S&P 500 INDEX‐FUTURES PRICE JUMPS AND

MACROECONOMIC NEWS

HONG MIAO*, SANJAY RAMCHANDER and J. KENTON ZUMWALT

This study examines the influence ofmacroeconomic news on price discontinuities in the S&P500index futures. Results document a strong association between macro news and price jumps. Overthree‐fourths of the price jumps between 8:30 am and 8:35 am and over three‐fifths of the jumpsbetween 10:00 am and 10:05 am are related to news released at 8:30 am and 10:30 am,respectively. Notably, among several types of news releases considered, Non‐farm Payroll andConsumer Confidence are found to be significantly related to price jumps. Our findings alsoprovide insights into the speedof news absorption and the influence of alternative trading platformson the jump return behavior. © 2013 Wiley Periodicals, Inc. Jrl Fut Mark 34:980–1001, 2014

1. INTRODUCTION

This study examines the influence of macroeconomic news announcements on pricediscontinuities in equity index futures. The study can be placed in the context of a large andrapidly evolving research program examining the distributional properties of speculativereturns. This research carries important implications on risk measurement and management,portfolio allocation and rebalancing, and on the pricing of various derivative instruments.Although most of the standard literature in finance assumes that prices follow a geometricBrownian motion, this assumption has come under heightened scrutiny as empiricalobservations increasingly point to the presence of infrequent and large price movements(labeled as “jumps”) that violate theGaussian distribution assumption. Several studies such asBates (2000), Eraker, Johannes, and Polson (2003), and Zhou and Zhu (2011) document theimportance of stochastic volatility and systematic jump risk when pricing returns. Maheu andMcCurdy (2004) argue that price discontinuities are likely the result of uncertainty resolutionassociated with the release of new and relevant information, or “news.”

Among the various sources of public information available for the resolution ofuncertainty, announcements pertaining to macroeconomic news are easily available andclosely followed. Hence, they provide insights into the canonical model of efficient marketswhich posit that security prices reflect all available information. The speed with which news isincorporated into prices and the volatility of returns are important for actively trading

Hong Miao (Assistant Professor) Sanjay Ramchander (Professor), and J. Kenton Zumwalt (Professor) arecollocated at the Department of Finance and Real Estate, Colorado State University, Fort Collins, Colorado.We thank Professor Robert I. Webb (the editor) and an anonymous referee for their extensive comments andsuggestions on our research. Any remaining errors belong to the authors.

JEL Classification: G10, G14

*Correspondence author, Department of Finance and Real Estate, Colorado State University, Fort Collins, CO. Tel:þ970‐491‐2356, Fax: þ970‐491‐7665, e‐mail: hong.miao@colostate.edu

Received April 2012; Accepted April 2013

The Journal of Futures Markets, Vol. 34, No. 10, 980–1001 (2014)© 2013 Wiley Periodicals, Inc.Published online 24 June 2013 in Wiley Online Library (wileyonlinelibrary.com).DOI: 10.1002/fut.21627

investment managers. Assets that are more sensitive to news announcements may need to bemore closely monitored and rebalanced more frequently than less sensitive assets.Furthermore, evidence favoring the influential role of macro announcements on returnswould support the notion that any variable that affects the investment opportunity set(Merton, 1973) or consumption level (Breeden, 1979) should be a priced factor inequilibrium.

However, the theoretical relationship between news—both firm‐specific and macroeco-nomic—and equity returns has found only limited, and often contradictory, support amongempirical studies. Studies that report a weak relationship between stock market activity andnews include Roll (1988), Cutler, Poterba, and Summers (1989), Mitchell and Mulherin(1994), and Berry and Howe (1994). Although, the effect of real sector macroeconomicvariables has proven to be somewhat elusive, the influence of monetary and price variables isfound to be more consistent with theoretical predictions (Flannery & Protopapadakis, 2002).In general, there seems to be a divergence of scholarly viewpoints on the role of fundamentalsin explaining equity returns, leading several researchers to the conclusion that there exists an“embarrassing gap” (Chen, Roll, & Ross, 1986) and a “poor showing” (Chan, Karceski, &Lakonishok, 1998) in the empirical asset pricing literature.

Given this backdrop the contributions of the study are threefold. First, by narrowing ourinvestigation to significant price movements, we attempt to better identify the underlyingrelationship between economic fundamentals and equity prices. Second, we compare thebehavior of intra‐day volatility and trading volume on news days with corresponding jumpsversus news days without any corresponding jumps. Finally, in the course of the examination,we provide insights into the speed of market response and price resolution to news releases.

There are two additional features of the study that are noteworthy. First, in analyzingprice jumps our analysis focuses on the standard‐size S&P 500 (SP) equity index futurescontract. This contract is not to be confused with the corresponding E‐mini (ES) contractwhich is sized at one‐fifth of their full‐sized counterpart. Although both SP and ES are closelyrelated instruments, and in fact the potential for arbitrage ensures that their prices are veryclose to each other, there are some important trading differences between them that motivateour analysis of SP. For instance, SP trades around‐the‐clock separately, without any overlap intrading hours, on the floor of the ChicagoMercantile Exchange (CME) and on the electronicexchange (Globex). The open outcry market for SP operates during regular daytime hours of9:30 am to 4:15 pm Eastern Time, whereas the electronic platform provides liquidity duringafterhours and overnight hours. In contrast, ES futures are traded, almost 24 hours a day,exclusively on Globex.

The coexistence of SP and ES is somewhat of a rarity in the current exchangeenvironment considering that, in most instances the initiation of electronic trading hasgenerally eviscerated trading in the open outcry market. However, the SP contract continuesto survive despite the growth and dominantmarket share of the electronically traded ES. It hasbeen argued that the relevance of SP partly stems from its ability to offer large institutionaltraders a transaction cost advantage (i.e., cost per dollar of trading volume) relative to theE‐mini market, which appeals primarily to small retail traders. Furthermore, the full‐sizecontract allows traders flexibility to switch positions in the electronic platform thus providingliquidity during afterhours (see Dungi, Fakhrutdinova, & Goodhart, 2009). Therefore, tosummarize, our study’s emphasis on SP is motivated by the fact that it provides an interestingset of circumstances to examine price jumps to news releases in non‐overlapping electronicand open outcry markets while accounting for the dominance of E‐mini futures. Specifically,our analysis sheds light on the market reaction to the same set of news announcements alongtwo key dimensions: (a) jump frequencies of SP prices for the period 2001–2003 (beforeGlobex trading dominated pit trading) and 2004–2010 (a period characterized by the

S&P 500 Index‐Futures Price Jumps and Macroeconomic News 981

dominance of Globex); and (b) jump returns in SP (traded on the pit) and ES (traded onGlobex).

A second noteworthy feature of our study is the methodology we employ to detect jumps.We use a jump identification technique, recently developed by Lee and Mykland (2008) thatdetects the precise timing of the intra‐day jump. Identifying the precise timing of thehigh‐frequency price jump is important in order to account for the possibility of major priceadjustments that occur within a fewminutes after news announcements and provide evidenceon the price response function (see Ederington & Lee, 1993, 1995; Balduzzi, Elton, &Green, 2001, inter alia).

The study proceeds as follows. Section 2 provides a brief literature review. Sections 3and 4 outline the data and describe the jump detection methodology, respectively. Section 5reports empirical evidence linking jumps with news announcements and presents results onthe speed of news absorption. Finally, Section 6 concludes this study.

