Trading platform, market volatility and pricing ef
ficiency in the floor-traded
and E-mini index futures markets
Huimin Chung
a, Her-Jiun Sheu
b, Shufang Hsu
c,⁎
a
Graduate Institute of Finance, National Chiao Tung University, Taiwan
bDepartment of Banking and Finance, National Chi Nan University, Taiwan
cDepartment of Management Science, National Chiao Tung University, No. 1001, Ta-Hsueh Road, Hsinchu 30050, Taiwan
a r t i c l e i n f o
a b s t r a c t
Article history: Received 5 May 2009
Received in revised form 4 January 2010 Accepted 31 March 2010
Available online 24 April 2010
This study examines the pricing efficiency of E-mini and floor-traded index futures under electronic versus open-outcry trading platforms. By using OLS and quantile regressions to control for changes in market characteristics, wefind that pricing errors are smaller in the E-mini markets than thefloor-traded markets, thereby confirming that electronic trading has special attractions for arbitrageurs and informed traders. However, during periods of higher volatility, the advantages of speedier execution, anonymity and information efficiency may be offset by arbitrage risks; as a result, larger pricing errors are observed in the E-mini markets. We provide new evidence confirming the important roles in pricing efficiency played by both traditional open-outcry systems and electronic trading systems.
© 2010 Elsevier Inc. All rights reserved.
JEL classification: G12 G13 Keywords: E-mini futures Floor-traded futures Pricing efficiency Noise trader risk
1. Introduction
With electronic trading having become a mainstream mechanism in globalfinancial markets, many futures exchanges have now made the move from open-outcry to electronic trading systems.1Although such movement seems all but inevitable, the
majority of futures trading in the US remainsfloor-based; indeed, despite the potential loss of their scale economy advantage, as opposed to making the complete transition from open-outcry to electronic trading, some of the futures exchanges clearly prefer to offer parallel trading platforms.2
This therefore raises quite an intriguing question as to whether an open-outcry system is an indispensable form of trading in derivatives. This study aims to clarify the argument by examining the pricing efficiency of index futures markets featuring E-mini (electronically-traded) and traditional (floor-traded) contracts. Although several analyses have been undertaken on price
⁎ Corresponding author. Tel.: +886 3 5712121x57103; fax: +886 3 5713796. E-mail address:shufang.ms92g@nctu.edu.tw(S. Hsu).
1
Including the Marche a Terme International de France (MATIF), the Sydney Futures Exchange (SFE) and the London International Financial Futures Exchange (LIFFE).
2Over recent years, some exchanges have gradually begun offering a choice between trading derivatives products on an electronic trading platform, an
open-outcry market, or a combination of both; for example, the Chicago Board of Trade (CBOT), the Chicago Mercantile Exchange (CME), the New York Mercantile Exchange (NYMEX) and the Singapore Exchange (SGX) have all made such shifts, with trading beginning with S&P 500, Nasdaq-100 and DJIA index futures, and Russell 1000, Russell 2000, S&P MidCap 400, S&P MidCap 600 and Euro FX products having subsequently been introduced.
1059-0560/$– see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.iref.2010.03.007
Contents lists available atScienceDirect
International Review of Economics and Finance
discovery and price clustering under coexisting electronic and open-outcry futures markets,3pricing efficiency relating to both
trading platforms has yet to be examined in thisfield.4
In the index futures markets of the US, both electronic and open-outcry trading systems have been retained, operating simultaneously during regular trading hours. This unique market mechanism offers a natural experimental environment in which to directly compare the differences in pricing efficiency between electronic and open-outcry trading systems.5We therefore
examine pricing errors in active trading on the Dow Jones Industrial Average (DJIA) and Nasdaq-100 indices, where both E-mini andfloor-traded futures are simultaneously traded, and where their trade prices are generally found to have a high degree of correlation. The transactions in these contracts are, nevertheless, dealt with under different trading systems; this uncommon contrast, in terms of the trading systems used, provides us with a unique opportunity to examine the real effects on pricing errors based upon the trading system used, whilst removing the influences of any changes in market conditions. Such comparisons between E-mini and floor-traded futures should prove to be particularly informative, and may well contribute to our understanding of causes of differences in pricing efficiency between the electronic trading and the open-outcry platforms with consideration of market volatility.
