www.elsevier.comrlocatereconbase
Information diffusion in electronic and floor
trading
Gunter Franke, Dieter Hess
¨
)Department of Economics, Center of Finance and Econometrics, UniÕersity of Konstanz,
Fach D147, D-78457 Konstanz, Germany
Accepted 14 November 2000
Abstract
The attractiveness of floor trading versus anonymous electronic trading systems for traders is analysed. We hypothesize that in times of low information intensity, the insight into the order book of the electronic trading system provides more valuable information than floor trading, but in times of high information intensity, this is not true. Thus, the electronic system’s market share in trading volume should decline when information intensity increases. This hypothesis is tested by DTB and LIFFE data on Bund-Future trading in the period 1991 to 1995. In the first years of trading, the DTB’s market share is inversely related to price volatility and trading volume as proxies for information intensity. In recent years, this relation fades away; this can be explained by the high frequency of
transactions which implies a steady flow of information on transactions.q2000 Elsevier
Science B.V. All rights reserved.
Keywords: LIFFE; DTB; Floor trading
)Corresponding author. Tel.:q49-7531-88-3164; fax:q49-7531-88-3559.
Ž .
E-mail addresses: [email protected] G. Franke , [email protected]
ŽD. Hess ..
0927-5398r00r$ - see front matterq2000 Elsevier Science B.V. All rights reserved.
Ž .
1. Introduction
The rapid development of electronic communication has led to a profound restructuring of exchange-based security trading. Electronic trading systems have attracted a rapidly growing share in trading. The overall cost of electronic trading systems is lower than that of floor-based systems. In addition, electronic trading systems permit remote access of traders. Yet, the coexistence of floor and electronic trading systems indicates that each system has its strengths and weak-nesses. The purpose of this paper is to provide some insight into this issue by analyzing information diffusion in both systems. The quality of information diffusion is important because it affects traders’ ability to react quickly to new information and to protect themselves against adverse trading with insiders. Therefore, information diffusion should affect trading activities in both systems.
The variety of existing screen-based trading systems as well as of floor trading systems makes it impossible to define each system precisely. Yet most electronic systems share one important feature which is the anonymity of trading, i.e. names of traders do not appear on the screen.1 Information diffusion related to names and observable behavior of traders is lacking in electronic trading systems. Floor trading systems provide this information. All traders can observe and talk to each other.2 Anonymous electronic trading systems try to make up for this lack of
information by offering traders insight into the limit order book. Most floor trading systems do not reveal this information.
As both trading systems differ in many features, it is impossible to ascribe a decisive role to this information differential in the competition between both systems. Yet this differential may be important. One method to look into this issue is to relate the information differential to certain characteristics of the trading situation. We argue that the information differential does not unambiguously support the competitive strength of one over the other system, but that such support depends on the trading situation. The main hypothesis advanced in this paper states: The information value, provided by the insight into the limit order book in the electronic trading system, relative to the information value of observing traders in the floor trading system, declines when the intensity of private and public information arrival increases. In other words, the hypothesis states that information diffusion in a floor trading system relative to that in an electronic trading system renders floor trading more attractive in times of high information
1
An exception is the APT-system of the LIFFE. This electronic trading system is in operation only before and after floor trading. In this system, floor trading is imitated; therefore names of traders appear on screens. Trading volume is modest.
2
These informational advantages of floor trading systems make it difficult for floor traders to switch to anonymous electronic trading systems. Swiss stock traders, for example, complained about the gap in information diffusion between floor and electronic trading when floor trading was replaced by
Ž .
intensity. Therefore, trading activity in a floor system should grow at a higher rate than in an electronic system when more information arrives.
The basic idea behind this hypothesis is that in times of low information intensity, only a few transactions take place providing only little information. Therefore, the insight into the limit order book in the electronic system provides more information and, thus, renders trade in the electronic system more attractive. In times of high information intensity, however, the high trading frequency implies a steady flow of transactions data so that the limit order book information has little value. Furthermore, observing other traders and exchanging opinions on the floor provide valuable information. As a consequence, information diffusion in the floor system is faster making trade in this system more attractive.
Since information intensity is not observable, we follow Admati and Pfleiderer
Ž1988 who argue that information arrival is reflected in the time patterns of price.
volatility and trading volume. High price volatility indicates high information intensity. Therefore, a testable implication of our main hypothesis is that the market share in trading volume of the electronic system should be inversely related to price volatility. This hypothesis is tested by analyzing the trade of the
Ž .
Bund-Future contract at the DTB Deutsche Terminborse, renamed EUREX and
¨
Ž .
at the LIFFE London International Financial Futures Exchange . The Bund-Future is a futures contract on a national German Government Bond with an annual coupon of 6% and residual maturity of 8.5 to 10 years at contract expiration. This contract is traded in almost identical design at the DTB, an electronic exchange, and at the LIFFE, a floor-based exchange. If our hypothesis is correct, then the DTB’s market share in trading activity should be inversely related to price volatility. Our analysis of transactions data from 1991 to 1995 confirms our hypothesis. However, the support declines over time. This is not surprising since low information intensity situations characterized by few transactions per unit of time become rare. Hence, the explanatory power of price volatility as a proxy for information intensity declines over time.
Ž .
A similar study has been done by Fremault Vila and Sandmann 1997 . They
Ž
analyze the trade of the Nikkei Stock Index Future at the SIMEX Singapore
.
International Monetary Exchange , an open outcry market, and the Osaka Securi-ties Exchange, a computerized market. In contrast to us, they find that the computerized market attracts additional trading volume in periods of high price
Ž .
2. Traders’ preferences for electronic vs. floor trading
2.1. Trading systems
Floor-based trading systems vary considerably in their design, the same is true of electronic screen-based trading systems. A discussion of traders’ preferences for one trading system versus another one requires a characterization of both systems. The characterization chosen here relates closely to the trading systems of the LIFFE and the DTB since data of these exchanges will be analyzed.3
The floor system is a dealer-driven system with continuous trade through open outcry. Quotes are valid as long asAbreath is warmB. An official order book does not exist. Transaction prices are published immediately, volumes of transactions are published with short delays. Names of traders are not disseminated; this information is available on the floor.
The electronic screen-based trading system is a continuous auction system with automatic order matching in which traders communicate only via computer screens without revealing their names.4 If two orders can be matched, then orders
with the best prices are matched. Information on transaction prices and volumes is published instantaneously in the electronic system.
Although both trading systems appear to be very different, they share many features. Both systems are operating continuously. Execution risk is eliminated in both systems: the quotes from the floor tell the trader at which prices he can trade. Similarly, in the electronic system the trader knows the limit order book and, thus, the prices at which an order can be executed. In both systems, traders are dual capacity traders, i.e. they may trade on their own and on customers’ accounts. The following discussion is based on these trading systems.