2. RELATED LITERATURE

The central questions we seek to answer are as follows: if economic state variables are not veryhelpful in explaining equity return movements, can they at least be used to explain dramaticprice fluctuations? How do volatility and trading volume adjust in the immediate aftermath ofnews announcements? Is the adjustment process different for days with news andcorresponding jumps versus days with news and no jumps? Furthermore, in the event macronews events are found to be related to price jumps in some predictable manner, what is thespeed of price adjustments? In examining the price reaction, several other related questionsarise: Are equity index futures prices more sensitive to changes in some economic news itemsthan other news items? What is the nature and direction of the relationship? Finally, how hasthe transition from open outcry to electronic trading influenced the behavior of price jumps?

The empirical literature provides guidance to some of these questions. Flannery andProtopapadakis (2002) document the impact of inflation on the level and/or volatility of equitymarket portfolio returns. However, they find that measures of overall economic activity suchas Industrial Production and GNP are not significant. Adams, McQueen, and Wood (2004)report that large stocks respond to inflation surprises “within 10–20minutes or about sixtrades,” and that small stocks response to inflation news is less significant. Fair (2002)identifies 69 events between 1982 and 1999 that were followed by large price changes in theS&P 500 futures. The author indicates that these events were directly associated with moneysupply or interest rate announcements, and were indirectly related to monetary policyannouncements.

More recently, jump detection methods are used to reexamine the news–returnsrelationship. Rangel (2011) examines “normal” and “surprising” news events and finds thatjumps are more frequent on announcement days than on non‐announcement days. Evans(2011) also reports that about one‐third of the observed price jumps in the equity, bond andcurrencymarkets occur onmacro news release days. The sizes of the jumps are directly relatedto the “informational surprise” contained in the announcements and the market incorporatesthe news within about 5minutes. However, elevated return volatility may persist for severalmore hours and, interestingly, the reactions of the S&P 500 E‐Mini futures are “moredramatic” than the reactions of the T‐bond and exchange rate futures. Bjursell, Wang, andWebb (2010) identify intra‐day jumps in interest rate futures prices for the period 2001–2004and find that although jumps make up a large proportion of the total variance for days withjumps, a substantial percentage of jumps cannot be associated with specific macroeconomicnews announcements. The “employment” report is found to have the most significant impacton interest rates. Their study documents that on days where news items are associated with

982 Miao, Ramchander, and Zumwalt

jumps there is a spike in the volatility and trading volume following the news release.Interestingly, volatility is found to revert back to pre‐announcement levels faster followingscheduled news releases with jumps than after announcements without jumps.

Lahaye, Laurent, andNeely (2011) relate macro news announcements to intra‐day pricejumps in exchange rates, bonds, and stocks. They find that their set of news announcements:(a) explains a larger proportion of bond or equity jumps than currency jumps, and (b) drives ahigher percentage of cojumps than jumps. Jiang, Lo, and Verdelhan (2011) use 5‐minuteprice data on Treasury notes and bonds to compare the impact of macroeconomic newsannouncements with the impact of liquidity shocks and conclude that liquidity shocks inaddition to announcement shocks “play an important role” in the price discovery process (alsosee Lee, 2012).

Finally, related to the trading feature of equity index futures contracts, several studiesexamine the ongoing transition from open outcry to electronic trading in equity andfutures markets. They find that automated exchanges tend to provide more liquidity, withlower bid‐ask spreads, faster execution, and lower transaction costs (see, e.g., Ates &Wang, 2005; Aitken, Frino, Hill, & Jarnecic, 2004; Tse & Zabotina, 2001). However,studies also find that the liquidity of automated exchanges deteriorates more rapidly thanfloor traded systems during periods of high volatility (Frino, McInish, & Toner, 1998). Ofthe three S&P 500 stock index futures markets—Globex traded E‐mini, pit traded full‐size,and Globex traded full‐size—there is evidence that the majority of price discovery seems tooccur in the E‐mini S&P 500 stock index futures (see, e.g., Hasbrouck, 2003; Kurov &Lasser, 2004).

3. DATA DESCRIPTION

The full‐size S&P 500 equity index futures (SP) and the E‐mini (ES) contracts are traded onthe Chicago Mercantile Exchange (CME). Although in most markets there has been amigration away from floor‐based trading toward screen‐based systems, in the case of SP thesame contract is traded in non‐overlapping hours on both platforms. Open outcry trading ofSP occurs on weekdays between 9:30 am Eastern Time (ET) and 4:15 pm ET, whereas theunderlying securities trade on the NYSE and Nasdaq stock exchanges between 9:30 am and4:00 pm (ET), Monday through Friday.1 Electronic (Globex) trading of SP begins at 4:30 pmand ends at 9:15 am the following day, Monday through Thursday with an hour maintenanceshutdown from 5:30 pm to 6:30 pm. The electronic market is closed Friday night and all daySaturday, and is open on Sunday from 6:00 pm to 9:15 am the next day.

The intra‐day futures prices for SP and ES are obtained from TickData whichprovides the transaction price and the time of each trade to the nearest second. Table Ipresents the yearly size‐adjusted trading volume statistics of SP and ES.2 In examining thetable it is evident that, with the exception of the year 2003, the size‐adjusted volume in ESdominates SP. For instance, in 2003 the volume in the regular SP contract was about112% of the E‐mini. In 2011, the corresponding volume in SP represented only about 6%of the volume in ES.

Our empirical analysis of SP contains all pit transactions from January 2001 throughDecember 2010 and all Globex transactions beginning July 2003 to year end 2010. Followingstandard procedure a continuous time series of transactions is developed using the contractwith the greatest number of transactions. We begin with the front‐month contract, but roll

1Note all times in the study are U.S. Eastern Standard Time.2We do not have pit volumes for SP. Consequently, our comparisons of volume rely on electronic afterhours trading inSP (which started in 2003) versus ES.

S&P 500 Index‐Futures Price Jumps and Macroeconomic News 983

into the first back‐month contract when the daily transactions of the current front monthcontract are exceeded by the first back‐month contract. By following this procedure we avoidthe front‐month contract stale prices that occur as it approaches expiration. The data are thensampled at the 1, 5, 10, and 15minutes frequencies.

This study considers several different types of announcements released at 8:30 am and10:00 am.3 Bloomberg is the source of both the pre‐announcement consensus (median)forecast and the realized value for eachmonthly, pre‐scheduledmacroeconomic news release.Each news release is “standardized” by dividing the difference between the realized value andthe consensus forecast by its standard deviation. This allows us to compare the impact acrossthe different macroeconomic news announcements. That is,

SAi;t ¼ Ai;t � Ei;t

si; ð1Þ

where SAi,t is the surprise element of the announcement of type i at time t,Ai;t is the realized oractual value of an announcement, Ei,t is the consensus forecast and si is the sample standarddeviation of the surprise component of the type i announcement, Ai;t � Ei;t. Thestandardization procedure does not affect the statistical significance of the estimatedresponse coefficients and the fit of the regression model discussed below, because si isconstant for each announcement.

A brief set of descriptive statistics for the 17 pre‐scheduledmonthly news releases at 8:30am and 10:00 am over the period 2001–2010 is shown in Table II. As the 8:30 amannouncements are released 45minutes before the opening of the CME pit market, wemeasure their price impact using GLOBEX transactions. With the exception of BusinessInventories each of the remaining 10 economic variables has 90 different news releases. Themajority of Business Inventories are released at 10:00 am. The 10:00 am announcements arereleased 30minutes after the opening of theCME futures pitmarket and after trading starts inthe NYSE and Nasdaq markets. The differences in the mean surprises and the standarddeviations confirm that the news variables should be standardized in order to assess the impactof the different types of announcements.

