Intraday volatility in the bond, foreign exchange, and stock index futures markets

INTRADAY VOLATILITY IN THE

BOND, FOREIGN EXCHANGE,AND STOCK INDEX

FUTURES MARKETS

VALERIA MARTINEZYIUMAN TSE*

Intraday volatility for the Eurodollar, the Euro/dollar foreign exchange rate, and theE-mini S&P 500 futures contracts traded on a continuous 23-hour schedule onthe Chicago Mercantile Exchange Globex electronic platform is studied.Volatility transmission in a single market across different regions is mainlyexplained by intraregion volatility (heat waves); interregion volatility (meteorshowers) plays a secondary role. The joint impact of liquidity variables such asvolume and open interest on volatility is also analyzed. Volume tends to increasevolatility, but open interest does not affect it. The results are explained by the typeof trading venue. Unlike floor-based trading systems, in electronic markets openinterest does not seem to provide additional information on market liquidity andits relation to volatility beyond any information contributed by volume. © 2008Wiley Periodicals, Inc. Jrl Fut Mark 28:313–334, 2008

INTRODUCTION

Return and volatility relation between financial assets traded in different geographic regions has prompted some important international investment

*Correspondence author, College of Business, 501 West Durango Blvd., San Antonio, Texas 78207; e-mail: yiuman.tse@utsa.edu

Received April 2007; Accepted August 2007

■ Valeria Martinez is an Assistant Professor of Finance at Fairfield University, Fairfield, Connecticut.

■ Yiuman Tse is a Professor of Finance and U.S. Global Investors, Inc. Research Fellow atUniversity of Texas at San Antonio, San Antonio, Texas.

314 Martinez and Tse

Journal of Futures Markets DOI: 10.1002/fut

research. Many financial data series such as exchange rates and stock returnsexhibit volatility clustering, persistence, and different patterns of volatilitytransmission, as reported by Engle, Ito, and Lin (1990), Hamao, Masulis, andNg (1990), Ito, Engle, and Lin (1992), Lin, Engle, and Ito (1994), Andersenand Bollerslev (1998), and Melvin and Melvin (2003). Engle et al. proposedtwo popular volatility transmission explanations related to the release of publicinformation, namely the heat wave and meteor shower hypotheses. The heatwave hypothesis is that shocks in market A affect next day volatility only in mar-ket A, but will not impact the next day’s trading session in market B. The meteorshower hypothesis is that intradaily volatility spills over from one geographicregion to another, much as a meteor shower occurs over many places. Shocksin market B will have an impact on the next day’s trading session in market Band also on the next day’s trading session in market A. Many researchers alsotried to explain volatility transmission based on private information and hetero-geneous beliefs. However, the bottom line is that a consistent explanation forthe observed volatility patterns in financial markets is yet to be found.

Does volatility come from the same geographic region or from otherregions? What events cause sharp spikes in volatility? How does the type of trad-ing venue affect volatility? How do variables such as volume and open interestaffect volatility? These are common questions that the authors attempt toexplain further. Intraday volatility transmission in the bond, foreign exchange(FX), and stock markets is examined using the Eurodollar, Euro/dollar exchangerates, and E-mini S&P 500 futures contracts electronically traded on theChicago Mercantile Exchange (CME) Globex platform for the period of January2004 through June 2007.

Value is added to the literature by using intraday price data on futures con-tracts traded continuously around the clock on the same electronic exchangecharacterized by fast order execution, high market transparency, and low trans-action costs. In all three markets there is evidence of both interregional (meteorshower) and intraregional (heat wave) volatility effects. The latter effects aremore pronounced than the former. Significant asymmetric volatility effects (i.e.,the negative relationship between volatility and returns) are also found in theindex futures market, but not in the Euro/dollar FX and Eurodollar markets.

The analysis is strengthened by studying the joint impact of activity vari-ables such as volume and open interest on volatility when futures are traded ona screen-based system instead of a floor-based system. Although volume tendsto increase volatility, open interest has no impact. These results are explainedby the type of trading venue. Although open interest may help explain volatilityin a floor-based system in previous studies, it does not explain it in electronicmarkets. In this case, the nature of liquidity creation differs considerably.Bloomfield, O’Hara, and Saar (2005) showed that in electronic markets, the

Intraday Volatility 315

strategies of informed and uninformed traders play a predominant role inexplaining the market liquidity.

The authors’ results should help policy makers better understand the causesand consequences of volatility transmission in markets around the world, and theliquidity factors and market dynamics that influence it.

LITERATURE REVIEW

It is common knowledge that many financial data series such as exchange ratesand stock returns exhibit volatility clustering and different patterns of volatilitytransmission. A variety of authors had proposed explanations for this behavior.

Engle et al. (1990) focused on volatility transmission of the yen/dollar FXmarket. They proposed two explanations: the heat wave and meteor showerhypotheses. The heat wave hypothesis posits that volatility impact affects onlythat particular geographic region. In other words, shocks in the Japanese FXmarket affect only volatility in Japan but will not impact the next day’s tradingsession in the U.S. FX market. The meteor shower hypothesis posits thatintradaily volatility spills over from one geographic region to another. For exam-ple, news released in the United States has an impact on the U.S. FX market aswell as on the next day’s trading session in the Japanese FX market. Engle et al.conclude that the yen/dollar FX market shows characteristics consistent with the meteor shower hypothesis. Ito et al. (1992) provided further evidenceof the meteor shower hypothesis for the yen/dollar FX market.