2.2. The static impact of information
Analyzing traders’ preferences for one or the other system, we assume that traders have access to both systems. Hence, the fixed costs are irrelevant for the short-term decision to trade in one or the other system. This decision depends also on the information available in each system. First, we discuss the information impact in a static, then in a dynamic framework.
Suppose that arbitrage between the electronic and the floor system functions well. Hence, whether an order is executed at a better price in one or the other system, depends on bid–ask spreads and on price sensitivities to order volume. Usually, the bid–ask spread is split into three components, a storage cost
3 Ž .
For a general discussion of different trading systems, see Pagano and Roell 1990, 1992 .¨
4
component, a premium for bearing price risk and an asymmetric information cost component. There is little reason to believe that the first two components differ among electronic and floor systems. Hence, differences in the bid–ask spread should be explained by differences in available information.
Ž .
Beneviste et al. 1992 argue that the bid–ask spread should be lower on the floor since observation of traders and sanctioning power of dealers allow them to distinguish information traders and liquidity traders.5 Hence, the adverse selection
problem should be weaker on the floor leading to lower bid–ask spreads and higher trading volume.6 This argument needs to be qualified, however. Risk-averse
traders can put very small orders into the electronic limit order book to protect themselves against adverse selection whereas on the floor quotes are valid for
Ž .
larger order sizes Glosten, 1994 . Hence, bid–ask spreads in electronic systems might be smaller since they relate to smaller order sizes. Large orders may face a higher spread in the electronic system. Also they are unlikely to be put into the electronic limit order book because of the free option problem. Thus, large orders
7 Ž .
preferably go to the floor. But, as pointed out by Bernhardt and Hughson 1997 , traders are likely to benefit from splitting large orders between both markets to equate marginal costs. Therefore, large orders add to the liquidity of both markets although perhaps more to the floor.8
Some empirical evidence concerning these issues is accumulating. The impact of competition among exchanges on the bid–ask spread has been demonstrated by
Ž .
McInish and Wood 1992 for the USA and for Europe by Pagano and Roell
¨
Ž1993 . Schmidt et al. 1993 show for Germany that regional floor exchanges. Ž .with small trading volume compete through smaller bid–ask spreads against an interbank electronic trading system with much higher trading volume. de Jong et
Ž .
al. 1995 find, in contrast to theoretical predictions, that the effective bid–ask spread does not increase in trade size, neither in the Paris bourse nor in the SEAQ International.
5 Ž .
Madhavan 1992 shows for a continuous dealer system and a continuous non-anonymous auction system that price competition between dealers eliminates theAwedgeB between the transaction price and the expected value of the asset whereas strategic behavior in auction markets distorts prices and thus induces inefficiency.
6
Higher trading volume, in turn, implies that prices are based on a larger set of information so that
Ž .
adverse selection is even more unlikely cf. Glosten and Milgrom, 1985; Stoll, 1989 . The inverse
Ž .
relationship between the bid–ask spread and trading volume is questioned by George et al. 1994 . They show that the impact of adverse selection on trading volume and the bid–ask spread depend on whether liquidity trading decreases in transaction costs at an increasing or a decreasing rate.
7 Ž .
Similarly, Pagano and Roell 1992 argue that large investors get a better price in dealer than in¨ auction markets.
8 Ž .
Finally, there are some empirical studies about the Bund-Future trade at LIFFE
Ž .
and DTB, mostly based on short time intervals. Kofman et al. 1994 investigate data from 6 weeks. Using the Roll measure, they find that the DTB offers a tighter bid–ask spread. Correcting this measure for conditional expected returns as
Ž . Ž .
suggested by George et al. 1991 , they find the opposite result. Pirrong 1996 finds for the period July 1992 to June 1993 that spreads as given by the Roll measure were not higher at the DTB and sometimes lower than at the LIFFE.
2.3. The dynamic impact of information
Now we analyze the impact of changes in information intensity on traders’ behaviour. The likelihood of adverse selection increases with information inten-sity. Therefore, in periods of high information intensity, non-informed traders preferably go to the system with faster information diffusion. Informed traders, however, prefer trading in the system with slower information diffusion to raise profits from insider trading. Since insiders represent only a small fraction of traders, their impact on trading volume is very likely to be smaller than that of non-informed traders. More precisely, in a period of intensive information arrival, non-informed traders are likely to concentrate their trading in the system with faster information diffusion. Therefore, the market share of this system should increase with information intensity. The market share of a trading system is defined as its trading volume per period, divided by the aggregate trading volume of both systems per period.
Information has two components, the information about market transactions and
Ž .
behavior of traders endogenous information and the information about other
Ž .
events being relevant for pricing exogenous information . This information can be
Ž .
private or public Admati and Pfleiderer, 1988 . It is private information or the
Ž .
difference in opinion that drives trading Harris and Raviv, 1993 . Hence, it is likely to raise volume, frequency of transactions, and price volatility and, thereby, the intensity of endogenous information arrival.
In periods of low information intensity, trade volume is low and transactions are infrequent; therefore information on the last trade is fairly old. Then the limit order book information of the electronic system is more updated and an important indicator of market developments. Also, in such a period, traders are relatively inactive so that observation of their behavior on the floor does not permit reliable predictions of their activities. Finally, in such a period there is not much to be gained from conversation among floor traders since new information to be evaluated is lacking. Hence, in a period of low information intensity, the order book information of the electronic system appears to offer more signals for predicting market developments than observation of traders on the floor.
This picture changes significantly in periods of high information intensity. Then
Ž .
Due to high price volatility, traders will reduce their limit orders in the electronic
Ž .
order book because the value of the free option increases with volatility. 2 Frequent trading with high trading volume9 yields continuously brand-new
infor-Ž .
mation on prices and trading volume. 3 Observation of other traders on the floor becomes more informative. Also, discussing new information with other traders
Ž .
helps each trader to better understand and evaluate the new information. 4 In periods of high exogenous information intensity, the danger of adverse selection is high. This danger is stronger in an anonymous electronic system than in a floor system. All the preceding arguments support the hypothesis that floor trading gains attractiveness relative to electronic trading in periods of intensive information arrival.
There are various ways to test this hypothesis. One way is to find out which
Ž .
system is leading price innovations. Shyy and Lee 1995 find for the period November 8–19, 1993 that the anonymous DTB-system is leading price innova-tions and that information asymmetries at the DTB are smaller. They argue that this may be explained by Germany being the Ahome marketB for German government bonds. Such a conclusion appears premature since we expect the relative speed of information diffusion at both exchanges to depend on the
Ž .
intensity of information arrival Franke and Hess, 1995 . This is confirmed by
Ž .
Martens 1997 . He investigates the information share of the DTB as defined by
Ž .
Hasbrouck 1995 . The information share of a market is the proportion of the efficient price innovation variance that can be attributed to that market. Martens finds for the period September 8 to December 20, 1995 that in times of high price volatility the information share of the DTB is lower than that of the LIFFE and vice versa in times of low price volatility. This finding is consistent with our conjecture that the speed of information diffusion in the electronic system relative to that in the floor system declines in terms of high information intensity. We test our conjecture by investigating the DTB’s market share across periods of varying information intensity. This leads to our first hypothesis.