We provide several contributions to the extant literature in the present study. Firstly, the differences in pricing efficiency between coexisting E-mini andfloor-traded index futures markets are analyzed from the perspective of arbitrage trading. This is an area which has received relatively little attention in the prior studies.6With bothfloor-traded and E-mini contracts
increasingly being offered by futures exchanges around the world, they have largely become accepted over time. Nevertheless, no examination has yet been undertaken on the differences in pricing efficiency attributable to the variations in the two trading platforms.7
Our second contribution is our test of the influence of market volatility on pricing efficiency for electronically-traded versus floor-traded index futures at different quantile levels. By controlling for the influence of market characteristics on pricing efficiency, the quantile regressions can reveal the entire distribution of the differences in the magnitude of mispricing between E-mini andfloor-traded index futures.8With additional dummy variables representing high market volatility, we can observe the
specific effects of high market volatility on mispricing differences between E-mini and floor-traded index futures across various quantile levels.
Our third contribution is the alternative explanation supporting the existence of open-outcry markets. The liberty of trading in parallel electronic trading and open-outcry platform in the US futures markets provides us with an opportunity to test the limit-to-arbitrage argument, and thereby, to propose a reasonable explanation for the phenomenon that efficiency of the electronic trading relative to the open-outcry system deteriorates during periods of high volatility.
The empirical results of our study indicate that the average pricing efficiency found in the E-mini markets is superior to that found infloor-traded markets, a result which may be explained by the characteristics of E-mini futures contracts; that is, by reducing pricing errors and pushing the market prices towards equilibrium, the speedier execution which is characteristic of electronic trading systems may help arbitrageurs to contend with the latent risk when executing their arbitrage trades. However, our results also show that with high volatility, more serious attacks occur on the pricing efficiency of an electronic trading system than that of an open-outcry trading system, afinding which indicates that the impact on an electronic trading system arising from noise trader risk is higher during periods of high volatility than during normal periods. Periods of high volatility will induce more noise traders to trade in the markets because they believe that profits are more easily made from such market timing. Both systems admittedly fulfill an important role in pricing efficiency; it is, however, also clear that, despite the electronic trading system having some special attractions for arbitrageurs and informed traders, in terms of enhancing pricing efficiency, these important advantages are offset by the influences of arbitrage risks (noise trader risk in particular) during periods characterized by higher market volatility.
The remainder of this paper is organized as follows. A review of the related literature is presented inSection 2, along with the development of our empirical hypotheses.Section 3 presents the data and methodology, including a description of the data sources and the research methodology adopted for this study.Section 4reports the empirical results, followed inSection 5by our presentation of the conclusions drawn from this study.
3
Examples includeKurov and Lasser (2004), Ates and Wang (2005) and Chung and Chiang (2006).Ates and Wang (2005)find that operational efficiency and relative liquidity jointly determine the rate of price discovery in electronic trading versus open-outcry trading systems, whilstChung and Chiang (2006)also point to the increased occurrence of price clustering in open-outcry markets due to the higher levels of human participation.
4
Although research has been undertaken comparing the market characteristics of E-mini andfloor-traded index futures markets–includingPirrong (1996), Kofman and Moser (1997), Frino, McInish, and Toner (1998) and Franke and Hess (2000)–such analyses have tended to be confined to comparisons between the various markets in different countries, such as the DTB and the LIFFE.
5
Trading in E-mini index futures on the DJIA by the Chicago Board of Trade (CBOT) began on 4 April 2002; thereafter, the three most actively traded contracts (DJIA, S&P500 and Nasdaq-100 index futures) could be traded in both the E-mini andfloor-traded futures markets.