Since then Melvin and Melvin (2003) had analyzed volatility in thedeutschemark/dollar as well as the yen/dollar FX spot markets. They found thatintraregion volatility spillovers or heat waves are economically more significantthan meteor showers or interregion volatility spillovers. They concluded that theintraregion volatility effect is three to four times the size of the interregioneffect. Fleming and Lopez (1999) examined volatility spillovers in the U.S.treasury market. They found meteor showers in Tokyo and London but heatwaves in New York. They also showed that lagged trading volume significantlyaffects treasury yield volatility for the overseas markets.

Many other studies examined volatility spillovers in international stock mar-kets. Hamao et al. (1990) reported significant spillovers from New York toLondon and Tokyo, but not the reverse, for the pre-October 1987 period.Adjusting for nonsynchronous trading and allowing for time-varying volatility;however, Lin et al. (1994) found no significant causality on returns and volatilitiesin either direction for the New York and Tokyo stock markets. These results areconsistent with the heat wave hypothesis. Recent research also showed that thePacific-Basin stock markets (see Ng, 2000) and European markets (Fratzscher,2002) are substantially influenced by the volatility from their own regions.

An important aspect of volatility is its relation to liquidity variables such asvolume and open interest. The number of contracts in the futures market atany time is endogenously determined; hence, studying the joint impact of vol-ume and open interest on volatility may provide additional insight on volatility.One of the few pieces of research that took advantage of the depth of informa-tion that can be extracted from these variables was Bessembinder and Seguin(1993). They analyzed the relation between volatility, volume, and open inter-est for eight agricultural, currency, financial, and metal futures contracts tradedin an open-outcry setting. They found that volume is positively related tovolatility, and open interest can also help explain it even considering volumeinformation.

Fleming (1997) analyzed the volume–volatility relation of the round-the-clock trading of U.S. treasury securities. He showed that the positive relationbetween volume and volatility found in other financial markets such as thestock or FX markets also holds for the U.S. treasury market. See also Flemingand Remolona (1999). Daigler and Wiley (1999) reported that the positive volume–volatility relation in floor-traded futures markets is driven by the gen-eral public, who are uninformed traders without accurate information on orderflow. Informed traders who observe order flow decrease volatility.

Variables beyond open interest and volume characterize liquidity in a mar-ket. In Bloomfield et al.’s (2005) analysis of liquidity in electronic markets,informed traders play a central role in liquidity creation. Liquidity is endoge-nously created and evolves throughout the trading session. Although initiallyuninformed investors provide most of the market’s liquidity, as trading pro-gresses and informed investors profit from their information, they graduallyswitch from the role of speculators to the role of dealers, submitting more limitthan market orders, and providing market liquidity. In electronic markets,informed investors not only take but also provide liquidity to the market, evenin the presence of information asymmetry.

Three contributions are made to the literature. The authors’ first contribu-tion is to use futures data instead of spot data in the analysis of volatility trans-mission in a single market across different regions. The use of a commonfutures trading platform such as Globex allows a more direct cross-marketcomparison than using the three spot markets. Recent studies in differentfinancial markets also found that futures prices are more important than orsimilar to spot prices in the price discovery process. Rosenberg and Traub(2006) reported that currency futures prices contribute more information thanspot prices to exchange rate determination. Tse, Bandyopadhyay, and Shen(2006) showed that the Dow Jones Industry Average index futures lead theindex in price discovery. In the U.S. treasury market, Mizrach and Neely (2006)found that the spot and futures prices provide comparable information shares.

The authors’ second contribution is the analysis of intraday volatilityacross different markets for a continuous 23-hour, second by second, tradingschedule. Earlier work was based on the analysis of daily data (Lin et al.,1994), whereas recent studies used data that are more finely sampled, but theydid not examine round-the-clock trading (Andersen, Bollerslev, Diebold, &Vega, 2005).

The authors’ third contribution is the use of electronic trade data comingfrom the same exchange for all markets analyzed. This helps the authors tominimize any microstructure effects between different types of trading venues,such as electronic vs. open outcry or differing regulation among exchanges. Italso shows that the impact of volume and open interest on volatility variesdepending on whether the futures contracts are traded in a floor-based or elec-tronic system.

MARKET STRUCTURE AND DATA

In 1992, the CME introduced the CME Globex, the first global electronicfutures trading platform. Initially the CME Globex offered only after-hourstrading, but it soon evolved into an electronic trading venue that offers contin-uous trading five days a week.

The authors use CME time and sales intraday data from January 2004through June 2007 for the Eurodollar, Euro/dollar FX, and E-mini S&P 500futures contracts traded on the CME Globex. The authors also use the dailyvolume and open interest data from Commodity Systems Incorporated.

Eurodollars are U.S. dollars deposited in banks outside the United States.These future contracts represent the London interbank offer rate for a $1 milliondeposit in a foreign bank for a three-month period. Eurodollar futures are themost actively traded interest rate futures contracts worldwide. AlthoughEurodollar futures contracts are traded in the CME pit and on the Globex elec-tronic system, 85% of the contracts are electronically traded.

The E-mini S&P 500 futures contract started trading on the CME in1997, following introduction of the S&P 500 regular-size contract in the early1980s. The mini contract is one-fifth the size of the regular-size S&P 500futures contract with a value of $50 times the index. The authors choose the E-mini S&P 500 futures contract over the regular-size contract for tworeasons. First, the authors are interested in the volatility transmission analysisof screen-based contracts, and only the E-mini contract is electronically trad-ed. Second, as Hasbrouck (2003) noted, the E-mini contract provides most ofthe price discovery contribution for the S&P 500 index.

Soon after inception of the E-mini S&P 500, the Euro/dollar FX futurescontract began trading on the CME. This futures contract represents

125,000 Euros. As in the case of the Eurodollar contract, the Euro/dollar FXfutures are traded both electronically and in the pits, but electronically tradedcontracts represent roughly 95% of the total volume.