Hypothesis 1. The market share in trading volume of the floor vis-a-vis the
´
electronic system increases with the intensity of information arrival.
Testing Hypothesis 1, we use volatility as a proxy for information. Given the positive relation between the intensity of exogenous information arrival and price volatility, Hypothesis 1 leads to Hypothesis 2.
9
High trading volume is likely to improve the reliability of information aggregated in transaction
Ž .
Hypothesis 2. The market share of the electronic system declines when price
volatility increases.
Since high exogenous information intensity also raises trading volume, the market share of the electronic system should also be inversely related to the trading volume aggregated over both markets.10
Hypothesis 3. The market share of the electronic system declines when aggregate
trading volume increases.
Similarly, an increase in endogenous information intensity renders the elec-tronic order book less informative.
Hypothesis 4. The market share of the anonymous electronic trading system
declines when trading frequency increases.
Hypotheses 2 and 3 relate the market share to price volatility and aggregate trading volume. A high aggregate trading volume may be generated by high price volatility andror by a large average order size, independent of volatility. Since large orders allegedly also favor floor trading, we separate both effects in Hypothesis 5.
Hypothesis 5. The market share of the electronic system declines when price
volatility increases andror the average order size increases independently of price volatility.
3. Empirical results
3.1. Data
The preceding hypotheses will be tested by data on Bund-Future trading at the DTB and the LIFFE. The LIFFE started the Bund-Future trade in September 1988;
10
The usual argument says that whenever new information arrives, traders and investors revise their expectations and, consequently, their portfolios. Hence, trade volume increases and, at the same time, price volatility increases because the revision in expectations leads to a revision of equilibrium prices. As the price does not instantaneously jump to its new equilibrium price, it takes some time for the market participants to find out the new equilibrium price by trial and error. In this time period, volatility is higher than normal. The positive relation between volume and volatility is well documented
Že.g. Karpoff, 1987; Lee et al., 1994 ; equally interesting is the finding of Berry and Howe 1994 that. Ž .
the DTB followed in January 1991 with an almost identical contract design. The futures have a maximum maturity of 9 months and expire in March, June, September, and December.
From both exchanges, we obtained time stamped data and daily data for a 5-year period from January 1991 to December 1995. Daily data include daily trade volume and highest and lowest prices of the day for each contract. Time stamped data include the price, the volume, and the time of each transaction. The quality of the intraday data differs substantially. While DTB intraday data are precisely recorded from the computerized trading system, at LIFFE prices and volumes are transmitted through hand signals by pit observers. Since reported intraday transac-tion volumes at LIFFE are not reliable, we use LIFFE’s daily floor trading volume figures exclusively. This also means that the average order size at LIFFE cannot be inferred from reported transactions volumes. Table 4 in Appendix A provides summary statistics on the daily trading volume of the Bund-Future contract at each exchange as well as on the DTB’s market share.
Trading concentrates in the front month contract until about 3 to 5 days before expiration when traders roll over to the next contract. For each day, we use the trading volume of the contract with the highest trading volume. To avoid biases around the roll-over period, we exclude the first and the last three days of the period in which a contract is the most actively traded.
Fig. 1 depicts the DTB’s market share in daily trading volume in the most actively traded Bund-Future contract.11 This market share increased steadily until
spring 1992 with an average of 32% for the Mar-92 contract. The market share peaked in the period from late October 1991 to January 1992 when the German banks launched a joint effort to increase trading volume in Frankfurt. Hence, the market share in this period appears to be biased. Later on, the average market share per contract varied within a fairly narrow range of 26% to 31%. Recently, the situation has changed. The market share of the DTB went up dramatically in 1997 and 1998. In the last quarter of 1998, it exceeded 99.95%. Presumably, one reason for this dramatic increase is the remote cross border access of traders which has been promoted since 1996 by the DTB. In order to exclude these factors, we concentrate in our analysis on the years 1991 to 1995.
3.2. Differences between DTB- and LIFFE-prices
We hypothesize that traders’ preferences for a trading system depend on information diffusion assuming that arbitrage between both exchanges eliminates price differences exceeding transaction costs. To analyze price differences, we
11
Fig. 1. Daily DTB market share in the most actively traded Bund-Future contract excluding roll-over periods. DTB’s market share is computed relative to LIFFE’s floor trading volume. Vertical lines indicate expiration dates for the contracts denoted on the top.
need synchronous observations. Yet, trades rarely occur simultaneously at both exchanges. Moreover, the time between transactions is random. For each exchange we compute average prices within intervals of 3-min length to obtain an intraday time series with equidistant observations.12 From both time series we derive price
differences, i.e. differences between the average prices at both exchanges for every 3-min interval. Moreover, these time series will be used to estimate price volatility for each trading day.
Mostly, prices at LIFFE are higher than the DTB, but daily mean price differences usually do not exceed two ticks.13 Major exceptions are the first two
contracts, when trading at DTB was thin: For the Mar-91 and the Jun-91 contract, we observe daily mean price differences up to six and three ticks, respectively. Another exception is the Mar-93 contract when Treuhand bonds were deliverable at DTB but not at LIFFE. This led to daily mean differences of 18 to 49 ticks. Furthermore, we observe large price differences at the end of our sample: For the Dec-94, Mar-95, and the Jun-95 contract, daily mean price differences went up to five, seven, and six ticks, respectively, reaching their peaks some days before the expiration dates. Interestingly, physical delivery of these contracts was also relatively high. This indicates opportunities for arbitrage. Overall, it appears that
12
We focus on the time span when trading is possible in both, DTB’s computerized system and
Ž .
LIFFE’s floor trading system. This is the interval from approximately 9:00 to 17:00 h CET .
13
after the start-up phase markets are well integrated and arbitrage between both exchanges works reasonably well.
3.3. Estimating priceÕolatility
In order to relate the market share to information diffusion, we use price volatility estimates for each trading day. Daily price volatility is estimated from the time series of 3-min-average prices of that day. We use DTB prices since the electronic reporting is likely to be more precise. For the first three contracts, the price volatility estimates differ somewhat from those obtained from LIFFE data. Later on, both volatility estimates are very similar. To account for dependencies in
Ž . 14
second moments of financial time series, we implement a GARCH 1,1 model. This model is estimated for each contract separately. A detailed description of the estimation approach along with parameter estimates is provided in Appendix B. Summary statistics on unconditional GARCH volatility estimates are provided in Table 6. For comparison, heteroskedasticity and autocorrelation consistent
stan-Ž .
dard deviations are estimated according to Newey and West 1987 using the
Ž .
automated lag selection procedure proposed by Newey and West 1994 . Summary statistics on these volatility estimates are also given in Table 6. Both intraday volatility estimates are similar indicating that their information content is roughly the same. This may also be inferred from the high correlation of both estimates reported in Table 6. We also use log ratios of daily highest and lowest transaction
Ž .
prices shigh–low price relatives as an additional volatility measure. These are also highly correlated with unconditional GARCH volatility estimates as shown in Table 6.