TABLE IYearly Size‐Adjusted Trading Volume Statistics of SP and ES

Year After hours: ES After hours: SP Regular hours: ES

2003 2.76 3.08 73.182004 7.91 6.08 153.432005 11.03 5.27 185.932006 16.34 5.73 224.922007 35.24 7.89 347.212008 61.43 9.67 525.392009 54.52 4.66 461.032010 74.63 4.38 440.22

This table reports the year‐by‐year size adjusted total volume (contracts in millions) of SP and ES and SP. Regular hours: 9:30 amto 4:15 am. Afterhours: all the other trading periods.

3The set of announcements used is essentially the same as that used by Ederington and Lee (1993) and Simpsonand Ramchander (2004). Also, it would be pertinent to note that Business Inventories was released alternatively at8:30 am or 10:00 am.

984 Miao, Ramchander, and Zumwalt

4. JUMP IDENTIFICATION METHODOLOGY

The evolution of asset prices in jump‐diffusionmodels is represented as a sum of a continuoussample path process and occasional discontinuous jumps with the following stochasticdifferential equation form:

dpt ¼ mt dtþ sðtÞ dwt þ kt dqt; t � 0; ð2Þwhere pt denotes the continuous‐time log‐price process, the mean process mt is continuousand locally bounded, the instantaneous volatility process st is càdlàg, wt is a standardBrownianmotion independent of the drift, and qt refers to a normalized counting process suchthat dqt¼1 indicates a jump at time t, and dqt¼ 0 otherwise, with the kt process describing thesize of the jump if a jump actually occurs at time t.

The continuous‐time expression in Equation (2) is convenient for theoretical pricingarguments, but is of limited relevance in empirical studies that rely on discretely sampledprices. In practice, the discrete‐time returns implied by Equation (2) are defined as

rt ¼ pt � pt�1; t ¼ 1; 2;…; ð3Þwhere the unit time interval is usually referred to as a “day.”WithMþ 1 observations per dayof high‐frequency data, the continuously compounded M intra‐daily returns for day t aresimilarly denoted by

rtj ¼ ptj � ptj�1; t ¼ 1; 2;…;T; ð4Þ

TABLE IIStatistics of Macroeconomic News Announcements over the Period 2001–2010

News Obs. Meana SDa

8:30 news announcementsAdvanced retail sales (ARS) 90 0.0000 0.0057Business inventories (BI) 17 �0.0002 0.0024Changes in nonfarm payrolls (CNP) 90 �16.1444 76.9932Consumer price index (CPI) 90 �0.0000 0.0015Durable goods orders (DGO) 90 �0.0024 0.0238Gross domestic product (GDP) 90 �0.0003 0.0048Housing starts (HS) 90 4.4222 91.3879Personal consumption (PC) 90 �0.0001 0.0036Personal income (PI) 90 0.0005 0.0032Producer price index (PPI) 90 0.0006 0.0053Trade balance goods and services (TBGS) 90 0.1444 3.4473

10:00 news announcements:Business inventories (BIb) 74 �0.0001 0.0022Consumer confidence (CC) 118 �0.2831 5.3733Construction spending (CS) 120 0.0011 0.0080Factory orders (FO) 119 0.0002 0.0071ISM‐manufacturing (ISM) 120 0.2067 2.1174Leading economic indicators (LEI) 119 0.0001 0.0018New home sales (NHS) 120 5.6000 69.4272

aThe mean and standard deviation are the statistics of daily data of those announcement days.bBefore 2003, Business Inventories was released at 8:30 am. During 2003–2005, it was released at 8:30 am for somemonths and10:00 am for other months. Specific, 3 announcements in 2003, 6 announcements in 2004 and 5 announcements in 2005 weremade at 10:00 am and the others at 8:30 am. It has been released at 10:00 am since 2006.

S&P 500 Index‐Futures Price Jumps and Macroeconomic News 985

where ptj denotes the jth intra‐day log‐price for day t and T is the total number of days in thesample.

Following Andersen and Bollerslev (1998), Andersen et al. (2001), and Barndorff‐Nielsen and Shephard (2002), realized volatility for day t is defined as

RVt ¼XMj¼1

r2tj ; t ¼ 1;…;T: ð5Þ

From the theory of quadratic variation, RVt provides a consistent estimator of the dailyincrement to the quadratic variation for the underlying log‐price process in Equation (2). Thatis, for M ! 1;

RVt!p

Z t

t�1s2s dsþ

Xqts¼qt�1

k2s ; t ¼ 1;…;T: ð6Þ

Clearly, the realized volatility measure includes the contributions of both integrated volatility(the first term) and total variation stemming from the squared jumps.

On the other hand, the realized bipower variation (BV) introduced by Barndorff‐Nielsenand Shephard (2004) is defined as

BVt � m�21

XMj¼2

rtj�� �� rtj�1

�� ��; t ¼ 1;…;T; ð7Þ

where m1 is the mean of the absolute value of the standard normally distributed randomvariable and m1 ¼ ffiffiffiffiffiffiffiffi

2=pp

. It has been shown that, even in the presence of jumps, forM ! 1;

BVt!p

Z t

t�1s2s ds; t ¼ 1;…;T: ð8Þ

Combining Equations (6) and (8), we have, for M ! 1,

RVt � BVt !Xqts¼qt�1

k2s ; t ¼ 1;…;T: ð9Þ

The difference between RVt and BVt provides a consistent estimate of the contribution of thejump component to the total variation. Based on this framework, Lee and Mykland (2008)propose a statistic to identify intra‐day jumps.4

For n observation in a fixed time interval [0,T], the statistic to test at time tiwhether therewas a jump from ti�1 to ti is defined as

LðiÞ ¼ rticstl ;ð10Þ

where cstl is the realized bipower variation and,

cstl2 ¼ 1

K� 2

Xi�1

j¼i�kþ3

rtj�� �� rtj�1

�� ��: ð11Þ

4We also applied the Andersen et al. (2010) jump identificationmethod proposed by Andersen et al. (2010) and foundthe results to be qualitatively similar.

986 Miao, Ramchander, and Zumwalt

Based on the further developed properties of the statistic, Lee and Mykland (2008)suggest a rejection region for the null hypothesis of no jump at ti at a given significance level, a,is

LðiÞj j � Cn

Sn> �logð�logð1� aÞÞ; ð12Þ

where

Cn ¼ ð2lognÞ1=2c

� logpþ logðlognÞ2cð2 lognÞ1=2

; ð13Þ

and

Sn ¼ 1

2cð2 lognÞ1=2; ð14Þ

where n is the number of observations. The selection of the window sizeK is determined by thesampling frequency. They suggest that the optimal choice for K is the smallest integer suchthat, K �

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi252� nobs

p, where nobs is the number of observations per day. Therefore, the

optimal window sizes for 1‐, 5‐, 10‐, and 15‐minute data are: 603, 270, 191, and 156,respectively.

5. MACROECONOMIC NEWS AND JUMPS IN EQUITY INDEX FUTURES

5.1. Jumps in Equity Index Futures Prices

In theory the jump identification test can be used at any data frequency. However,Andersen et al. (2007), Dumitru and Urga (2012), and Lee and Mykland (2008) indicatethat as the intra‐day sampling frequency used to detect jumps increases there is a greaterlikelihood that the jump tests are contaminated by microstructure noise. For instance,employing high frequency data for five stocks, Dumitru and Urga (2012) document amarked decrease in the percentage of identified jumps as the sampling frequency decreases.The authors propose using a sample frequency where the percentage of jumps tends tostabilize. Following their procedures, a 5‐minute frequency is used in our empiricalanalysis.

