively more important role than the Chinesemarket in the transmission of the contemporaneous cross-marketinformation. For many futures contracts in our sample, the cross-marketprice contribution factor is signicantly affected by trading volume, openinterest, and price volatility. Specically, the increase (or decrease) in therelative trading volume (or open interest) are associated with the increasein the price contribution factor. The signicant impact from the relativevolatility, however, does not follow a consistent direction. Overall, thesignicant relationship between the ratio variables and cross-market pricelinkages is primarily driven by forces from the Chinese market, especiallyfor the open interest and trading volume variables. Furthermore, thecontribution made by the daytime return in the Chinese market to theovernight return in the US (or UK) market tends to be reduced duringthe recent global nancial crisis for some futures studied.The rest of the chapter is organized as follows. Section 2 shows the

methodology. Section 3 describes the data and summary statistics. Section 4presents and discusses empirical results. The nal section concludes thechapter.

2. Methodology

In order to quantify the linkage between the Chinese and the foreigncommodity futures markets, we use the weighted price contribution(WPC) variable proposed by Agarwal, Liu, and Rhee (2007) to measurethe contribution of the daytime return of a futures contract traded on theChinese exchange (C) to the overnight return of a comparable futurescontract traded in the foreign market (F), and vice versa. WPC in thisstudy is dened as

WPCFtoC XTt1

PN;t1 PTt1 PN;t1

! PD;t

PN;t1

The Leading Role of the Chinese Futures 27

where DPN, t+1 is the overnight return of the futures contract traded inChina (C) on day t+1 (i.e., the market close of day t to the market openof day t+1), and DPD,t is the daytime return of the futures contract tradedin the US or UK market (F) on day t. Thus, WPCFtoC measures thecontribution of the daytime return of a futures contract on a foreignexchange to the overnight return of a futures contract in China over aspecic period of time, namely, one week in this study (i.e., T one week).In the reverse direction, the contribution of the daytime return of a futurescontract in Chinas market to the overnight return of a futures contract inthe UK or US market is measured by

WPCCtoF XTt1

PN;t PTt1 PN;t

! PD;t

PN;t

where DPN,t is the overnight return of the futures contract traded on theUS or UK exchange on day t (i.e., the market close of day t1 to themarket open of day t), and DPD,t is the daytime return of futures in Chinaon day t. It is noted that the overnight return from the Chinese (or foreign)market and the daytime return from the foreign (or Chinese) market arecontemporaneous, as dened in the WPC equations.Determinant factors that may have some impact on the cross-market

price contribution variable are explored with the following regressions

WPCFtoC;t a1 b1VOMF;t

VOMC;t c1

OIF;t

OIC;t d1

VOLF;t

VOLC;t h1CRISISt

(1a)

WPCCtoF;t a1 b1VOMC;t

VOMF;t c1

OIC;t

OIF;t d1

VOLC;t

VOLF;t h1CRISISt

(1b)

WPCFtoC;t a1 b1VOMF;t c1OIF;t d1VOLF;t e1VOMC;t f 1OIC;t g1VOLC;t h1CRISISt 2a

WPCCtoF;t a2 b2VOMF;t c2OIF;t d2VOLF;t e2VOMC;t f 2OIC;t g2VOLC;t h2CRISISt

(2b)

where VOMF,t (VOMC,t) is the log of trading volume of futures in theforeign (or Chinese) market, OIF,t (OIC,t) represents the log of openinterest, and VOLF,t (VOLC,t) measures the volatility. All of thesevariables are at the weekly frequency. CRISISt is a (1,0) dummy variablewhich equals to one during the recent nancial crisis period and zerootherwise. The volatility variable is estimated following the method inBessembinder and Seguin (1993).


In regression (1), we use the trading activity ratios (i.e., VOMF,t/VOMC,t,OIF,t/OIC,t) and the volatility ratio (i.e., VOLF,t/VOLC,t) to capture theimpact of relative changes in trading activities and price volatility in theChinese and foreign futures markets on the cross-market price contribu-tions. The estimated coefcients from regression (1), therefore, reect thecombined effect from both markets (i.e., China and the United States, orChina and the United Kingdom). By using the trading activity and pricevolatility variables from both markets instead of their ratios, regression(2) provides supplemental information to the results obtained fromregression (1). For example, if the combined effect in regression (1) isfound to be signicant, which market contributes more can be inferredfrom regression (2). Both regressions are estimated with the NeweyWestheteroskedasticity- and autocorrelation-consistent covariance matrixapproach.Trading volume and open interest are trading activity measures closely