3.4. Test of hypotheses
To test the hypotheses of Section 2.3, we use the market share of the electronic trading system as a proxy for traders’ preferences and run regressions with the market share being the dependent variable. The DTB’s market share MS is
w x
constrained to the interval 0, 1 so that a normal distribution is ruled out.
Ž Ž ..
Therefore, we use the log transformation TMStsln MStr 1yMSt in all regressions.
Table 1 summarizes the results of OLS regression tests of Hypotheses 2 to 4. With the exception of three contracts expiring after June 1994, volatility coeffi-cients are negative supporting Hypothesis 2, i.e. DTB’s market share declines when the daily unconditional standard deviation of intraday price changes in-creases. Moreover, 14 out of the 17 negative coefficients are significant. One positive coefficient is also significant, however. The empirical results for
Hypothe-14 Ž .
Table 1
Regression of the DTB’s market share in Bund-Futures trading on daily volatility, log aggregate trading volume, and log time between trades at DTB
Contract Regression of DTB’s market share on
Unconditional GARCH volatility Log aggregate trading volume Log time between trades
2 2 2
a2 R BG a2 R BG a2 R BG
) ) ) ) )
Mar-91 y0.120 0.40 5.01 y0.287 0.28 3.20 y0.003 0.06 0.92
) ) ) )
Jun-91 y0.268 0.28 1.36 y0.264 0.14 1.78 y0.252 0.11 3.65
) ) ) ) ) ) ) )
Sep-91 y0.310 0.15 1.86 y0.318 0.23 3.20 0.198 0.03 2.42
) )
Dec-91 y0.339 0.75 5.25 y0.166 0.76 2.96 0.027 0.74 5.05
) ) ) ) ) ) ) ) )
Mar-92 y0.910 0.52 3.01 y0.581 0.82 2.25 0.706 0.74 1.52
) ) ) ) ) ) ) ) )
Jun-92 y0.389 0.22 2.22 y0.322 0.44 4.25 0.306 0.29 3.02
)
Sep-92 y0.155 0.16 1.53 y0.137 0.20 0.88 0.066 0.14 1.01
) ) ) ) ) ) ) )
Dec-92 y0.230 0.21 2.00 y0.331 0.41 3.17 0.202 0.18 1.78
) ) ) ) ) ) )
Mar-93 y0.560 0.21 9.66 y0.228 0.17 5.85 0.120 0.03 5.75
) ) ) ) ) ) ) ) )
Jun-93 y0.280 0.24 3.45 y0.355 0.50 4.33 0.329 0.25 2.81
) ) )
Sep-93 y0.102 0.19 2.28 y0.116 0.23 2.06 0.046 0.15 2.03
) ) ) ) )
Dec-93 y0.097 0.01 3.32 y0.255 0.29 6.86 0.202 0.09 4.91
) ) ) ) ) ) ) )
Mar-94 y0.065 0.42 6.16 y0.202 0.60 4.96 0.224 0.53 6.18
) ) ) ) ) ) ) ) )
Jun-94 y0.063 0.23 3.20 y0.272 0.47 4.08 0.260 0.31 6.91
Sep-94 y0.010 y0.01 5.22 y0.011 y0.00 4.12 y0.032 0.01 3.10
Dec-94 0.008 0.17 1.31 0.022 0.17 0.94 y0.056 0.18 0.91
) ) ) ) ) ) ) ) )
Mar-95 y0.165 0.64 0.96 y0.200 0.72 2.46 0.188 0.62 2.94
)
Jun-95 y0.071 0.08 5.22 y0.031 0.02 4.86 y0.044 0.03 5.43
) ) ) )
Sep-95 0.034 0.25 3.11 0.045 0.24 2.28 y0.092 0.28 2.46
Dec-95 0.019 0.23 3.45 y0.019 0.20 3.56 y0.019 0.21 3.66
The general structure of the three regressions is: TMStsa0qa tq1 a x2 tqa x3 ty1qa TMS4 ty1qet,
where TMS is the log transformed market share and t is the trading day. x stands for the unconditional GARCH volatility, the log aggregate trading volume, and log daily average of time between trades, respectively.e denotes the disturbance term.
For each regression, estimates of a are reported, along with the adjusted R2 2, and the Breusch–Godfrey test statistic for autocorrelated residuals. Significance of estimated parameters is tested according to heteroskedasticity consistent t-ratios.) ) ),) ), and) indicate significance of parameters at the 1%,
5%, and 10% level, respectively. None of the Breusch–Godfrey test statistics indicates a significant autocorrelation of residuals.
sis 3 are comparable. For 14 contracts DTB’s market share significantly declines when logarithmic aggregate trading volume increases, i.e. the sum of DTB’s and LIFFE’s trading volume. After June 1994, the significance of this relation diminishes.
trades per minute on average for the last six contracts. At such a high trading frequency, little information is gained from the insight into the electronic order book.
The observed relationship between market share and volatility does not reveal whether trading volume at the DTB declines in times of high volatility or whether it increases less than at the LIFFE. This question is addressed by estimating
Ž .
simultaneously three equations. The first second equation relates the log trading
Ž .
volume at the DTB LIFFE to the log daily high-low price relative, non-lagged and lagged, and the lagged aggregate trading volumes. The third equation relates the daily log high-low price relative to lagged values of this variable, the log aggregate trading volumes, non-lagged and lagged, and the DTB’s market share.
5 5
TVi tsaiq
Ý
b HLit tytqÝ
c ATVit tytqhi t,ts0 ts1
isD DTB , L LIFFE
Ž
.
Ž
.
Ž
3.1.
5 5
HLtsa3q
Ý
b HL3t tytqÝ
c ATV3t tytqd TMS3 tqh3 t.ts1 ts0
t/3 ,4
TV denotes the log trading volume at exchange i at day t, HLi t i t the log ratio of the highest over the lowest transaction price at day t and ATV the log aggregatet trading volume at day t. The high-low price relative is used in many studies as a measure of price volatility. Based on instrumental variables, we estimate system
Ž3.1 by GMM.. 15Since the number of observations per contract is small relative to
Ž .
the number of coefficients in Eq. 3.1 , we pool contracts Jun-92 to Jun-94
Žexcluding the first contracts with a strong trend in market share and contracts.