Table III presents summary statistics of 5‐minute returns for both pit and electronictrades (GLOBEX). Using data from pit trades, a 5‐minute discrete interval sampling processresults in about 200,000 return observations. The average 5‐minute return is slightly negative,and the standard deviation is substantially higher at 0.13%. The distribution is slightlypositively skewed at 0.274. The high kurtosis indicates that the return distribution is notnormal. The results reveal that GLOBEX trading returns are relatively less volatile but exhibithigher skewness and kurtosis.

Table IV documents descriptive properties of jump returns that are significant at the 1%threshold at the 5‐minute frequency. Several interesting results are evident for the pit tradingstatistics. First, the number of days with at least one significant jump occurs during 20% of thetrading days. Second, there are 727 jumps for SP which accounts for about 0.36% of the totalnumber of observations. Third, there aremore negative jumps than positive jumps. Finally, themean absolute jump values are 0.46% which is nearly five times higher than the mean of theabsolute returns (0.08%).

S&P 500 Index‐Futures Price Jumps and Macroeconomic News 987

TheGlobex results show a higher jump propensity. There is evidence of at least one jumpin about 70% of the trading days. The average numbers of jumps per jump day is 2.29.Interestingly, the jump sizes in Globex are substantially smaller in magnitude than those in pittrading. The jump size in the pit is 0.46% versus 0.20% in the electronic market. Althoughjump sizes are relatively much smaller in Globex, they are still significantly higher than the“normal”means of the absolute returns. In addition, the return levels in the pit market are onaverage larger than Globex returns whichmay explain why the pit market exhibits higher pricejumps. If transaction prices in the two trading platforms are pooled together most of the pricejumps in Globex tend to get washed away due to their relatively smaller sizes.5

TABLE IIISummary Statistics of 5‐Minute Returns

Statistics

Returns (%)

Pit Globex

Mean �0.0004 �0.0001SD 0.1253 0.0546Min �2.7304 �1.4250Max 3.6989 4.2652Skewness 0.2744 1.9449Kurtosis 25.21 199.72Count 203,523 291,731Days 2,553 1,903

This table reports the summary statistics of the 5‐minute returns for pit and globex transactions. Sample periods for the pit andglobex transactions are from January 2001 through December 2010 and from July 2003 through December 2010, respectively.

TABLE IVDescriptive Properties of Significant Return Jumps Sampled at 5‐Minute Frequency

Pit Globex

Observations 203,523 291,731(|abs(return)|) 0.08 0.03Days 2,553 1,903Jump Days 520 1,327P(Jumpday) (%) 20 70E(#Jump|Jump Day) 1.40 2.29Jumps 727 3041P(jump) (%) 0.36 1.04E(|jumpsize||jump) 0.46 0.20Std(|jumpsize||jump) (%) 0.35 0.19Positive Jumps 306 1473P(jump>0) (%) 0.15 0.50E(jumpsize|jump>0) 0.48 0.20Std(jumpsize|jump>0) (%) 0.40 0.22Negative Jumps 421 1568P(jump<0) (%) 0.21 0.54E(jumpsize|jump<0) �0.44 �0.20Std(jumpsize|jump<0) (%) 0.32 0.16% of negative jumps 57.91 51.56

5These results are available from the authors upon request.

988 Miao, Ramchander, and Zumwalt

5.2. Role of Macro News Releases

Having identified intra‐day jumps, we next match the 8:35 am and 10:05 am jumps (defined asjump returns for the interval 8:30 am–8:35 am and 10:00 am–10:05 am, respectively) withnews released at 8:30 am and 10:00 am. Figure 1 presents the jump distribution. It is clear thatthere are a far greater numbers of jumps at 8:35 am than at any other time during the tradingday, and there are also more jumps at 10:05 am than most other time in the trading day exceptmarket openings. There are 362 significant jump events at 8:35 am; and additional 99 jumpsat 10:35 am.6 These preliminary findings suggest that some of these jumps may be associatedwithmacroeconomic news released at those times. Table IV provides additional results on thisrelationship.

Table V shows that about 60% of the 10:05 am jumps can be matched with the release ofat least one 10:00 am macroeconomic news announcement. Notably, Consumer Confidencehas the largest impact on jumps—about 18% of these releases can be matched with jumps ofSP returns. In terms of importance, ISM and Construction Spending are also prominentlyassociated with jumps in the index futures. The table also documents a substantial number ofjumps at 8:35 am. About 78% of these jumps occur in the 5‐minute interval after the release ofat least one pre‐scheduled 8:30 am news release. Changes in Nonfarm Payroll (which is partof the Employment Report), CPI, GDP, and Personal Consumption are among the mostinfluential macro news announcements. Out of the 90 total news releases of Changes inNonfarm Payroll at 8:30 am, there are 69 (77%) announcements that are followed by jumps inSP. The corresponding statistic for CPI is 57%. All of the 8:30 am jumps (except TradeBalance) are more highly associated with news releases than are the 10:30 am jumps. TradeBalance seems to be the least influential on jump returns.

Table VI documents the top twenty 10:05 am and 8:35 am jumps based on the absolutevalues of the returns in the index futures series and the corresponding macroeconomicannouncements. Among the top twenty jumps, 16 occur immediately after the release of atleast one macroeconomic announcement. Among the top ten jumps, five jumps can be taggedwith the release of Consumer Confidence, and three closely follow the releases of ISM. Thedirectional relationship is also highlighted by the fact that most negative jumps followworse‐than‐expected economic news and positive jumps follow better‐than‐expected news.Seventeen out of the top 20 jumps at 8:35 am are primarily connected to one (or in some casestwo)macroeconomic news announcements. Furthermore, the results confirm the importanceof the nonfarm payroll statistics. A total of 11 among the top twenty 8:35 am jumps correspondwith the Changes in Nonfarm Payroll.

Overall, results from Table VI indicate a positive association between news and indexreturn jumps and highlight several news announcements that seem to be influential. Thisexploratory work is more formally investigated through regression analysis.

5.3. Marginal Impact of Macro News on Jump Returns

Themarginal impact of each time‐stamped news surprise on the 8:35 am and 10:05 am jumpsare examined by fitting a multivariate regression model of the following form:

jptjþ1¼ cþ

Xni¼1

cþi SAþi;tj þ

Xni¼1

c�i SA�i;tj þ etjþ1 : ð15Þ

6We also observe a relatively large number of significant jumps at 9:35 am, 4:35 pm, and 6:05 pm. These jumpscorrespond to the immediate 5‐minute time interval after the stock exchange opens, opening of electronic trades onGlobex, and the reopening of Globex after the regular maintenance shutdown.

S&P 500 Index‐Futures Price Jumps and Macroeconomic News 989

The variable jptjþ1refers to the 8:35 am or 10:05 am jump returns and SAþ

i;tj , and SA�i;tj are the

positive and negative standardized surprises of the ith macro news announcement. Theregression model fits jumps following the release of at least one announcement.