Both Eurodollar and Euro/dollar FX futures trade on Globex from Mondaythrough Friday for 23 hours daily, with a one-hour break from 4:00 to 5:00 P.M.Central Standard Time. The E-mini S&P 500 futures are also traded daily foralmost 24 hours, with breaks from 3:15 to 3:30 P.M. and 4:30 to 5:00 P.M.

Table I gives descriptive statistics for intraday minute-by-minute data fromJanuary 2004 to June 2007 for the three futures contracts. Note that volatility,as described by average absolute returns, remains fairly constant over the sam-ple period for the Eurodollar futures contract. Volatility for the Euro/dollar FXdrops by 42% and for the E-mini S&P 500 futures contracts by 12%. The E-mini S&P 500 shows the most volume and transactions, surpassing annualEurodollar volume (transactions) by no less than three times (ten times), andannual Euro/dollar FX volume by at least five times (60%).

The greatest increase in volume during the sample period is for theEurodollar futures contract. From 2004 to 2007, volume rose 166% for thiscontract. Volume for the Euro/dollar FX contract increased by 139%, and 86%for the E-mini S&P 500 contract. In terms of number of transactions, the great-est increase was in the Euro/dollar FX contract (19%) followed by the

TABLE I

Descriptive Statistics

Minute by minute

2004 2005 2006 June 2007

Mean Std. dev. Mean Std. dev. Mean Std. dev. Mean Std. dev.

Absolute returns (%)

Eurodollar 0.0006 0.0022 0.0007 0.0020 0.0006 0.0019 0.0007 0.0020Euro/dollar FX 0.0112 0.0147 0.0100 0.0174 0.0084 0.0112 0.0065 0.0085E-mini S&P 500 0.0130 0.0192 0.0109 0.0171 0.0113 0.0171 0.0115 0.0172

Transactions

Volume

Note. The table shows minute-by-minute summary statistics for a three and a half year sample period for the Eurodollar, theEuro/dollar FX, and the E-mini S&P 500 futures contract traded on the CME Globex platform on a continuous daily 23-hour schedule.FX, foreign exchange; CME, Chicago Mercantile Exchange.

Eurodollar (18%) and the E-mini S&P 500 futures contracts (17%). There is ahealthy number of transactions and volume for every minute in the authors’sample period; hence, one-minute intervals can be used instead of longer timeperiods to analyze the return volatility of the three futures contracts.

RESULTS

The authors’ analysis is described in three ways: as daily volatility, transmissionof volatility, and the relation between volatility and open interest and volume.

Daily Volatility

Daily volatility plots are analyzed for trading activity of the bond, FX, and stock markets using the average intraday minute-by-minute absolute returnsfor the authors’ sample period. In Panel A of Figure 1 the volatility plot for theEuro market is shown. It can be seen that this contract is least volatile whenthe Asian markets are open (23:00–8:00 Greenwich Mean Time (GMT)). AsAsian markets close, volatility rises through the opening of European markets,and continues to increase as American markets join in the trading session.

Most European markets begin trading sessions between 8:00 and 8:30 GMT.During this period it can be seen that volatility approximately doubles over thetime only the Asian markets are open. U.S. futures markets start trading at13:20 GMT. Soon after, a huge volatility spike attributable to the release ofU.S. macroeconomic news is observed at 13:30 GMT (8:30 Eastern StandardTime(EST)). A second smaller spike occurs at exactly 15:00 GMT, correspon-ding to the 10:00 EST release of U.S. macroeconomic data. Main Americanmarket activity occurs until 21:00 GMT (16:00 EST).

Panel B shows the volatility pattern for the Euro/dollar exchange rate.Once again significant spikes in volatility are observed at 13:30 and 15:00 GMT.Excluding these two spikes, volatility is highest during the period when bothAmerican and European markets are open, which makes sense, given that theEuro/dollar exchange rate is being analyzed. Once all other markets are closed,volatility declines throughout the U.S. trading session.

Panel C depicts the volatility plot for the E-mini S&P 500 contract. Thegraph indicates that volatility is lowest throughout the Asian market tradingsession. It increases considerably at the European markets’ opening, and againat the American markets’ opening. Clear spikes can been seen at 13:30 and15:00 GMT, again corresponding to the release of U.S. macroeconomic infor-mation. Approximately half an hour before the closing of American markets(20:30 GMT), volatility drops until the end of the CME equity market’s tradingsession (21:15 GMT).

Although the pattern is more pronounced for the E-mini S&P 500 futurescontract, all three volatility plots show three distinct U-shapes indicating theopen and close of the Asian, European, and American markets. This is evidencethat traders in each region prefer to trade in their own time zones and explainshigher market activity at the beginning and the end of the regional trading ses-sion, signaling portfolio rebalancing.

Tse (1999) pointed out that segmentation does not imply market inefficien-cy. Even if investors in a particular market react rapidly and efficiently to infor-mation transferred from other similar markets, they might still prefer to trade intheir home markets.

Panel A

Eurodollar interest rate

Panel B

€/$ FX

sPanel C

E-mini S&P 500

FIGURE 1Volatility plots for the bond, FX, and stock index futures markets. Volatility plots for average absolutereturns obtained from one-minute intervals from January 2004 through June 2007 for the Eurodollar

interest rate, the Euro/dollar FX, and the E-mini S&P 500 futures contracts traded on the CME Globex platform.

Volatility Transmission Analysis

Following Melvin and Melvin (2003) the authors analyze volatility transmissionfor the bond, FX, and stock markets using one-minute intervals. The authors’ dataare divided into five subperiods representing the active times of different marketsaround the world. Each regional period is based on the trading activity pattern inthe 23-hour period the CME futures markets are open. The authors’ sample peri-od is longer than that of Melvin and Melvin, December 1993 to April 1995.