Sep-94 to Dec-95. Table 2 shows the results for the most interesting coefficients. The figures in Table 2 show that trading volumes at both exchanges covary with the non-lagged high-low price relative, but the trading volume reacts clearly stronger at the LIFFE. This is more evident for contracts Jun-92 to Jun-94 than for
Ž .
contracts Sep-94 to Dec-95. Still, the difference bL0ybD 0 is significant at the 1% level even for the latter period. Hence, it is not surprising that DTB’s market
Ž .
share declines when price volatility increases Table 1 . The high–low price relative is strongly driven by the aggregate trading volume and inversely related to the DTB’s market share. The latter impact is significant only in the second period. The relationship between market share and price volatility is confirmed in a simultaneous test of the impact of the high–low price relative on DTB’s market
15 Ž .
Table 2
Ž .
Estimation results for the system 3.1
TVD bD,0 bD,1 cD,1 BG J
) ) ) ) ) ) ) )
Contracts Jun-92 to Jun-94 1.413 y0.136 0.228 3.09 0.15
) ) ) ) ) )
Contracts Sep-94 to Dec-95 1.212 y0.056 0.218 2.30 0.16
TVL bL,0 bL,1 cL,1 BG
) ) ) ) ) ) ) ) )
Contracts Jun-92 to Jun-94 1.840 y0.296 0.317 3.68
) ) ) ) ) )
Contracts Sep-94 to Dec-95 1.296 0.009 0.161 2.22
HL b3,1 c3,0 d3 BG
) ) ) ) ) )
Contracts Jun-92 to Jun-94 0.157 0.358 y0.040 5.43
) ) ) ) ) )
Contracts Sep-94 to Dec-95 0.001 0.515 y0.307 1.53
The log daily trading volumes of the DTB, TV , and of the LIFFE, TV , and the log daily high–lowD L price relative HL, are estimated simultaneously by an instrumental variables technique. For each equation, the Breusch–Godfrey test statistic for autocorrelated residuals is shown. Also, the J statistic testing the validity of overidentifying restrictions is displayed.
share and of the impact of the market share on the price relative. Again, an
Ž .
instrumental variables approach is used to estimate system 3.2 .
2
The instruments are mostly the same as those for estimating system 3.1 . This system is estimated separately for each contract. Estimates for the coefficients c1,0
and d are displayed in Table 7 in Appendix C. As hypothesized, the high–low2
price relative has a strong negative impact on the DTB’s market share for the contracts until June 94; all coefficients are significant except for contract March 94. Equally, the market share has a strong negative impact on the high–low price relative for most of these contracts. The coefficients for two contracts have the wrong sign. Overall, for the period until June 1994 the results indicate a strong inverse relation between the DTB’s market share and price volatility. After June 1994 this relation fades away.
Ž .
trading volume on the standard deviation of price changes and on lagged trading volume:
ATVtsa0qa t1 qa UGV2 tqa UGV3 ty1qa ATV4 ty1qjt,
Ž
3.3.
with UGV denoting unconditional GARCH volatility estimates.t jt is the de-trended aggregate trading volume which is not explained by price volatility. It is uncorrelated with price volatility and normalized to zero mean. In the second step, we run a regression of DTB’s market share, TMS , on the volatility estimates, ont
the aggregate trading volume not explained by volatility, jt, and on the lagged market share:
TMStsb0qb t1 qb UGV2 tqb UGV3 ty1qb4jtqb TMS5 ty1qet.
Ž
3.4.
Table 3 presents the parameter estimates for UGV andt jt. Most parameters are negative as hypothesized. Overall, volatility appears to have a somewhat stronger impact on the market share than the volatility independent aggregate trading volume. Again, significance is much stronger until June 1994. The explanatory power as measured by the adjusted R2 is mostly higher for the bivariate regression
as compared to the univariate regression of market share on volatility in Table 1.
Table 3
Ž .
Regression 3.4 of the DTB’s daily market share, TMS, on daily unconditional GARCH volatility estimates, UGV, on the volatility independent detrended aggregate trading volume,j, and on lagged market shares
Estimates of b and b are shown. In addition, the adjusted R2, and the Breusch–Godfrey test statistic
2 4
Ž .
Both, the one- and the two-step analysis see Tables 1 and 3 were performed also using as volatility estimates the heteroskedasticity and autocorrelation
consis-Ž .
tent standard deviation estimator proposed by Newey and West 1987 as well as daily high–low price relatives. The results are quite similar to those reported here and, therefore, omitted. Hence, the results are robust with respect to different volatility measures.
3.5. Discussion
The central hypothesis of this paper is that in times of low information intensity the insight into the electronic order book provides valuable information to traders while in times of high information intensity this insight is of little value. As a consequence, traders’ preference for electronic trading is inversely related to information intensity. The market share of the electronic trading system is used as a proxy for traders’ preferences; price volatility, aggregate trading volume and trading frequency are used as proxies for information intensity implying Hypothe-ses 2 to 4, respectively.
The empirical support of these hypotheses is strong until June 1994. All regression parameters of price volatility and aggregate trading volume have the correct sign and almost all parameters are significant. Also, a test of simultaneous equations shows a negative impact of price volatility on DTB’s market share and vice versa. Trading volume at each exchange increases with information intensity, but this increase is stronger at the LIFFE. Apart from the initial phase of Bund-Future trading at the DTB, its market share is also positively related to the time between trades and, hence, negatively related to trading frequency. But this evidence is somewhat weaker.
After June 1994, the empirical support of Hypotheses 2 to 4 is weak, at best. This should not come as a surprise. Consider trading frequency as an indicator of information intensity. The median of the daily average time between trades at DTB declined from 103 s for the Mar-91 contract over 46 s for the Mar-93 contract to 14 s for the March 95-contract. This median also declined strongly at LIFFE; it was lower at LIFFE than at DTB until June 94, afterwards it was about the same. Therefore, the frequency of transactions data has grown strongly. Moreover, the standard deviation of the daily average time between trades has declined substantially over time so that the frequency of transactions is also fairly stable in the latter years. Due to the implied steady and strong endogenous information flow, the relative importance of the electronic order book information has become small permanently. Hence, information intensity has lost its explana-tory power for traders’ preferences and, thus, for the DTB-market share. It can only explain variations in market share if it varies substantially over time.
go to the LIFFE and also raise volatility. The arguments for this conjecture have been discussed in Section 2.2. Consistent with this conjecture, the empirical support for Hypothesis 3 which relates market share to aggregate trading volume is somewhat stronger than for Hypothesis 2 which relates market share to price volatility.
Hypothesis 5 states explicitly that DTB’s market share is affected by price volatility and by the volatility-independent aggregate trading volume. Changes in the latter variable may be interpreted as a proxy for changes in the average order size although other effects may also be present.
The evidence supports Hypothesis 5. Both price volatility and the proxy for the average order size have a clearly negative impact on the DTB’s market share until June 1994. After June 1994, also the impact of this proxy fades away. One explanation might be that the liquidity of the DTB as measured by the daily trading volume16 has increased strongly over time. Therefore, it is likely that the
sensitivity of the DTB bid–ask spread to order size has declined over time so that large orders are executed at average prices comparable to LIFFE. This would be in
Ž .
line with the findings of de Jong et al. 1996 .