Table VII provides results sorted by positive and negative news. Positive surprises inthese variables are taken to represent stronger‐than‐expected economic growth, and negativevalues are indicative of weaker‐than‐expected economic growth. We first discuss the resultsfor 10:00 am news releases. First, examining the set of positive surprises, we find that themajority of the coefficients have a positive sign, with Consumer Confidence, Factory Ordersand Construction Spending and ISM having statistically significant impacts. For example, aone standard deviation positive surprise of ISM results in a 0.34% price jump on average

050

100150200250300350400

0:00

1:00

2:00

3:00

4:00

5:00

6:00

7:00

8:00

9:00

10:00

11:00

12:00

13:00

14:00

15:00

16:00

17:00

18:00

19:00

20:00

21:00

22:00

23:00N

umbe

r of J

umps 362 Jumps at 8:35

99 Jumps at 10:05 89 Jumps at 16:35

FIGURE 1Jumps distribution frequency at 5‐minute intervals

TABLE VJump Returns Matched with Macroeconomic News Releases

10:05 am jumps and 10:00 ammacroeconomic news releases

8:35 am jumps and 8:30 ammacroeconomic news releases

News Obs. % News Obs. %

BI 3 4.05 ARS 38 42.22CC 21 17.80 BI 6 35.29CS 17 14.17 CNP 69 76.67FO 7 5.88 CPI 51 56.67ISM 20 16.67 DGO 25 27.78LEI 3 2.52 GDP 43 47.78NHS 7 5.83 HS 27 30.00

PC 43 47.78PI 22 24.44PPI 35 38.89TBGS 12 13.33

Total jumps 99 362Jump match news 60 60.61 284 78.45

Note. BI, business inventories; CC, consumer confidence; CS, construction spending; FO, factory orders; ISM, ISM‐-manufacturing; LEI, leading economic indicators; NHS, new home sales; ARS, advanced retail sales; CNP, changes in nonfarmpayrolls; CPI, consumer price index; DGO, durable goods orders; GDP, gross domestic product; HS, housing starts; PC, personalconsumption; PI, personal income; PPI, producer price index; TBGS, trade balance goods and services.

990 Miao, Ramchander, and Zumwalt

during the 5‐minute interval immediately after the news release. Second, in the case ofnegative surprises, all coefficients that are statistically significant are found to carry a positivesign. In interpreting the c�i coefficients, it should be noted that a positive sign suggests thatworse‐than‐expected economic news leads to negative jump returns. Notably, ConsumerConfidence announcements have significant impacts. Overall the results provide clear andcompelling evidence that good (bad) economic news is followed by positive (negative) jumps.Finally, support for the model’s goodness‐of‐fit is provided by relatively large adjusted‐R2

values of 52%.Examining the 8:35 am jumps, we find that a majority of the surprises are positively

related to jumps. Among the 22 different parameters estimated 17 are positive. Notably,Changes in Non‐farm Payroll is the only announcement for which the parameters of bothpositive and negative surprises are significantly positive. The primacy of the “EmploymentSituation” report is a well‐documented phenomenon in asset markets (Andersen & Bollerslev(1998) refer to this announcement as the “king” of all announcements). Furthermore, theparameter of negative GDP surprises is positive and significant at the 5% level of significance.The results also highlight the presence of a lackluster association between several newsreleases—for example, Durable Goods Orders, Housing Starts—and price jumps. Finally, an

TABLE VITop 20 Jumps and Corresponding Macroeconomic Announcements

Rank

10:05 am jumps and 10:00 am news 8:35 am jumps and 8:30 am news

Date Return News Standard surprise Date Return News Standard surprise

1 10/29/2002 �1.11 CC �1.97 10/16/2008 1.42 CPI �0.682 1/2/2003 0.98 ISM 2.22 6/4/2010 �1.12 CNP �1.363 8/1/2002 �0.94 ISM �2.13 3/11/2008 1.09 TBGS 0.384 9/1/2010 0.86 ISM 1.65 1/4/2008 �1.00 CNP �0.685 7/11/2002 0.85 6/5/2009 0.92 CNP 2.276 8/27/2002 �0.84 CC �0.65 4/4/2008 �0.90 CNP �0.397 7/29/2003 �0.79 CC �1.56 8/19/2010 �0.878 5/26/2009 0.78 CC 2.29 10/30/2008 0.87 GDP 0.42

PC �1.979 9/24/2002 0.75 CC 0.22 3/14/2008 0.85 CPI �2.0310 1/17/2008 �0.75 5/2/2008 0.85 CNP 0.7111 8/19/2010 �0.74 LI 0.00 2/1/2008 �0.84 CNP �1.1312 2/7/2008 0.74 9/5/2008 �0.79 CNP �0.1213 9/5/2002 �0.72 FO 0.00 6/6/2008 �0.78 CNP 0.1414 11/2/2009 0.72 CS 1.24 3/7/2008 �0.78 CNP �1.08

ISM 1.2815 11/12/2001 0.68 12/5/2008 �0.78 CNP �2.5716 9/4/2001 0.66 ISM 1.84 1/9/2009 0.77 CNP 0.0117 7/1/2010 �0.64 CS 0.75 11/25/2008 0.76 GDP 0.00

ISM �1.32 PC �1.4018 6/29/2010 �0.62 CC �1.79 2/5/2009 �0.7519 6/1/2010 0.62 CS 3.36 8/17/2007 0.74

ISM 0.3320 6/23/2010 �0.62 NHS �1.58 1/31/2008 �0.73 PI 0.31

Note. CC, consumer confidence; CS, construction spending; FO, factory orders; ISM, ISM‐manufacturing; LEI, leading economicindicators; NHS, new home sales; ARS, advanced retail sales; CNP, changes in nonfarm payrolls; CPI, consumer price index;DGO, durable goods orders; GDP, gross domestic product; HS, housing starts; PC, personal consumption; PI, personal income;PPI, producer price index; TBGS, trade balance goods and services.

S&P 500 Index‐Futures Price Jumps and Macroeconomic News 991

examination of the magnitude of the coefficient values for positive and negative surprises doesnot support the presence of a strong asymmetric behavior in the price response. In general, themagnitudes of the estimates are not statistically different from each other.

5.4. Intra‐day Adjustment Process of Returns, Volatility, and Volume to News

This section examines the intra‐day adjustment process of return, volatility, and tradingvolume to macroeconomic news. The announcements are conditioned on whether or notthere is a jump in the return process at the time of the news event. Similar to Bjursell et al.,(2010), we use the following regression model:

Vti ¼ c0 þXni¼1;i≠j

ciDi þ eti ; ð16Þ

TABLE VIIMarginal Impact of Macroeconomic News on Jumps

10:05 am jumps and 10:00 am news 8:30 am jumps and 8:30 am news

News Estimate t‐Stat P‐value News Estimate t‐Stat P‐value

BIþ 0.00 ARSþ 0.06 0.97 0.33CCþ 0.35��� 3.37 0.00 BIþ 0.12 0.76 0.45CSþ 0.22�� 2.09 0.04 CNPþ 0.35��� 5.22 0.00FOþ 0.45�� 2.39 0.02 CPIþ �0.10

� �1.72 0.09LEIþ 0.19 1.14 0.26 DGOþ 0.06 0.66 0.51ISMþ 0.34��� 3.76 0.00 GDPþ 0.09 1.39 0.17NHSþ 0.12 0.63 0.53 HSþ 0.08 0.79 0.43