6
There are only a few isolated examples of examinations of the trade characteristics of electronic E-mini and correspondingfloor-traded futures indices over recent years; these are, essentially,Hasbrouck (2003), Ates and Wang (2005), Chung and Chiang (2006) and Kurov (2008).
7
AlthoughCheng, Fung, and Tse. (2005)examine the effects of a switch to electronic trading on relative pricing efficiency, their event focuses on a “before and after” analysis of a single market over different time periods. Similar studies are provided byTse and Zabotina (2001) and Gilbert and Rijken (2006).
8We follow the method ofChen, Chou, and Chung (2009)in which an investigation is undertaken into the impact of decimalization on overall pricing
2. Literature review and hypothesis development 2.1. Pricing efficiency in electronic and open-outcry systems
The comparison between electronic and open-outcry systems has attracted considerable research attention, with the prior studies having summarized the advantages to demonstrate that an electronic trading system is more operationally and informationally efficient than an open-outcry system. Proponents of electronic trading systems argue that the benefits of such trading include greater speed and accuracy of processing transactions, timely and accurate reporting offills, improved pricing transparency, higher liquidity and anonymity.9Conversely, the speed of execution in open-outcry markets is limited by the
mechanics of pit trading, with the process of order submission, execution and relaying the trade information to the customer potentially taking several minutes, particularly during periods of high activity.
The argument that an electronic trading system appears to play a more important role in the price discovery process than the open-outcry system has been generally accepted; furthermore, advocates of the screen-based system also contend that this enhances price discovery. By examining the intraday transaction data of the coexisting E-mini and traditional index futures markets, the empirical evidence discussed inKurov and Lasser (2004) and Ates and Wang (2005)provides support for the argument that an electronic trading system plays a more important role in the price discovery process than an open-outcry system.
Ourfirst hypothesis is motivated by the results from the extant market microstructure literature. In the present study, we enlarge upon the prior price discovery studies to conduct a parallel test of our hypothesis in the E-mini andfloor-traded futures markets. If the operational advantages of an electronic trading system encourage arbitrageurs to execute their transactions in the markets, then E-mini index futures may well have lower pricing errors thanfloor-traded index futures. Accordingly, the first hypothesis proposed in this study is as follows:
Hypothesis 1. E-mini index futures have lower pricing errors thanfloor-traded index futures. 2.2. The effects of noise trading on pricing efficiency
The execution speed of afloor-traded system is essentially limited by the relative mechanics of pit trading, since it can take several minutes to process order submission, execution and information conveyance, which can clearly cause delays in transactions, particularly during periods of high activity in the exchanges (Kurov & Lasser, 2004; Ates & Wang, 2005). An alternative viewpoint, however, is that an open-outcry system will be more efficient in a rapidly moving market due to the way in which prices are released;
Martens (1998) demonstrates the importance of competing market markers in an open-outcry market, indicating that the
disadvantage of the electronic order book lies in its slackness in changing prices, particularly in rapidly moving markets.10
Clearly, the shift in the futures markets from open-outcry to electronic trading is justified in the vast majority of the market microstructure literature; nevertheless, the recent behavioral literature prompts us to rethink the rationality of the coexistence of electronic trading andfloor-traded platforms from an arbitrage standpoint. The limit-to-arbitrage literature features noise traders who either deal on the basis of mistaken information on fundamentals, or trade on rules in the spirit of technical analysis (such as examining historical security prices).11Whilst the motives of these noise traders are still indefinite, the momentum trading behavior or contrarian trading strategy of such traders may affect the willingness of arbitrageurs to compete with/against them, thereby impeding the recovery of price divergence.12
Noise traders act as if they have valuable fundamental information when they do not, with such overconfident characteristics inspiring them to increase their transactions during periods of higher market volatility on the basis of their belief that profits can be more easily made from such market timing. Consistent with such assumptions, in their recent investigation of the laboratory market,Bloomfield, O'Hara, and Saar (2009)find that the addition of noise traders into the markets actually leads to a dramatic increase in trading volume, particularly when the fundamental value of the security is far from its prior expected value.