Overall, the evidence supports our hypotheses about the impact of information diffusion on traders’ preferences for electronic versus floor trading, but this impact appears to fade away in very active markets. Our results are also consistent with
Ž .
those of Martens 1997 . He finds that the information share of the DTB is inversely related to volatility. Yet, our findings need to be interpreted with caution. Since traders’ preferences and information intensity cannot be observed directly, we have to use proxies which involve errors-in-variables problems. As the break in the middle of 1994 suggests, these variables portray not only preferences and information intensity, but also other aspects of trading. Clearly, both exchanges have continuously tried to improve their trading systems which might also lead to breaks in the empirical findings.
4. Conclusions
This paper argues that the information differential between an anonymous screen-based trading system and a floor trading system should increase the attractiveness of the latter in periods of high information intensity. These are also periods of high volatility, high volume, and high trading frequency. More informa-tion is available from observing actual trades so that the informainforma-tion of the electronic order book is less valuable. Placing orders in the order book becomes less attractive. Also traders are more active so that on the floor more information can be inferred from observing traders. Moreover, the reputation effect which is
16
precluded by the anonymity of electronic trading becomes more important in periods of high information intensity. All this makes floor trading more attractive in those periods. Besides, large orders tend to go to the floor if price sensitivity to order size is smaller on the floor. Therefore, an increase in average order size should also raise the floor’s market share in aggregate trading volume.
These conjectures are tested by analysing the trade of the Bund-Future contract at the DTB and the LIFFE. The market share of electronic trading is inversely related to price volatility for the period from 1991 to the middle of 1994. We find even stronger evidence for an inverse relation between the DTB’s market share and aggregate trading volume. Also, the volume effect is impressively supported at both exchanges. Trading volume grows faster with volatility at the LIFFE than at the DTB. Separating the impact of volume and volatility on DTB’s market share, we find that a volatility independent increase in trading volume also reduces the market share of electronic trading. This is consistent with the claim that large orders preferably go to the floor.
After the middle of 1994, the evidence in favor of our hypotheses fades away. This is not surprising since the average time between successively reported trades has grown strongly over time. Thus, times of low information intensity have become rare, the same being true of the informational advantage of the electronic order book. Therefore, because of little variation information intensity fails to explain variations in the DTB’s market share after June 1994. The same is true of the volatility independent proxy for order size. This may be due to the strong improvement of DTB-liquidity over time so that large orders may be executed at similar prices at both exchanges. Therefore, our hypotheses appear to be valid more for thin than for active markets.
Information intensity is one of many factors explaining market shares. The recent concentration of the Bund-Future trade at the DTB certainly suggests more research into these factors.
Acknowledgements
For valuable comments we are grateful to Yakov Amihud, Frank Gerhard, Siegfried Heiler, Walter Kramer, Beni Lauterbach, Marco Pagano, Winfried
¨
Pohlmeier, Gerd Ronning, Anne Fremault Vila, Peter Winker, the editor of the Journal of Empirical Finance, an anonymous referee, members of the EFA-con-ference in Milano, the CBOT-workshop in Barcelona, and the LSE-workshop in London.
Appendix A. Daily trading volume and DTB’S market share
()
G.
Franke,
D.
Hess
r
Journal
of
Empirical
Finance
7
2000
455
–
478
473
Summary statistics on daily trading volume of the Bund-Future at the DTB and the floor trading system at LIFFE and on the market share of the DTB
w x w x w x
Contract Observations Volume DTB 1000 contracts Volume LIFFE 1000 contracts Market share DTB %
Minimum Maximum Mean Minimum Maximum Mean Minimum Maximum Mean Standard
deviation
Mar-91 34 1.5 6.3 3.3 14.1 75.8 40.0 4.9 11.8 8.0 1.8
Jun-91 51 1.9 10.7 5.8 19.5 81.2 38.1 7.1 20.9 13.3 3.0
Sep-91 52 2.7 22.0 7.7 14.5 113.9 36.9 12.5 29.5 18.0 3.4
Dec-91 50 5.1 52.4 15.2 17.0 75.1 35.8 16.2 47.5 28.4 8.6
Mar-92 53 5.0 31.0 20.6 3.0 85.8 47.9 22.7 62.5 32.1 7.3
Jun-92 52 10.4 32.3 21.6 21.0 90.3 53.8 21.9 42.3 29.4 3.8
Sep-92 57 7.1 51.0 20.1 16.3 95.8 42.5 24.2 46.4 32.5 4.4
Dec-92 56 6.2 47.6 20.7 13.4 108.7 53.1 19.0 35.9 28.3 3.7
Mar-93 51 2.4 30.3 14.5 9.6 87.7 42.6 18.1 35.8 25.9 4.2
Jun-93 56 16.0 46.5 29.7 30.0 118.2 75.8 22.6 37.1 28.8 3.2
Sep-93 52 13.0 47.9 30.1 31.8 112.9 70.8 25.5 36.4 30.0 2.6
Dec-93 57 19.2 67.6 36.4 36.7 175.6 94.1 21.5 37.1 28.4 3.2
Mar-94 49 8.2 109.0 56.5 14.6 265.1 143.5 24.4 37.8 29.5 3.5
Jun-94 52 34.0 101.7 62.7 74.7 250.5 155.5 24.3 35.7 29.1 2.5
Sep-94 59 8.1 89.2 55.6 16.2 208.7 122.4 27.3 36.5 31.2 2.1
Dec-94 56 22.2 92.4 47.9 56.0 236.6 128.1 21.4 31.4 27.2 2.0
Mar-95 55 5.5 74.7 42.2 6.5 180.1 101.3 22.5 46.0 30.5 4.6
Jun-95 53 18.3 74.8 45.9 51.3 196.2 114.1 22.6 33.7 28.7 2.3
Sep-95 57 17.1 106.9 48.3 40.9 208.4 111.1 25.5 35.7 30.0 2.5
Dec-95 57 26.6 115.8 54.4 55.2 272.4 121.1 25.2 36.0 31.0 2.4
Volume is reported in thousands of contracts. DTB’s daily market share equals DTB’s trading volume divided by the sum of DTB’s volume and LIFFE’s floor trading volume for that day. It is given in percentage points. The contract identification MMM-YY refers to the expiration date of a contract. The number of
Ž . Ž .
Appendix B. Price volatility estimates
Let Pt, i denote the contract’s average transaction price in the 3-min interval i of trading day t. If no transaction is observed in an interval i, the previous interval’s average price is taken as P .t, i DPt, i stands for the change in 3-min average prices from interval iy1 to i. These intraday price changes are modelled
Ž .
as a first-order autoregressive process, AR 1 :
DPt , isgtqbDPt , iy1qu ,t , i
Ž
B.1.
where gt denotes the trend for each trading day t and b the autoregression coefficient which is assumed to be the same for all trading days of the contract.