linked with price changes in the futures markets. Kyle (1985) shows thatthe information is gradually incorporated into prices by the trades ofinformed traders who try to exploit their private information for prots,while the trades of noise traders provide liquidity and induce informedtraders to release private information over time. Admati and Peiderer(1988) in a model that includes discretionary liquidity traders suggestthat informed traders with homogeneous information would competewith discretionary liquidity traders, resulting in trade clustering. That is,liquidity traders like to trade together with informed traders because theirwelfare is improved when more informed traders enter the competition.The positive correlation between trading volume and the absolute value ofprice changes is found in both the equity and futures markets, and suchempirical phenomenon can be theoretically explained by the sequentialinformation arrival model (e.g., Copeland, 1976) or the mixture ofdistributions hypothesis (e.g., Clark, 1973; Harris, 1983; Tauchen &Pitts, 1983).On the other hand, open interest reects hedging activity by uninformed

traders and thus has different informational implications from tradingvolume, as suggested by Bessembinder and Seguin (1993). The modeldeveloped in Chen, Cuny, and Haugen (1995) predicts that when the pricevolatility of the underlying stock increases, investors will reduce theirequity exposure by increasing their short positions in the futures contract.The open interest, as a result, will increase as the perceived risk increases inthe spot market. Thus, the ndings in Chen et al.s study suggest thatincreasing (decreasing) open interest reects the higher (lower) hedgingdemand of investors in the spot market. Open interest is also viewed as aproxy for the divergence of traders opinions (Bessembinder, Chan, &Seguin, 1996). The existence of a long-run relationship between futuresprices and open interest of storable commodities is documented in Yang,Bessler, and Fung (2004).

The Leading Role of the Chinese Futures 29

3. Data

Daily futures price and trading activity data were collected fromCommodity Systems, Inc. (CSI). Commodity futures data from theChinese markets, including aluminum, copper, zinc, gold, early long-grainnonglutinous rice, white sugar, hard white wheat, strong gluten wheat,cotton, No. 1 soybean, No. 2 soybean, soybean meal, soybean oil, andcorn were obtained. For each of these Chinese futures contracts, a compa-rable futures contract with the same or similar underlying product fromthe most active market outside China would be identied. For example,the Chinese gold futures market was matched with the gold futures tradedat Chicago Mercantile Exchange (CME), while the aluminum futuresin China was matched with the aluminum futures traded at the LondonMetal Exchange (LME). For the 14 futures in the sample, the most activeforeign markets were located either in the United States or the UnitedKingdom. Table 1 provides a detailed description of these futurescontracts.Since the beginning date of data obtained from the CSI varies among

different futures, the sample start date for each futures was set to theearliest date available from the database; all sample futures have the sameend date, that is, the year end of 2011. Since futures traded in differentmarkets used different price quotations, we standardized the pricequotation unit of each futures pair, ensuring that the price of the Chinesefutures contract is comparable to that of the foreign. Similarly, tradingvolume and open interest data were resized. Chinese futures contracts aretraded electronically between 9:30 a.m. and 3:00 p.m. (Beijing time), with atwo-hour break from 11:30 a.m. to 1:30 p.m. For foreign markets withmultiple trading sessions, prices from oor trading were used.Table 2 presents the match-up of each Chinese futures contract with its

foreign counterpart. For example, the aluminum, copper, and zinc futurestraded at the Shanghai Futures Exchange (SHFE) were paired with thecontracts traded at the LME. The open-outcry trading session at LME isring trading with traders sitting around a circle. The ring trading sessionstarts at 11:40 a.m. (London time), with each futures contract traded at ave-minute period and ends at 5:00 p.m. The trading hours of aluminum,copper, and zinc futures fall between 11:55 a.m. and 5:00 p.m. The whitesugar futures traded on the Zhengzhou Commodity Exchange (CZCE)was paired with a comparable white sugar futures traded on theEURONEXT, which has a trading session between 8:45 a.m. and 5:30 p.m.(London time). All the rest commodity futures (i.e., gold, rice, wheat,cotton, soybean, soybean meal, soybean oil, corn) on the Chinese futuresexchanges were paired with futures traded on the US exchanges (i.e., CMEand ICEFuturesUS),with tradinghours from8:20 a.m. to 2:15 p.m., EasternStandard Time (EST).


Table1.

Futurescontract

specications

Contract(CSIticker)

Exchange

Contractsize

Ticksize

Tradinghoursa

Deliverablegrade

Aluminum

(SAF)

ShanghaiFutures

Exchange(SHFE)

5tons

10Yuan/ton

9:00a.m.11:30a.m.,

1:30p.m.3:00p.m.

Standard

goods:Aluminum

ingot,

GB/T1196-93,AL99.70,main

ingredientsZ99.7%.

Substitutions:TheLME

RegisteredBrand,P1020A.

Aluminum

(MHA6)

LondonMetalExchange

(LME)

25tons

2.5USD

per

contract

11:5512:00,12:55

13:00,13:2014:45,

15:1515:20,15:55

16:00,16:1517:00

Primary

aluminum

withimpurities

nogreaterthanin

theregistered

designationP1020AintheNorth

AmericanandInternational

RegistrationRecord

entitled

InternationalDesignationsand

Chem

icalCompositionLimits

international financial markets

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