Those trading under an electronic system may suffer higher noise risk than those trading in an open-outcry market; indeed,
Chelley-Steeley (2005)concludes that the introduction of an electronic trading mechanism leads to an increase in noise risk. In
addition, as noted byKurov and Lasser (2004), whereas large institutional traders may still actively trade in the traditional contracts, trading in the E-mini market is dominated by small retail traders who simply do not have sufficient capital to trade full-size contracts.13This characterization is consistent with the argument ofBlack (1986), who indicated that noise traders prefer
low-priced stocks to high-priced ones. Furthermore,Tu and Wang (2007)also empirically demonstrate that the small size of futures contracts attracts relatively ignorant noise traders, leading to greater pricefluctuations caused by noise trading.
9See for example,Pirrong (1996), Frino et al. (1998), Franke and Hess (2000), Kurov and Zabotina (2005) and Fung, Lien, Tse, and Tse (2005).
10Franke and Hess (2000)also suggest that the contribution made by electronic trading systems to information sharing is relatively greater during quiet
periods than during highly volatile periods.
11
Examples includeDe Long, Shleifer, Summers, and Waldman (1990), Shleifer and Summers (1990), Shleifer and Vishny (1997), Shleifer (2000) and Barberis and Thaler (2003).
12
Barber, Odean, and Zhu (2009)document informed trader faces some risks (such as information risk, fundamental risk and noise trader risk) when they execute their arbitrage strategy and thereby create losses for the investor whose trading horizon is short or whose cost of carrying a short position is high.
13Kurov and Lasser (2004)argue that E-mini trading has higher transaction costs for larger traders. As a result, they expect tofind trading in the E-mini market
being dominated by small retail traders who simply have insufficient capital to trade in the full-size contracts, whereas large institutional traders may still actively trade in the lower costfloor-traded contracts.
The formulation of our second hypothesis, that during periods of high market volatility, E-mini index futures suffer greater deterioration in pricing efficiency than floor-traded index futures, is based upon the foregoing analysis. This hypothesis extends the prior research into the relative damage to pricing efficiency caused by electronic trading platforms in rapidly moving markets, a phenomenon whichDe Long et al. (1990)attributed to the impact of noise traders in the market.14We note that one of the
advantages of electronic trading systems is that they attract greater execution of transactions by arbitrageurs in the E-mini markets, thereby accelerating the recovery in price divergence; however, in periods of high market volatility, the potential for even greater disturbance caused by noise trader behavior may severely dampen any desire to engage in arbitrage trading, thereby impeding the recovery in price divergence. Accordingly, the second hypothesis proposed in this study is as follows:
Hypothesis 2. E-mini index futures suffer greater deterioration in pricing efficiency than floor-traded index futures during periods of high market volatility.
3. Data and research methodology 3.1. Data and sample
The sample data used in this study comprises of allfloor-traded index futures and E-mini index futures traded on the DJIA and Nasdaq-100 indices.15The contract specifications of our dataset are provided inTable 1, which shows that the Dow Jones E-mini
andtraded index futures are all traded at a minimum tick size of 1.0 futures index point. Similarly, the tick size of the floor-traded index futures and E-mini index futures in the Nasdaq-100 are also equally scaled, at 0.5 futures index point. We can therefore examine the real effects of the trading systems on pricing efficiency by focusing our research on the sample of index futures traded on both the electronic and open-outcry markets under the same tick rules.16
Our sample period runs from 1 May 2002 to 30 April 2005, covering the three-year period after the CBOT initiated the trading of E-mini futures on the DJIA index; only the nearby futures contracts are selected for our analysis. We employ the tick-by-tick transaction data on E-mini andfloor-traded index futures to examine and compare the pricing efficiency of the electronically-traded and open-outcry index futures markets, obtaining the index futures prices and trading volume from the intraday database of Tick Data Inc., with the risk-free rate being substituted by the three-month T-Bill rates.17All other variables, such as the dividend
rates on the DJIA and Nasdaq-100 indices, are obtained from the OptionMetrics database.