The authors determine the schedules for market trading around the worldusing the market activity patterns for different world regions observed in Figure 1.Trading hours for each region are defined as follows: Asia from 23:00 to 5:59GMT, Europe from 8:00 to 13:19 GMT, and America from 16:30 to 21:59 GMT.From 6:00 to 7:59 GMT, both the Asian and European markets are open, andfrom 13:20 to 16:29 GMT, both European and American markets are open.

Daily volatility is estimated as the sum of minute-by-minute squaredintradaily returns (Andersen, Bollerslev, Diebold, & Labys, 2001). This volatilitydefinition lets a simpler vector autoregressive (VAR) model to be used insteadof a GARCH model to analyze volatility transmission across and within regions.As Melvin and Melvin (2003) noted, one advantage of a VAR model is thatresults will be less model dependent.

Because markets in each region are open for different lengths of time, dailyvolatility measures per region are standardized, dividing daily volatility by thenumber of one-minute intervals in which each region is considered to be active.

To describe a region’s volatility, the authors use its volatility as a depend-ent variable and lagged volatilities of the same region and other regions asindependent variables. For example, to analyze volatility in the Asian markets(left-hand-side variable), which is the first market to open on any given day,the right-hand-side variables will be the previous day’s volatility for theAsia–Europe, Europe, Europe–America, American, and Asian markets. Toexplain American markets’ volatility, however, the right-hand-side variablesrepresenting lagged volatilities will be same-day volatilities in the Asian,Asia–Europe, Europe, and Europe–America markets, because these marketstrade before the American markets. In this case, the American market laggedvolatility is the previous day’s volatility. Besides lagged volatilities, a dummyvariable is included in each equation to account for Mondays and holidays,which may present different volatility behavior.

This all results in a five-equation system, where the left-hand-side variablesare the volatilities in each of the five world regions, and the right-hand-sidevariables are lagged volatilities for each market. As lagged values will not be thesame for each equation, volatility transmission cannot be analyzed using a stan-dard VAR model. The authors instead use a seemingly unrelated regression

(SUR) model, which allows different lagged independent variables for eachequation in the system.

The model can be represented as follows:

where is a vector of regional volatility measures, B and C are coefficientmatrices, xt is a dummy vector for Mondays and holidays, and et is the vector ofinnovations.

To enable the reader a better understanding of the equation system, theequations that describe volatility transmission for the Asian and American mar-kets using a two-lag SUR model are written out. The lag length is determinedby the Akaike information criterion and Schwartz information criterion.

For the Asian market, volatility transmission can be described as

For the American market:

Table II presents the results of the SUR system volatility analysis for theEurodollar futures contract. Wald tests for intraregion volatility spillovers along the table’s diagonal show persistent intraregion or heat wave volatility effects for theAsia–Europe, Europe, and Europe–America regions, but not for the Asia orAmerica region. With regard to interregion spillovers, volatility from the Asianmarket spills over to all other regions; Asia–Europe region volatility spills over toEurope; Europe region volatility spills over to Asia–Europe and Europe–America; Europe–America volatility has an impact on all other regions exceptEurope; finally, America region volatility does not have a significant impact onitself or any other region. Overall the authors find evidence of both heat waveand meteor shower volatility effects.

Table III presents results from the multivariate volatility model for theEuro/dollar FX futures. Intraregion volatility effects found along the table’s

� b59sAmerica, t�12 � b510sAmerica, t�2

2 � c5xt � et.

� b55sEurope, t2 � b56sEurope, t�1

2 � b57 sEurope�America, t2 � b58sEurope� America, t�1

sAmerica, t2 � b50 � b51sAsia, t

2 � b52sAsia, t�12 � b53sAsia� Europe, t

2 � b54sAsia� Europe, t�12

� b18s2Europe� America, t�2 � b19sAmerica, t�1

2 � b110sAmerica, t�22 � c1xt � et.

� b15sEurope, t�12 � b16sEurope, t�2

2 � b17sEurope� America, t�12

sAsia, t2 � b10 � b11s

2Asia, t�1 � b12s

2Asia, t�2 � b13s

2Asia� Europe, t�1 � b14s

2Asia� Europe, t�2

st2 � B1st�1

2 � B2st�22 BPst�p

2 � Cxt � et,

diagonal are significant for all five regions. In addition, significant meteorshower or interregion volatility effects are found from the Asia region to theAsia-Europe, Europe, and America regions; from Asia–Europe to Europe andEurope–America; from Europe to all other regions; from Europe–America toEurope and America; and from America to Europe and Europe–America.

TABLE II

Wald Test for Heat Wave and Meteor Shower Volatility Effects for Eurodollar Futures

Independent variables

Independent Asia– Europe–variables Asia Europe Europe America America

Asia 4.58 (0.10) 51.78 (0.00) 23.93 (0.00) 12.76 (0.00) 47.68 (0.00)Asia–Europe 1.77 (0.41) 5.50 (0.06) 29.61 (0.00) 0.58 (0.75) 0.08 (0.96)Europe 0.93 (0.63) 8.18 (0.02) 16.00 (0.00) 10.42 (0.00) 2.55 (0.28)Europe–America 7.66 (0.02) 5.05 (0.08) 0.92 (0.63) 10.72 (0.00) 10.85 (0.00)America 0.74 (0.69) 4.47 (0.11) 3.13 (0.21) 4.12 (0.13) 3.82 (0.15)R2 0.04 0.17 0.24 0.17 0.23p-Value Q(5) 0.50 0.55 0.63 0.92 0.66p-Value Q(35) 1.00 0.32 0.74 0.22 0.89

Note. The table shows Wald coefficient tests for groups of coefficients that represent heat waves (intraregion volatility) and meteorshowers (interregion volatility) for the Eurodollar futures contract traded on CME Globex from January 2004 through June 2007. Chi-squared p-values are presented in parentheses. Adjusted R 2 and p-value coefficients for five- and 35-lag Q-statistics on residualautocorrelation are presented at the foot of the table. Results are generated using a five-equation SUR model with two lags and adummy variable for Mondays and holidays. CME, Chicago Mercantile Exchange; SUR, seemingly unrelated regression.