PCþ 0.08 0.83 0.40PIþ 0.02 0.33 0.74PPIþ �0.11� �1.69 0.09TBGSþ 0.37 1.17 0.24

BI� �0.24 �0.92 0.36 ARS� 0.17��� 2.68 0.01CC� 0.30��� 3.11 0.00 BI� �0.05 �0.40 0.69CS� 0.10 0.94 0.35 CNP� 0.27��� 5.21 0.00FO� 0.07 0.32 0.75 CPI� �0.13�� �2.06 0.04LEI� 0.27 0.80 0.43 DGO� 0.15 1.60 0.11ISM� 0.18 1.19 0.24 GDP� 0.18�� 2.25 0.03NHS� 0.20 1.40 0.17 HS� 0.09 0.91 0.36

PC� 0.00 0.05 0.96PI� �0.02 �0.13 0.90PPI� 0.03 0.27 0.79TBGS� 0.06 0.34 0.73

Obs 60 284Adj‐R2 (%) 51.52 22.43F‐value 5.82��� 4.72���

P‐value 0.00 0.00

Note. 1. ���, ��, � Statistical significance at the 1%, 5%, and 10% level, respectively.2. BI, business inventories; CC, consumer confidence; CS, construction spending; FO, factory orders; ISM, ISM‐manufacturing;LEI, leading economic indicators; NHS, new home sales; ARS, advanced retail sales; CNP, changes in nonfarm payrolls; CPI,consumer price index; DGO, durable goods orders; GDP, gross domestic product; HS, housing starts; PC, personal consumption;PI, personal income; PPI, producer price index; TBGS, trade balance goods and services.This table reports panel regression results of the following form: jpt j ¼ c þPn

i¼1 cþi SA

þi ;t j þ

Pni¼1 c

�i SA

�i ;t j þ et j . Specifically, jumps

that match at least one news announcement are regressed against the standardized surprises of news announcements 5‐minutebefore the occurrence of the jump.

992 Miao, Ramchander, and Zumwalt

where Vti alternatively denotes intra‐day return, volatility (measured by absolute returns) orvolume for the ith 5‐minute interval on day t, andDi is a dummy variable that takes a value of 1for the ith intra‐day interval and 0 otherwise. These regressions are conducted separately forGlobex and pit market transactions. In the case of Globex the interval 8:20–8:25 am is omittedfrom the corresponding regressions. For pit market transactions the omitted interval is 9:50–9:55 am. In this regression framework, c0 provides an intra‐day benchmark estimate of themean volatility or volume for the omitted (jth) time interval. Our empirical analysis isconstrained to the extent that trading volume data are available only for Globex.

The regression model is run on three samples, days with both news and jumps (Model I);days with news that are unaccompanied by jumps (Model II); and days without news and nojumps (Model III). In order to capture the influence of positive versus negative jumps,Model Iis further distinguished by days with news and positive jumps, and days with news and negativejumps. Tables VIII and IX provide results from the various regression models.

Table VIII reports the intra‐day return, volatility, and volume adjustment processes from8:30 am announcements using Globex transactions. In general, the return regression resultsshow that on days with news and jumps, the mean of returns in the 8:30–8:35 am interval(0.03%) is significantly different from the benchmark interval (�0.006%). When the jumpsare separated into positive and negative jumps, the results are much clearer. On days withnews and positive and negative jumps, the mean returns are 0.291% with T‐statistic of 23.14and �0.293% with T‐statistic of �21.62%, respectively. However, those are the onlysignificant coefficients for Model I. The coefficients in the following 5‐minute intervals arenot significant. The results indicate that the impacts of news on returns are spikes and vanishvery quickly.

In order to obtain a more granular perspective on the impact of 8:30 am news on thereturn, volatility, and volume adjustment process, Figure 2 plots the time series of the meansof return, volatility, and volume at 1‐minute intervals. The return plot shows that on days withpositive or negative jumps and news, there are big spikes in the time interval between 8:30 amand 8:31 am. The mean returns for the other intervals are very close to zero. The resultssuggest that news takes less than 1minute to be fully absorbed into prices.

The volatility regression results show that on days with news and jumps (see Model I)there is a dramatic increase in volatility in the 8:30–8:35 am interval. For example, themean ofvolatility in the benchmark (or omitted) interval is 0.041%. By comparison, the regressioncoefficient for the 8:30–8:35 am interval is about five times larger, 0.254% with a t‐statistic of31.19. In other words, the mean of volatility at 8:30–8:35 am is about 0.296%, which iscalculated as the sum of the coefficient of the intercept and the coefficient at the 8:30–8:35am interval (i.e., 0.041%þ 0.254%¼0.296%). In contrast, for days with news with no jumps(seeModel II) the average volatility for the 8:30–8:35 am interval is relatively much smaller at0.060%, and is only slightly higher than the benchmark interval at 0.41%. Not surprisingly, forthe “no news‐no jump” sample (Model III) the mean of volatility during the 8:30–8:35interval, and in fact all subsequent intervals, are found to be very close to the mean for thebenchmark 8:20–8:25 am interval. On days with news and associated jumps, the largestvolatility spikes are observed at the 8:30–8:35 am interval, followed by days with news but nojumps during the same time interval. The coefficients are significant only for the three5‐minute intervals right after 8:30 am. That is, the persistence of the impacts of news on thefutures volatility is about 15minutes. The results in columns “POS” and “NEG” show thatboth positive and negative jumps elicit a similar volatility response. The average increases (i.e.,the regression coefficients) at the 8:30–8:35 am interval on positive and negative jump daysare 0.25% and 0.26%. Figure 2 indicates that the volatility responses on news related jump andnon‐jump days persist for several periods, with the most dramatic impact registered on theimmediate 1‐minute interval after the 8:30 am announcement. Finally, and not surprisingly,

S&P 500 Index‐Futures Price Jumps and Macroeconomic News 993

TABLE

VIII

Returns,V

olatility,an

dVolum

e,Adjustm

entProcesses

intheGLOBEXMarket

Returns

Volatility

Volum

e

Model

I

Model

IIModel

III

Model

I

Model

IIModel

III

Model

I

Model

IIModel

III

All

POS

NEG

All

POS

NEG

All

POS

NEG

Intercep

t�0

.006

(�0.70

)�0

.002

(�0.26

)�0

.010

(�1.06

)0.00

0(0.06)

0.00

4�

(1.96)

0.04

1���

(7.16)

0.04

0���

(4.98)

0.04

3���

(5.21)

0.04

3���

(12.73

)0.04

1���

(22.62

)94

.285

���

(8.71)

89.465

���

(5.99)

100.24

4���

(6.40)

72.254

���

(13.49

)69

.668

���

(18.70

)8:15

–8:20

0.01

8(1.52)

0.01

8(1.46)

0.01

7(1.25)

�0.004

(�0.69

)�0

.010

��

(�3.34

)�0

.002

(�0.28

)0.00

2(0.19)

�0.008

(�0.66

)�0

.002

(�0.37

)0.00

1(0.37)

�9.928

(�0.65

)�1

2.52

2(�

0.59

)�6

.649

(�0.30

)�2

.161

(�0.28

)5.82

2(1.11)

8:25

–8:30

0.01

1(0.95)

0.00

0(0.02)

0.02

4�

(1.80)

0.00

5(0.93)

�0.006

�

(�1.85

)0.02

1���

(2.63)

0.01

7(1.47)

0.02

7��

(2.34)

0.00

5(1.09)

�0.005

�

(�1.81

)16

.718

(1.09)

10.758

(0.51)

24.087

(1.09)

8.36

3(1.10)

�6.401

(�1.22

)8:30

–8:35

0.03

0���

(2.59)

0.29

1���

(23.14

)�0

.293

��

(�21

.62)

�0.009

(�1.54

)0.00

1(0.20)