We adopt certain data processing principles in this study in order to ensure the accuracy of our sample data; all trades that are out of time sequence from 8:30 a.m. to 3:00 p.m. (Chicago time) are deleted,18as are those where the trade prices are equal to, or
less than, zero. FollowingHuang and Stoll (1996), we further minimize data errors by eliminating trades meeting the following additional criteria: (i) all trades which took place either before the market opened or after it closed; and (ii) all trade prices with consecutive absolute relative changes (absolute returns) of more than 10%.
In order to explore both the magnitude and the sustained effects of the pricing errors in the two markets, we match the prices of each reported spot index and the respective futures contract (E-mini index futures andfloor-traded index futures) with the
Table 1
Contract specifications for floor-traded and E-mini index futures⁎.
Futures indices Specifications Floor-traded index futures E-mini index futures Dow Jones Date offirst trade 6 October 1997 4 April 2002
Contract size 10⁎ Dow Jones futures value 5⁎ E-mini Dow Jones futures value Minimum tick size and pricefluctuation 1 futures index point, US$10 1 futures index point, US$5 Trading hours 7:20 a.m. to 3:15 p.m. Virtually 24 h
Nasdaq-100 Date offirst trade 10 April 1996 21 June 1999
Contract size 100⁎ Nasdaq-100 futures value 20⁎ E-mini Nasdaq-100 futures value Min. tick size and pricefluctuation 0.5 futures index point, US$50 0.5 futures index point, US$10 Trading hours 8:30 a.m. to 3:15 p.m. Virtually 24 h
Note: *The sample period runs from 1 May 2002 to 30 April 2005, with trading being examined in Chicago time.
14
De Long et al. (1990)demonstrate that the risk associated with the unpredictability of the opinions of unsophisticated investors significantly reduces the attractiveness of arbitrage, such that noise trading can lead to considerable divergence between market prices and fundamental value.
15In the index futures markets of the US, both electronic trading andfloor-traded index futures contracts are traded side by side during regular trading hours.
This uncommon contrast, in terms of the trading systems used, provides us with a unique opportunity to examine the real effects on pricing errors stemming from a particular trading system, whilst removing the influences of any changes in market conditions.
16
The three most actively traded futures in the US equity index futures markets are Dow Jones, Nasdaq-100 and S&P 500 futures. The E-mini andfloor-traded index futures traded on the Dow Jones and Nasdaq-100 are all equally scaled, at 1.0 and 0.5 futures index points, respectively; however, the tick size of the floor-traded index futures and electronically-floor-traded index futures in the S&P 500 are floor-traded at minimum tick sizes of 0.1 and 0.25 futures index points each; this sample is therefore excluded from the present study.
17The data on T-Bill rates is available from website:www.federalreserve.gov/releases/h15/data.htm.
18In order to ensure the accuracy of our sample data, we only analyze the tick-by-tick transaction data from 8:30 a.m. to 3:00 p.m. (Chicago time) each trading
most recent (trading time) futures trade prices in order to form trading pairs.19Given that the information onfloor-traded and
E-mini index futures is provided for various time horizons (Table 1), pricing efficiency is considered only for those cases where both the futures and the underlying assets can be analyzed simultaneously, that is, from 8:30 a.m. to 3:00 p.m. (Chicago time). Although the price series are not uniformly spaced over time, it is clear that relative pricing efficiency should be compared over a standardized time interval; we therefore adopt 5-minute data which is formed by the tick data within the 5-minute interval. The calculations of the data on each 5-minute interval (for the dependent and independent variables in our regression analysis in
Table 5) use all tick data within the same time interval.20
3.2. Methodology
We begin by using the cost-of-carry model to calculate the fair value (Ft⁎) of an index futures contract at time t:
Ft*= Idxte r−q
ð Þ T−tð Þ; ð1Þ
where Idxtis the value of the index at time t; r is the risk-free interest rate; q is the dividend yield on the stock index portfolio;21
and T is the expiration day of the index futures contract.