Ž .
The error term, ut, i is assumed to follow a GARCH 1,1 process with ht, i
denoting the conditional variance of u :t, i
h skqu h qfu2 .
Ž
B.2.
t , i t t t , iy1 t t , iy1
Ž .
We assume that the GARCH 1,1 structure is identical across trading days, i.e.
utsu and ftsf for all trading days of a contract but we allow the
uncondi-Table 5
Ž . Ž .
Estimated AR 1 –GARCH 1,1 parameters of 3-min-average price changes at DTB
a
Ž .
Contract Days b kt median u f LR
) ) ) ) ) ) ) ) ) ) ) )
Mar-91 34 0.159 1.595 0.576 0.044 500
) ) ) ) ) ) ) ) ) ) ) )
Jun-91 51 0.115 0.446 0.739 0.081 743
) ) ) ) ) ) ) ) ) ) ) )
Sep-91 52 0.121 0.328 0.707 0.079 1009
) ) ) ) ) ) ) ) ) ) ) )
Dec-91 50 0.093 0.183 0.722 0.101 566
) ) ) ) ) ) ) ) ) ) ) )
Mar-92 53 0.145 0.185 0.720 0.120 943
) ) ) ) ) ) ) ) ) ) ) )
Jun-92 52 0.167 0.243 0.714 0.093 885
) ) ) ) ) ) ) ) ) ) ) )
Sep-92 57 0.138 0.217 0.632 0.126 1406
) ) ) ) ) ) ) ) ) ) ) )
Dec-92 56 0.169 0.237 0.791 0.089 1793
) ) ) ) ) ) ) ) ) ) ) )
Mar-93 51 0.131 0.147 0.750 0.071 965
) ) ) ) ) ) ) ) ) ) ) )
Jun-93 56 0.203 0.330 0.742 0.115 1550
) ) ) ) ) ) ) ) ) ) ) )
Sep-93 52 0.159 0.241 0.767 0.089 259
) ) ) ) ) ) ) ) ) ) ) )
Dec-93 57 0.139 0.207 0.791 0.098 1241
) ) ) ) ) ) ) ) ) ) ) )
Mar-94 49 0.142 0.295 0.777 0.120 2614
) ) ) ) ) ) ) ) ) ) ) )
Jun-94 52 0.094 0.467 0.829 0.113 1664
) ) ) ) ) ) ) ) ) ) ) )
Sep-94 59 0.105 0.399 0.837 0.107 2164
) ) ) ) ) ) ) ) ) ) ) )
Dec-94 56 0.104 0.551 0.756 0.116 1519
) ) ) ) ) ) ) ) ) ) ) )
Mar-95 55 0.106 0.225 0.792 0.122 1537
) ) ) ) ) ) ) ) ) ) ) )
Jun-95 53 0.127 0.201 0.816 0.102 1680
) ) ) ) ) ) ) ) ) ) ) )
Sep-95 57 0.111 0.282 0.739 0.155 3090
) ) ) ) ) ) ) ) ) ) ) )
Dec-95 57 0.123 0.186 0.784 0.118 2009
Significance of parameters is tested according to asymptotic t-ratios. Significance at the 1% level is indicated by) ) ).
The estimated model is tested against a simple random walk with constant drift and variance:gtsg and ktsk for all trading days t of a contract and bs0, us0, and fs0. Likelihood ratio test
Ž .
statistics LR are given in the last column.
a
()
Summary statistics on daily volatility estimates
a b
Contract Unconditional GARCH volatility estimates Newey–West standard deviation Correlation between
10%- Median 90%- 10%- Median 90%- UGV and UGV and
percentile percentile percentile percentile NWS HL
Mar-91 1.53 2.07 3.00 1.34 1.79 2.65 0.86 0.69
Jun-91 1.30 1.59 2.10 0.93 1.36 1.88 0.80 0.87
Sep-91 1.01 1.25 1.62 0.77 1.03 1.45 0.93 0.84
Dec-91 0.78 1.02 1.35 0.68 0.89 1.22 0.73 0.80
Mar-92 0.83 1.09 1.39 0.68 1.10 1.47 0.95 0.89
Jun-92 0.81 1.14 1.48 0.70 1.16 1.54 0.95 0.89
Sep-92 0.79 0.96 1.37 0.62 0.85 1.65 0.90 0.89
Dec-92 0.97 1.42 1.91 0.84 1.40 2.15 0.97 0.97
Mar-93 0.74 0.91 1.18 0.58 0.88 1.28 0.94 0.83
Jun-93 1.26 1.55 2.02 1.23 1.58 2.38 0.92 0.84
Sep-93 1.06 1.31 1.76 1.07 1.36 1.91 0.79 0.61
Dec-93 1.11 1.38 1.66 1.02 1.37 1.80 0.83 0.81
Mar-94 0.99 1.71 2.59 0.96 1.76 2.66 0.98 0.96
Jun-94 1.89 2.85 4.01 1.93 2.96 4.02 0.94 0.81
Sep-94 1.82 2.70 4.22 1.83 3.02 4.33 0.92 0.91
Dec-94 1.63 2.09 3.01 1.71 2.29 2.98 0.90 0.72
Mar-95 1.10 1.62 2.10 1.18 1.65 2.25 0.82 0.78
Jun-95 1.18 1.58 2.17 1.28 1.69 2.38 0.88 0.68
Sep-95 1.19 1.64 2.64 1.08 1.65 2.87 0.94 0.91
Dec-95 1.06 1.39 2.03 1.10 1.47 2.02 0.95 0.87
10%, 50%, and 90% percentiles of volatility estimates are given for each contract. In addition, the correlation coefficients between unconditional GARCH
Ž . Ž . Ž .
volatility estimates UGV and Newey–West standard deviations NWS , resp. log high–low price relatives HL are provided.
a Ž . Ž .
Daily unconditional volatility estimates derived from the estimated AR 1 –GARCH 1,1 model described above.
b Ž .
tional variance to differ across trading days through a day t-dependent constantkt. Then, the variance equation is:
h skquh qfu2 .
Ž
B.3.
t , i t t , iy1 t , iy1
The unconditional variance of 3-min-average price changes for trading day t is
Ž . Ž . Ž .
VARDPt skt 1yb r1yuyf . The estimated conditional variance is posi-tive if the restrictions kt)0;t, and uG0, as well as fG0 hold. uqf-1 guarantees that u2 is covariance stationary. This model is estimated for each
t, i
contract.
Interestingly, for none of the contracts these parameter restrictions turned out to be binding.
Table 5 summarizes estimation results. All estimates of b, u, and f are significant at the 1% level. The same is true of the kt estimates except for 2 days of the first contract. Table 5 reports the median of kt for each contract.
Ž . Ž .
We also test this AR 1 –GARCH 1,1 model against a simple random walk. The likelihood ratio test statistic in Table 5 shows that our volatility model
Table 7
Ž .