TABLE III

Wald Test for Heat Wave and Meteor Shower Volatility Effects for Euro/dollar FX Futures

Dependent variables

Independent Asia– Europe–variables Asia Europe Europe America America

Note. The table shows Wald coefficient tests for groups of coefficients that represent heat waves (intraregion volatility) and meteorshowers (interregion volatility) for the Euro/dollar FX futures contract traded on CME Globex from January 2004 through June 2007. Chi-squared p-values are presented in parentheses. Adjusted R 2 and p-value coefficients for five- and 35-lag Q-statistics on residual auto-correlation are presented at the foot of the table. Results are generated using a five-equation SUR model with two lags and a dummyvariable for Mondays and holidays. FX, foreign exchange; CME, Chicago Mercantile Exchange; SUR, seemingly unrelated regression.

Results indicate that the area with the most important volatility effect for thismarket is Europe. For the Euro/dollar FX futures, heat wave volatility effectsare more important than meteor shower effects.

For the E-mini S&P 500 futures, Table IV illustrates that heat wavevolatility effects are significant in all the five regions. Meteor shower volatilityspillovers are significant from Asia to Asia–Europe, Europe, and America; Asia-Europe to Europe, Europe–America, and America; Europe to all other regions;Europe–America to America; and America to all other regions. Results showthat European and American market volatilities are an important source ofvolatility spillovers in the S&P 500 futures market.

Figure 2 shows the impulse response functions of a one-standard devia-tion shock to the innovations in each region for each of the three futures mar-kets for a two-week period (ten lags). For ease of exposition, the diagram refersto Asia as region AS, Asia-Europe as ASEU, Europe as EU, Europe–America asEUAM, and America as AM. Each column of the graphs represents the same-region volatility impact of a one-standard deviation shock from each of the fiveregions, with a two-standard deviation error band. Same-region volatilityeffects are significant for all markets. These are at least twice the size of inter-region effects.

Consistent with the SUR model of volatility analysis, interregional volatil-ity spillovers are significant for all three markets, but the same region’s volatilityspillovers are considerably greater than interregional spillovers.

TABLE IV

Wald Test for Heat Wave and Meteor Shower Volatility Effects for E-Mini S&P 500 Futures

Dependent variables

Independent variables Asia Asia–Europe Europe Europe–America America

Note. The table shows Wald coefficient tests for groups of coefficients that represent heat waves (intraregion volatility) and meteorshowers (interregion volatility) for the E-mini S&P 500 futures contract traded on CME Globex from January 2004 through June 2007.Chi-squared p-values are presented in parentheses. Adjusted R 2 and p-value coefficients for five- and 35-lag Q-statistics on residualautocorrelation are presented at the foot of the table. Results are generated using a five-equation SUR model with two lags and adummy variable for Mondays and holidays. CME, Chicago Mercantile Exchange; SUR, seemingly unrelated regression.

AM to AM

1 2 3 4 5 6 7 8 9 10

EUAM to EUAM

�0.10

1 2 3 4 5 6 7 8 9 10

EU to EU

�0.10

1 2 3 4 5 6 7 8 9 10

ASEU to ASEU

�0.10

1 2 3 4 5 6 7 8 9 10

Eurodollar

AS to AS

�0.10

1 2 3 4 5 6 7 8 9 10

AM to AM

0.000.050.100.150.200.250.300.350.400.450.50

1 2 3 4 5 6 7 8 9 10

EUAM to EUAM

0.000.050.100.150.200.250.300.350.400.450.50

1 2 3 4 5 6 7 8 9 10

EU to EU

0.000.050.100.150.200.250.300.350.400.450.50

1 2 3 4 5 6 7 8 9 10

€/$ FX

AS to AS

0.000.050.100.150.200.250.300.350.400.450.50

1 2 3 4 5 6 7 8 9 10

ASEU to ASEU

0.000.050.100.150.200.250.300.350.400.450.50

E-mini S&P 500

1 2 3 4 5 6 7 8 9 10

AS to AS

0.000.050.100.150.200.250.300.350.400.450.50

1 2 3 4 5 6 7 8 9 10

ASEU to ASEU

0.000.050.100.150.200.250.300.350.400.450.50

1 2 3 4 5 6 7 8 9 10

EU to EU

0.000.050.100.150.200.250.300.350.400.450.50

1 2 3 4 5 6 7 8 9 10

EUAM to EUAM

0.000.050.100.150.200.250.300.350.400.450.50

1 2 3 4 5 6 7 8 9 10

AM to AM

0.000.050.100.150.200.250.300.350.400.450.50

FIGURE 2Response of Eurodollar, Euro/dollar FX, and E-mini S&P 500 futures to a one-standard deviation shockin the same region. The figure shows the impact of a one-standard deviation shock on volatility of eachworld region on itself for a ten-day period. Results show two standard error bands around each impulseresponse function. AS represents Asia; ASEU represents Asia–Europe, EU represents Europe, EUAM

represents Europe–American, and AM represents America.