0.25

4���

(31.19

)0.24

9���

(22.00

)0.26

0���

(22.30

)0.01

7���

(3.56)

�0.002

(�0.65

)46

6.59

5���

(30.47

)48

8.75

2���

(23.14

)43

9.20

5���

(19.81

)13

1.07

0���

(17.31

)10

.733

��

(2.04)

8:35

–8:40

0.00

0(0.04)

0.00

5(0.37)

�0.005

(�0.34

)�0

.003

(�0.44

)�0

.005

(�1.57

)0.03

3���

(4.08)

0.03

0���

(2.68)

0.03

7���

(3.15)

0.01

6���

(3.41)

�0.004

(�1.42

)18

2.45

4���

(11.92

)19

9.22

9���

(9.43)

161.71

7���

(7.30)

40.331

���

(5.33)

�6.322

(�1.20

)8:40

–8:45

0.01

3(1.07)

0.00

6(0.44)

0.02

1(1.56)

0.00

2(0.35)

�0.005

�

(�1.68

)0.02

6���

(3.15)

0.01

9�

(1.70)

0.03

4���

(2.88)

0.00

8�

(1.74)

�0.005

�

(�1.89

)10

2.18

3���

(6.67)

115.72

6���

(5.48)

85.441

���

(3.85)

21.112

���

(2.79)

�7.997

(�1.52

)8:45

–8:50

0.00

8(0.71)

0.00

7(0.55)

0.01

0(0.74)

�0.000

(�0.08

)�0

.006

�

(�1.89

)0.01

1(1.39)

0.01

4(1.20)

0.00

9(0.73)

0.00

6(1.16)

�0.005

�

(�1.82

)63

.989

���

(4.18)

81.318

���

(3.85)

42.567

�

(1.92)

11.134

(1.47)

�6.911

(�1.31

)8:50

–8:55

0.01

0(0.82)

0.01

6(1.28)

0.00

1(0.10)

�0.001

(�0.18

)�0

.006

��

(�2.09

)0.01

8��

(2.20)

0.01

1(0.99)

0.02

6��

(2.26)

0.00

0(0.05)

�0.005

��

(�2.04

)33

.884

��

(2.21)

45.777

��

(2.17)

19.113

(0.86)

13.098

�

(1.73)

�6.449

(�1.23

)8:55

–9:00

�0.002

(�0.13

)�0

.004

(�0.31

)0.00

1(0.10)

�0.007

(�1.22

)�0

.003

(�1.05

)0.00

6(0.72)

0.00

3(0.27)

0.00

9(0.81)

�0.000

(�0.06

)�0

.006

��

(�2.45

)18

.874

(1.23)

23.388

(1.11)

13.291

(0.60)

2.76

7(0.37)

�4.538

(�0.86

)9:00

–9:05

0.00

4(0.34)

0.00

2(0.18)

0.00

6(0.45)

0.00

5(0.83)

�0.007

��

(�2.14

)0.00

7(0.83)

0.00

6(0.49)

0.00

8(0.70)

0.00

7(1.49)

�0.001

(�0.36

)23

.757

(1.55)

31.920

(1.51)

13.693

(0.62)

8.67

9(1.15)

6.25

2(1.19)

9:05

–9:10

0.00

4(0.30)

�0.001

(�0.09

)0.00

9(0.68)

0.00

3(0.50)

�0.008

��

(�2.66

)0.00

5(0.67)

0.01

0(0.87)

0.00

0(0.01)

�0.001

(�0.22

)�0

.006

��

(�2.45

)19

.644

(1.28)

25.000

(1.18)

13.012

(0.58)

5.45

1(0.72)

�1.836

(�0.35

)9:10

–9:15

0.00

8(0.65)

�0.000

(�0.04

)0.01

7(1.29)

�0.003

(�0.57

)�0

.005

�

(�1.68

)0.00

4(0.43)

0.00

6(0.53)

0.00

1(0.04)

�0.000

(�0.03

)�0

.007

��

(�2.53

)9.30

6(0.61)

15.401

(0.73)

1.77

2(0.08)

16.881

��

(2.23)

�0.941

(�0.18

)Adj‐R

20.00

0.33

0.38

0.00

0.00

0.33

0.30

0.36

0.01

0.00

0.33

0.34

0.32

0.09

0.00

FStat.

1.15

87.13

83.87

1.17

1.99

150.97

75.05

77.90

3.75

2.04

152.55

87.98

64.86

46.10

2.75

Note.

��� ,

��,�

Statistical

sign

ifica

nceat

the1%

,5%,a

nd10

%leve

l,resp

ectively.

Thistablerepo

rtstheresu

ltsof

theregres

sion

mod

el:V

t i¼

c 0þP n i¼

1;i≠

jc iD

iþe t

i,whe

re,the

5‐minutereturn,volatility(m

easu

redby

theab

solute

returns),a

ndvo

lumeareregres

sedag

ains

tinterva

ldu

mmieson

diffe

rent

sub‐sa

mples

,res

pectively.

Spe

cifica

lly,M

odelsI–III

areon

days

with

newsan

d8:35

jumps

,day

swith

newsan

dno

8:35

jumps

,and

days

with

none

wsan

dno

8:35

jumps

.

994 Miao, Ramchander, and Zumwalt

TABLE

IXReturns

andVolatility

Adjustm

entProcesses

inthePitMarket

Returns

Volatility

Model

I

Model

IIModel

III

Model

I

Model

IIModel

III

All

POS

NEG

All

POS

NEG

Intercep

t�0

.006

(�0.23

)0.02

7(0.84)

�0.028

(�1.13

)�0

.007

(�1.19

)�0

.005

(�1.59

)0.08

3���

(5.67)

0.09

6���

(3.89)

0.07

4���

(4.14)

0.09

6���

(22.70

)0.10

6���

(42.03

)9:45

–9:50

�0.006

(�0.17

)�0

.018

(�0.40

)0.00

2(0.04)

0.01

1(1.36)

0.00

1(0.20)

0.00

9(0.44)

�0.010

(�0.29

)0.02

2(0.86)

0.00

9(1.46)

�0.001

(�0.36

)9:55

–10

:00

0.03

7(1.00)

�0.006

(�0.13

)0.06

6�

(1.88)

0.02

7���

(3.21)

�0.001

(�0.25

)0.00

7(0.36)

�0.022

(�0.64

)0.02

7(1.08)

�0.005

(�0.90

)�0

.006

(�1.63

)10

:00–

10:05

�0.111

��

(�3.00

)0.42

8���

(9.59)

�0.471

��

(�13

.53)

0.01

7��

(2.00)

0.00

7(1.54)

0.39

8���

(19.28

)0.35

9���

(10.27

)0.42

5���

(16.78

)0.04

2���

(7.11)

0.01

1���

(2.99)

10:05–

10:10

�0.014

(�0.36

)�0

.018

(�0.41

)�0

.010

(�0.30

)�0

.008

(�1.02

)0.00

6(1.25)

0.04

7��

(2.25)

0.02

0(0.58)

0.06

4��

(2.53)

0.02

0���

(3.33)

�0.004

(�1.12

)10

:10–

10:15

0.01

0(0.28)

�0.021

(�0.47

)0.03

1(0.89)

0.00

8(0.99)

0.00

3(0.57)

0.02

4(1.17)

�0.000

(�0.00

)0.04

0(1.60)

0.01

1�

(1.92)

�0.008

��

(�2.30

)10

:15–

10:20

0.02

5(0.67)

0.05

0(1.12)

0.00

8(0.23)

�0.003

(�0.36

)0.00

6(1.20)

0.03

4�

(1.66)

0.06

6�

(1.88)

0.01

3(0.53)

0.00

4(0.67)

�0.009

��

(�2.48

)10

:20–

10:25

�0.037

(�0.99

)�0

.090

��

(�2.02

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S&P 500 Index‐Futures Price Jumps and Macroeconomic News 995

our results show that the response of volatility is hardly perceptible on days with no news andno jumps.