Estimation results for the system 3.2
Contract TMS HL J
c1,0 BG d2 BG
) ) ) ) ) )
Mar-91 y0.497 3.98 y0.345 5.18 0.27
) ) ) ) )
Jun-91 y0.622 1.52 0.311 7.65 0.27
) ) ) ) ) )
Sep-91 y1.679 4.95 y0.271 1.32 0.26
) )
Dec-91 y0.583 5.15 0.043 4.52 0.27
) ) ) ) )
Mar-92 y0.588 0.92 y0.160 5.08 0.29
) ) ) ) ) )
Jun-92 y1.309 3.37 y0.456 3.57 0.27
) ) ) ) ) )
Sep-92 y0.813 3.41 y0.158 2.33 0.25
) ) ) ) ) )
Dec-92 y0.718 4.28 y0.344 4.93 0.27
) ) ) ) ) )
Mar-93 y1.377 5.27 y0.440 4.66 0.29
) ) ) ) ) )
Jun-93 y0.745 8.76 y0.610 4.29 0.27
Sep-93 y0.163 1.09 y0.113 0.99 0.25
) ) ) ) ) )
Dec-93 y0.633 5.58 y0.255 1.47 0.28
) ) ) ) ) )
Mar-94 y0.694 1.85 y0.512 5.22 0.28
)
Jun-94 y0.097 5.23 y0.122 2.84 0.27
Sep-94 y0.024 4.78 y0.358 0.13 0.27
Dec-94 y0.006 0.84 y0.220 1.57 0.25
) ) ) ) ) )
Mar-95 y0.571 3.86 y0.671 6.46 0.29
)
Jun-95 0.127 3.80 y0.165 3.05 0.25
Sep-95 0.006 2.31 0.045 7.12 0.26
)
Dec-95 y0.009 3.69 y0.200 5.19 0.26
performs significantly better than a random walk model in which the variance is restricted to be constant over the whole life of the contract. In Table 6, summary statistics of volatility estimates and correlations among them are shown.
Appendix C. The relationship between DTB’s market share and price volatil-ity
See Table 7.
References
Admati, A., Pfleiderer, P., 1988. A theory of intra-day patterns: volume and price variability. Review of Financial Studies 1, 3–40.
Beneviste, L., Marcus, A., Wilhelm, W., 1992. What’s special about the specialist? Journal of Financial Economics 32, 61–86.
Bernhardt, D., Hughson, E., 1997. Splitting orders. Review of Financial Studies 10, 69–101. Berry, T., Howe, K., 1994. Public information arrival. Journal of Finance 49, 1331–1346.
Blume, L., Easley, D., O’Hara, M., 1994. Market statistics and technical analysis: the role of volume. Journal of Finance 49, 153–181.
Bollerslev, T., Chou, R.Y., Kroner, K.F., 1992. ARCH modelling in finance: a review of the theory and empirical evidence. Journal of Econometrics 52, 5–59.
Ž .
Cathomen, I., 1996. Hundert Tage Aktienhandel an der EBS. Neue Zuricher Zeitung, 29 Dec. 20 .¨ de Jong, F., Nijman, T., Roell, A., 1995. A comparison of the cost of trading French shares on the Paris¨
Bourse and on SEAQ international. European Economic Review 39, 1277–1301.
de Jong, F., Nijman, T., Roell, A., 1996. Price effects of trading and components of the bid–ask spread¨ on the Paris Bourse. Journal of Empirical Finance 3, 193–213.
Franke, G., Hess, D., 1995. Anonymous Electronic Trading Versus Floor Trading. Discussion Paper, University of Konstanz.
Fremault Vila, A., Sandmann, G., 1997. Floor Trading Versus Electronic Screen Trading: An Empirical Analysis of Market Liquidity in the Nikkei Stock Index Futures Markets. Working Paper, London School of Economics.
George, T., Kaul, G., Nimalendran, M., 1991. Estimation of the bid–ask spread and its components: a new approach. Review of Financial Studies 4, 623–656.
George, T., Kaul, G., Nimalendran, M., 1994. Trading volume and transaction costs in specialist markets. Journal of Finance 49, 1489–1505.
Glosten, L., 1994. Is the electronic open limit order book inevitable? Journal of Finance 49, 1127–1161.
Glosten, L., Milgrom, P., 1985. Bid, ask and transaction prices in a specialist market with heteroge-neously informed traders. Journal of Financial Economics 14, 71–100.
Harris, M., Raviv, A., 1993. Difference of opinion make a horse race. Review of Financial Studies 6, 473–506.
Hasbrouck, J., 1995. One security, many markets: determining the contributions to price discovery. Journal of Finance 50, 1175–1199.
Karpoff, J., 1987. The relation between price changes and trading volume. Journal of Quantitative Analysis 22, 109–126.
Ž .
Lee, C., Ready, M., Seguin, P., 1994. Volume, volatility, and New York stock exchange trading halts. Journal of Finance 49, 183–214.
Madhavan, A., 1992. Trading mechanisms in securities markets. Journal of Finance 47, 607–641. Madhavan, A., Cheng, M., 1997. In search of liquidity: block trades in the upstairs and downstairs
markets. Review of Financial Studies 10, 175–203.
Martens, M.P.E., 1997. Interaction between Financial Markets. Tinbergen Institute Research Series, Thesis Publishers, Amsterdam.
McInish, T., Wood, R., 1992. An analysis of intraday patterns in bidrask spreads for NYSE stocks. Journal of Finance 47, 753–764.
Newey, W.K., West, K.D., 1987. A simple, positive semi-definite, heteroskedasticity and autocorrela-tion consistent covariance matrix. Econometrica 55, 703–708.
Newey, W.K., West, K.D., 1994. Automatic lag selection in covariance matrix estimation. Review of Economic Studies 61, 631–653.
Pagano, M., Roell, A., 1990. Trading systems in European stock exchanges: current performance and¨ policy options. Economics Policy 10, 65–115.
Pagano, M., Roell, A., 1992. Auction and dealership markets. What is the difference? European¨ Economic Review 36, 613–623.
Pagano, M., Roell, A., 1993. Shifting gears: an economic evaluation of the reform of the Paris Bourse.¨
Ž .
In: Conti, V., Mamani, R. Eds. , Financial Markets’ Liberalisation and the Role of Banks. Cambridge Univ. Press, pp. 152–177.
Pirrong, C., 1996. Market liquidity and depth on computerized and open outcry trading systems: a comparison of DTB and LIFFE Bund contracts. Journal of Futures Markets 16, 519–543. Schmidt, H., Iversen, P., Treske, K., 1993. Parkett oder computer? Zeitschrift fur Bankrecht und¨
Bankwirtschaft 5, 209–221.
Shyy, G., Lee, J.-H., 1995. Price transmission and information asymmetry in Bund Futures markets: LIFFE vs. DTB. Journal of Futures Markets 15, 87–99.