Open Interest, Volume, and Volatility

Few researchers have taken advantage of information on the effects of volumeand open interest on volatility. A salient characteristic of futures markets is thatthe number of outstanding contracts at a given point in time is endogenouslydetermined, which is why open interest may provide more information on trad-ing activity than volume alone. Expected open interest represents open interestat the beginning of the trading session, or open interest as of yesterday’s close.Because speculators typically do not hold open positions overnight, open inter-est at the close is more than likely represented by hedgers or uninformedtraders. Unexpected open interest reflects changes in open interest during theday, and can be considered a measure of informed trading.

Bessembinder and Seguin (1993) studied the relation between volatility,volume, and open interest for eight agricultural, financial, and metal futurescontracts traded via open outcry. They analyzed whether surprises in tradingvolume and open interest convey more information and thus have a greatereffect on prices than forecastable volume and open interest.

Bessembinder and Seguin found that expected and unexpected volume arepositively related to volatility, but unexpected volume shocks, especially posi-tive ones, have a greater impact on volatility than expected volume shocks.With regard to open interest, which proxies for market depth, they concludedthat expected open interest mitigates volatility. More important, unexpectedopen interest can also help explain volatility, even considering volume.Whether a change is positive or negative, large changes in unexpected openinterest increase volatility.

The authors test the impact of volume and open interest on volatility ofthe Eurodollar, Euro/dollar FX, and E-mini S&P 500 futures contracts follow-ing Bessembinder and Seguin (1993). Conditional means and volatilities areestimated as follows:

In Equation (2), Rt is the daily returns for each futures contract, di the day of the week dummy variables, �t the volatility estimates from Equation (4), andUt the residuals or unexpected returns.

In Equation (3), Ut is the estimated unexpected returns and Ak the m trad-ing activity variables related to volume and open interest.

stˆ � d � an

j�1vjUt�j � a

i�1hidi � a

k�1mjAk � a

j�1bjst� j � et.

Rt � a � an

j�1gjRt�j � a

t�1ri di � a

j�1pjst� j � Ut,

Unexpected returns from Equation (2) are used to estimate daily standarddeviations (�t) using the transformation

First, Equation (2) is estimated without lagged volatilities. The trans-formed residuals (�t ) from Equation (4) are used to estimate Equation (3).Then fitted values from Equation (3) are used to re-estimate Equation (2).Equation (3) is re-estimated using the residuals from the consistent estimationof Equation (2). Lags of the estimated standard deviation series are included toallow persistence in price volatility.

The trading activity variables (Ak) are expected and unexpected volumeand expected and unexpected open interest. To avoid biases in these variablesbecause of approaching contract maturity, the authors use data on total volumeand open interest for all outstanding contracts. The authors also use returnsand volatility data for the nearest and most actively traded contract in eachmarket.

Expected and unexpected volume and open interest are estimated using Box-Jenkins methods. Stationary series are modeled using an ARMA(10,0) model andnonstationary series are modeled using an ARIMA(10,1,0) model. In our sample,volume data for the Eurodollar, Euro/dollar FX, and E-mini S&P 500 futures arestationary, and open interest data for these three contracts are nonstationary.

To partition each activity variable into its expected and unexpected com-ponents, first the one-step-ahead forecast error is calculated for each volumeand open interest series as follows:

where is the one-step-ahead forecast error, Activityit the open interest andvolume actual values, and E[Activityit� Activityi, t�tt� 1......,.10] � the estimat-ed activity value from each time series model.

The activity variables’ unexpected components are obtained by regressingthe one-step-ahead forecast error on lagged values of volatility, volume, andopen interest, as well as the number of days to the nearest contract’s expiration.The unexpected value for each series (vit) is the regression’s residual:

where is the one-step-ahead forecast error, sit�j the estimated daily standardeit

eit � c � a10

j�1rijsit�j � a

k�1likVolit�k � a

m�1mmOIit�m � fiDTEi � vit,

eit � Activityit � E[Activityit ƒ Activityi, t�tt � 1......,.10],

st � ƒ Ut ƒ2p�2.

deviations, Volit�k the volume, OIit�m the (differenced) open interest, DTEi thedays until next contract expiration, and vit the unexpected component.

Expected values for each series are obtained by subtracting the unexpect-ed component from the actual series:

Expected component � Activityit � vit. (7)

Results of this analysis are presented in Tables V–VII. Conditional meanreturn estimations in Table V show that for all markets neither lagged returnsnor lagged volatilities help explain realized returns. Dummies for days of theweek are also insignificant for all three markets.

The results of the conditional volatility model (3) are presented in Table VI.The sum of lagged volatility coefficients is positive for all three markets.However, it is found that volatility persistence is significant only for theEurodollar futures market. As noted by Bessembinder and Seguin (1993),these estimates underestimate the degree of persistence because the modelincludes past volumes, which are related to past volatilities.

The leverage effect found in equity markets predicts a negative relationbetween past returns and volatilities. Nonetheless, unlike a corporation’s stock,for many futures contracts there are no debt claims on the underlying assets, and thus no leverage effect explanation for the relation between past returns andvolatility. The authors’ results are consistent with this analysis. The sum of lagged

TABLE V

Time Series Models of Daily Returns

Daily returns

Market Eurodollar Euro/dollar FX E-mini S&P 500

Intercept 0.0105 (0.64) 0.1707 (1.17) �0.1853 (�1.12)

Day of the week dummies

Monday �0.0146 (�1.62) �0.0443 (�0.57) 0.0882 (1.20)Tuesday 0.0053 (0.58) �0.0414 (�0.56) 0.0373 (0.49)Wednesday 0.0003 (0.03) 0.0223 (0.32) 0.0407 (0.53)Thursday 0.0030 (0.34) 0.0191 (0.27) �0.0705 (�0.94)Sum of ten lagged volatilities �0.2468 (0.83) �0.2812 (1.18) 0.3294 (1.84)Sum of ten lagged returns �0.0129 (0.01) �0.1734 (1.97) �0.1213 (0.46)Adjusted R2 0.0117 �0.0060 0.0194Regression F-statistics 1.39 0.82 1.67