Table VIII also presents the volume adjustment process to 8:30 am news announce-ments using Globex transactions. In general, the results suggest a contemporaneous andpositive relationship between volatility and volume; however, the elevation in volume(particularly, on news days with positive jumps) seems to persist longer than thecorresponding volatility response. For example, on news related jump days (see Model I),the mean of SP volume in the immediate 5‐minute interval after 8:30 am news releases isfound to be nearly 467 contracts larger than the corresponding volume in the 8:20 am–8:25am benchmark interval. By comparison, for days with news but no related jumps (Model II)

-0.25-0.2

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FIGURE 2One‐minute intra‐day return, volatility, and volume adjustment process in the globex market

Note. “Pos” for days with positive jump and news; “Neg” for days with negative jumps and news; I is fordays with news and jumps; II is for days with news but no jumps; III is for days with no news and nojumps.

996 Miao, Ramchander, and Zumwalt

the average increase in volume is only 131 contracts. These results are corroborated in thesecond graph in Figure 2 which plots the mean of volumes at 1‐minute intervals. The graphsshows large volume spikes on days with both news and jumps at the time of the announcementfollowed by a smooth reversal process to pre‐announcement levels.

Table IX uses pit market transactions to report intra‐day, 5‐minute, return and volatilityresponse behavior in response to 10:00 am news announcements.7 The 1‐minute means ofreturn and volatility are presented in Figure 3. The results show that the return response issimilar to Globex. For days with jumps, there are return spikes in the first 1‐minute and themeans revert to normal (close to zero). These results along with the 1‐minute plots in Figure 3indicate that there is a distinct surge in volatility in the immediate post‐announcementinterval. The increase in volatility is relatively more pronounced than those associated with8:30 am announcements. Furthermore, in contrast to Globex, there is some indication thatthemean of volatility on days with news and jumps subsides to pre‐announcement levels fasterthan on days with news but no corresponding jumps, with negative jumps lasting a bit longerthan positive jumps on news days.

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FIGURE 3One‐minute intra‐day return and volatility adjustment process in the pit market

Note. “Pos” for days with positive jump and news; “Neg” for days with negative jumps and news; I is fordays with news and jumps; II is for days with news but no jumps; III is for days with no news and nojumps.

7Since volume data are not available for pit transaction, we apply the price adjustment model on two alternativeliquidity measures introduced by Roll (1984) and Bao, Pan, andWang (2011). There is evidence, albeit weak, that fordays with news and corresponding jumps liquidity increases (or illiquidity decreases) in equity futures. These resultsare available from the authors upon request.

S&P 500 Index‐Futures Price Jumps and Macroeconomic News 997

5.5. Price Response in Open Outcry Versus Electronic Trading

As alluded to earlier, our choice of the regular S&P 500 futures contract provides a uniquewindow into how markets process information under open outcry and electronic tradingsystems. Specifically, we seek to provide insights on price reaction to the same set of newsannouncements along two key dimensions: (a) jump frequencies of SP prices for the period2001–2003 (before ES trading dominated SP) and 2004–2010 (a period characterized by thedominance of ES); and (b) jump returns in SP (traded on the pit) and ES (traded on Globex).In order to draw meaningful inferences we focus on the 10:00 am announcements that arereleased during regular trading hours and when both pit (SP) and Globex (ES) markets areopen.

Figure 4 shows the number of jumps of both SP and ES during regular trading hours,9:30 am to 4:15 pm, at 5‐minute intervals. We notice that, in general, the jump frequencies inSP contracts are slightly lower for the post‐2003 period, a period marked by the dominance ofE‐mini futures. For both sub‐periods, we observe significantly more jumps in the 10:00 am to10:05 am time interval than other intervals. More specifically, we find that there are a total of41 price jumps during 2001–2003 recorded at the 10:00 am–10:05 am, and 58 jumps duringthe same 5‐minute interval for the period 2004–2010.8

To provide an alternative perspective on the role of trading platforms we compare theimpact of 10:00 am news on jump returns in SP (traded on the pit) and ES (traded onGlobex).Figure 5 provides this comparison.We notice that, in contrast to the earlier period, since 2004the average jump size (absolute jump returns, %) for SP is slightly lower than ES. Overall, thecombined evidence suggests that the greater volume and liquidity of E‐mini since 2004 seemsto have had a perceptible effect on both the frequency and size of price jumps to newsannouncements on SP contracts.

0

10

20

30

40

50

60

70

9:30

10:00

10:30

11:00

11:30

12:00

12:30

13:00

13:30

14:00

14:30

15:00

15:30

16:00

16:30

2001-2003

2004-201041 (2001-2003) and 58 (2004-2010) jumps at 10:05

FIGURE 4Jump distribution frequency at 5‐minute intervals during regular trading hours for the sub‐periods,

2001–2003 and 2004–2010

8We also examine the marginal impact of individual news announcements released at 10:00 am on the immediate5‐minute returns for SP for each of the two sub‐periods. Results indicate that during the 2001–2003 periods, 24 out ofthe 41 jumps between 10:00 am to 10:05 am can be connected to 10:00 am announcements whereas 36 out of the 58jumps between 10:00 am and 10:05 am during the post‐2003 period are caused by 10:00 am announcements.For both periods, the influential announcements are Consumer Confidence, Factory Orders, and thexxxISM‐Manufacturing. These results may be obtained from the authors upon request.

998 Miao, Ramchander, and Zumwalt

6. CONCLUDING REMARKS

The study examines the relationship betweenmacroeconomic news and intra‐day price jumpsin the S&P 500 equity futures index. Several important results are evident. First, we documenta strong correspondence between the two sets of morning economic news releases and jumpsin S&P 500 index futures prices. Over 60% of the jumps between 10:00 am and 10:05 am andover 75% of the jumps between 8:30 am and 8:35 am are related to one or more news itemsreleased at 10:00 am and 8:30 am, respectively, and not surprisingly news share a pro‐cyclicalrelationship with jumps. Second, among the 8:30 am and 10:00 am announcements,Non‐farm Payroll and Consumer Confidence respectively are most significantly related tojumps. The influential role of positive surprises in GDP, PPI, and Factory Orders, andnegative surprises in CPI and Advanced Retail Sales are also noted. Fourth, there is a sharpsurge in both volatility and volume in the immediate post‐announcement period on days withscheduled news and corresponding jumps. For instance, for the 8:30 am (10:00 am)announcements the mean of the absolute returns for each equity index product during theimmediate 5‐minute post‐announcement interval is about 5 times (3.5 times) higher on newsdays with related jumps compared to news days without jumps. In the case of the pit market,the post‐announcement volatility adjustment process is faster on days with news and jumpsthan on days with news but no jumps. We also document a rapid adjustment of returns,volatility, and volume to macro news events. Finally, we document differential price reactionrelated to the dominance of electronic trading of S&P 500 equity index futures.

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0.0

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ES

SP

FIGURE 5Average news‐related 10:05 am jump sizes of SP and ES

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S&P 500 Index‐Futures Price Jumps and Macroeconomic News 1001