The pricing error, or mispricing, Zt, is defined as the deviation in the actual futures price (Ft) from its theoretical value (F⁎) att
time t.22
Zt=jFt−Ft*j; ð2Þ
The magnitude of the pricing error may reflect the pricing efficiency of the index futures; if the futures market is efficient, the market price of a futures contract (Ft) will be equal to its theoretical value (Ft⁎) and will therefore push the pricing error
closer to zero. However, within the open-outcry and electronic trading markets, differences exist between certain factors such as transaction costs, price discreteness, exchange price rules and arbitrage risks (noise trader risk in particular) which make the price less efficient; these differences have further distinct effects on the formation of pricing errors within the two markets.
It has been argued in several of the prior studies that the existence of transaction costs and other types of market friction allows the futures prices tofluctuate within the no-arbitrage boundary:
Idxteðr−qÞ T−tð Þ h i 1−C ð ÞbFtb Idxteðr−qÞ T−tð Þ h i 1 + C ð Þ ð3Þ
where C represents the total transaction costs involved in executing arbitrage; and Ftis the market price of the index futures at
time t.
Once the futures price goes beyond the no-arbitrage boundary depicted in Eq. (3), it may trigger the simultaneous trading of spot and futures by arbitrageurs in pursuit of profits, with the actions of these arbitrageurs ultimately leading to a recovery from the earlier price divergence. In order to determine which trading mechanism may lead to a recovery from the earlier price divergence, we adopt an approach similar to that used in the prior studies, measuring the no-arbitrage violation at different levels of transaction costs.23The one-way transaction cost levels used in our analysis are set at 0.2% to 0.4% of the theoretical futures
price, at 0.05% increments. Given that no-arbitrage boundary violations tend to occur in clusters, we use the method suggested by
Chu and Hsieh (2002)to compute the occurrence of ex-post no-arbitrage violations, and further compare the pricing efficiency of
the coexisting open-outcry and electronic trading futures markets.24
We also compute the average maximum signal size within an occurrence by calculating the average of the maximum deviation in all no-arbitrage violations, and then observe the degree of boundary violation deviation attributable to the behavior of traders
19
The futures prices and their spot index are synchronized using the MINSPAN procedure suggested byHarris, McInish, Shoesmith, and Wood (1995). Every reported index is matched with the trading price of an E-mini future so as to form trading pairs; if a futures trade occurs at exactly the same time in the reported index, then a pair is formed, whereas if no futures trades occur at exactly the same time in the reported index, the futures trades within the prior and subsequent 7-second periods are then considered. When only one futures trade meets this criterion, a pair is formed. If both leading and lagging futures trades are obtained, the closer of the two trades is used to form the pair with the other trade being discarded. Our empirical results, presented inTables 3 and 4, are calculated using pair data synchronized under the MINSPAN procedure.
20The qualitatively similar empirical results are obtained from an examination using variables defined over 10 min and 30 min intervals. 21
The continuous dividend yield data is obtained from the OptionMetrics database.
22We follow the prior studies on pricing efficiency, including
Draper and Fung (2002) and Cheng et al. (2005), disregarding the signs of the magnitude (absolute value) of the errors.
23SeeChung (1991), Yadav and Pope (1994) and Chu and Hsieh (2002).
24InChu and Hsieh (2002)the potential overestimation of actual opportunities for arbitrage is avoided by integrating all mispricing occurring within a
been unable to further discriminate between the arbitrage risks that may be attributable to different market participants. This clearly provides a topic for further research based upon the availability of more detailed data.
Acknowledgement
We are grateful to the valuable comments of two anonymous reviewers, the editor, and seminar participants at the 2009 Annual Conference of Central Taiwan Finance Association (CTFA). We also deeply appreciate Wei-Peng Chen for his research assistance. Huimin Chung would like to gratefully acknowledge thefinancial support provided by the MoE ATU Plan of the National Chiao Tung University.
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