Note. The table shows autoregressive models of daily returns for the Eurodollar, Euro/dollar FX, and E-mini S&P 500 futures con-tracts traded on the CME Globex platform from January 2004 through June 2007. t-Statistics are presented in parentheses. and arecomputed using White (1980) standard errors. Test statistics for the sum of lagged return and volatility coefficients equal to zero areF-statistics, presented beside the coefficient sum of each variable. FX, foreign exchange; CME, Chicago Mercantile Exchange.

unexpected returns is significant only in the E-mini S&P 500 futures market (F-statistic � 12.4). In addition, the relationship between past returns andvolatility is negative, consistent with the leverage effect in the equity market.

Table VI illustrates that the unexpected volume coefficient is positive and significant at any conventional level (t-statistics > 5) for all three markets,indicating that unexpected increases in volume raise volatility. The expectedvolume coefficient is significant only in the Euro/dollar FX market. Moreimportant, the unexpected volume coefficient is more than four times the sizeof the expected volume coefficient, revealing that changes in unexpected vol-ume have a greater impact on volatility than changes in expected volume.1

Admati and Pfleiderer (1988) suggested that market depth may depend onwhether volume changes are expected or unexpected. They contended that toreduce their exposure to adverse selection, traders who can time their tradesprefer to trade when volume is expected to be high. Thus markets should be

TABLE VI

Relationship Between Daily Return Volatility and Trading Activity

Daily return standard deviations

Market Eurodollar Euro/dollar FX E-mini S&P 500

Intercept 0.0594 (5.74)** 0.6187 (6.11)** 0.4628 (4.35)**Expected volume �0.0007 (�1.76) �0.1103 (�2.68)** 0.0052 (0.73)Unexpected volume 0.0041 (10.48)** 0.5021 (9.50)** 0.0810 (5.70)**Expected open interest �0.0025 (�0.39) �0.1975 (�0.45) 0.0274 (0.62)Unexpected open interest �0.0011 (�0.65) 0.2445 (1.37) �0.0211 (�0.77)

Monday �0.0047 (�0.81) 0.0782 (1.33) 0.0493 (0.87)Tuesday �0.0051 (�0.78) �0.0298 (�0.55) 0.0102 (0.18)Wednesday �0.0096 (�1.50) �0.1126 (�2.22)* �0.0014 (�0.03)Thursday �0.0108 (�1.83)* �0.0700 (�1.45) 0.0420 (0.73)Sum of ten lagged volatilities 0.2463 (7.16)** 0.1541 (2.00) 0.1579 (2.13)Sum of ten lagged unexpected returns 0.0742 (0.73) 0.0914 (1.24) �0.3652 (12.40)**Adjusted R2 0.1709 0.1423 0.1874Regression F-statistics 6.74** 5.28** 7.61**

**Significant at the 1% level. *Significant at the 5% level.Note. The table shows regressions of daily return standard deviations on expected and unexpected trading activity for the Eurodollar,Euro/dollar FX, and E-mini S&P 500 futures contracts traded on the CME Globex platform from January 2004 through June 2007. Thedependent variable is the absolute value of unexpected returns times (p�2)1/2. Volume and open interest variables are in units of100,000 contracts. Expected and unexpected data series are fitted values and residuals from forecasting models applied to the origi-nal series. t-Statistics are presented in parentheses and are computed using White (1980) standard errors. Test statistics for the sumof lagged return and volatility coefficients equal to zero are F-statistics, presented besides the coefficient sum of each variable. FX, foreign exchange; CME, Chicago Mercantile Exchange.

1The results are similar if spot prices are used. Specifically, the leverage effect is significant in the index (F-statistic � 23.2), but not in the Euro/dollar FX and Eurodollar markets (F-statistics � 0.25 and 0.42).

deeper when volume is expected to be high. Positive and negative volume shocksmay have a different effect on price volatility. Positive volume shocks, for exam-ple, may signal increased informed trading. Allowing for an asymmetricresponse of volume shocks in the authors’ model may thus yield additional infor-mation.

Table VII explores whether positive and negative shocks of unexpected vol-ume and open interest have varying impacts on volatility. A dummy variablethat is equal to one if the unexpected activity impact is positive and zero other-wise is created. To isolate the effects of positive and negative unexpectedchanges, an interaction variable of this dummy times the unexpected activityvariable is included. Thus the coefficient of unexpected volume or unexpectedopen interest represents the impact of a negative shock on volatility, and the sumof the previous and the interaction variable coefficient represents the impact of apositive shock on volatility.

TABLE VII

Relationship Between Daily Return Volatility and Trading Activity Allowing for Asymmetric Activity

Daily return standard deviations

Market Euro Euro/dollar FX E-mini S&P 500

Intercept 0.0585 (6.65)** 0.5948 (5.64)** 0.4596 (4.24)**Expected volume �0.0009 (�2.92)** �0.1177 (�2.77)** 0.0006 (0.08)Unexpected volume 0.0004 (0.75) 0.4574 (3.96)** 0.0570 (3.57)**Unexpected volume, *Voldummy 0.0376 (9.38)** 0.0572 (0.32) 0.0374 (0.95)Expected open interest �0.0044 (�0.80) 0.1009 (0.19) 0.0367 (0.55)Unexpected volume �0.0017 (�1.26) 0.0539 (0.22) �0.0192 (0.61)Unexpected open interest, *OIdummy 0.0232 (1.35) 0.7229 (1.12) 0.0076 (0.07)

Monday �0.0073 (�1.26) 0.0740 (1.21) 0.0423 (0.74)Tuesday �0.0034 (�0.63) �0.0353 (�0.66) 0.0145 (0.26)Wednesday �0.0057 (�1.07) �0.1128 (�2.23)* �0.0490 (0.08)Thursday �0.0063 (�1.16) 0.0052 (�1.47) 0.0462 (0.80)Sum of ten lagged volatilities 0.2213 (6.28)* 0.1663 (2.27) 0.1702 (2.45)Sum of ten lagged unexpected returns 0.0561 (0.45) 0.0908 (1.22) �0.3718 (12.84)**Adjusted R2 0.2646 0.1422 0.1884Regression F-statistics 10.35** 4.99** 7.21**

**Significant at the 1% level. *Significant at the 5% level.Note. The table shows regressions of daily return standard deviations on expected and unexpected trading activity for theEurodollar, Euro/dollar FX, and E-mini S&P 500 futures contract traded on the CME Globex platform from January 2004 through June2007. The dependent variable is the absolute value of unexpected returns times (p�2)1/2. Volume and open interest variables are inunits of 100,000 contracts. Expected and unexpected data series are fitted values and residuals from forecasting models applied tothe original series. Voldummy and OIdummy are equal to 1 if unexpected volume and unexpected open interest are correspondinglypositive and are equal to 0 otherwise. t-Statistics are presented in parentheses and are computed using White (1980) standarderrors. Test statistics for the sum of lagged return and volatility coefficients equal to zero are F-statistics, presented besides the coef-ficient sum of each variable. FX, foreign exchange; CME, Chicago Mercantile Exchange.

Results show that the unexpected volume coefficients associated with neg-ative volume shocks are positive but significant only for the Euro/dollar FX andthe S&P 500 markets. The positive coefficient indicates that an unexpecteddrop in volume reduces return volatility in all markets. For the Euro/dollar FXand the S&P 500 futures, cross-terms involving volume are positive, butinsignificant. Therefore, in these markets positive shocks cause volatility torise, and both positive and negative volume shocks are of similar size. Thecross-term coefficient for the Eurodollar futures is significant at the 1% level;hence, for the Eurodollar the effect of positive volume shocks on volatility isconsiderably greater than the effect of negative volume shocks.

Whether or not asymmetries in the activity variables are allowed for,expected and unexpected open interests do not have an impact on volatility forthe markets analyzed. This result is different from that of Bessembinder andSeguin (1993). The explanation has to do with the type of trading venue.Bessembinder and Seguin examined futures contracts traded in open outcry;the authors examine contracts traded electronically.

The nature of liquidity supply differs in these types of markets. The mainliquidity providers in open outcry are scalpers, whereas on electronic exchangesliquidity is endogenously created throughout the trading session. Grossmanand Miller (1986) and Miller (1991) suggested that open-outcry trading ismore liquid and more deep than computerized trading. They claimed that in anelectronic system traders have a greater exposure to adverse selection than infloor trading systems.

Other researchers including Bloomfield et al. (2005) argued that informa-tion asymmetry on electronic exchanges is no reason for these trading venuesto be less liquid. They studied the evolution of liquidity in an electronic marketby focusing on how liquidity emerges endogenously. Early in the trading ses-sion, liquidity traders use more limit orders, and as the end of the trading session approaches, they switch to market orders. In the meantime, informedtraders profit from their private information using market orders and taking liq-uidity from the market. As the session progresses, and their informationbecomes less valuable, they switch to limit orders, profiting from the bid-askspread and providing liquidity to the market. Toward the end of the trading ses-sion, informed investors use more limit orders than uninformed investors, pro-viding a greater portion of the market’s liquidity.

Thus informed traders not only take liquidity from but also provide liquid-ity to the market. This explains why electronic markets can endogenously cre-ate liquidity even in the presence of information asymmetry.

Unlike its effect on pit-traded futures, a rise in expected or unexpectedopen interest does not impact return volatility for instruments traded in ascreen-based system. It seems that in this type of trading venue open interest is

not a good proxy for liquidity. In this setting, the trading strategies of informedand uninformed players along with information on the limit order book mayprovide more detail on the market’s liquidity than open interest does.

CONCLUSIONS

The authors analyze intraday volatility in the bond, FX, and stock markets usingdata on the Eurodollar, the Euro/dollar exchange rate, and the E-mini S&P 500futures contracts traded on a continuous 23-hour schedule on the CMEGlobex electronic platform. Using a SUR model for volatility, the authors findthat volatility transmission in a single market across different regions isexplained largely by intraregion volatility (heat waves). Interregion volatility(meteor showers), although significant, plays a secondary role in volatility trans-mission. Impulse response functions for the bond, FX, and stock markets showthat heat wave volatility shocks have at least twice the impact of meteor show-er shocks. Thus, return volatility in the Eurodollar, the Euro/dollar FX, and the E-mini S&P 500 futures is mainly driven by each region’s own volatility.

The authors also study the impact of expected and unexpected changes inactivity variables such as volume and open interest on volatility. Although volumetends to increase volatility, open interest does not affect it. In electronic marketsopen interest does not seem to be a good proxy for liquidity. Rather, strategies ofinformed and uninformed investors along with information on the limit orderbook provide valuable information on the market’s liquidity. Informed tradersplay a dual role as liquidity takers and as liquidity providers, indicating the impor-tance of information for liquidity creation in electronic trading systems.

These results may help policy makers understand better the causes andconsequences of volatility transmission in different markets around the worldand give them a better understanding of liquidity factors and the marketdynamics that may affect them. Implementation of market policies using thisinformation may help avoid extreme market movements and perhaps even market failures.

BIBLIOGRAPHY

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