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
Ž
. Ž
. e.g. Karpoff, 1987; Lee et al., 1994 ; equally interesting is the finding of Berry and Howe 1994 that
there is only a moderate positive relationship between public information and volume, but an insignificant relation with volatility.
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
DTB’s market share in all three contracts differs only slightly from that in the front month contract. The mean difference between DTB’s market share in all three contracts traded at the same
time and it’s market share in the most actively traded contract is less than 0.1.
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
Since the basket of deliverable bonds normally is the same at both exchanges, the delivery option in the Bund-Future contract should have about the same value for both, the DTB- and the LIFFE-con-
tract. However, trade at LIFFE stops 1 day ahead of DTB, which is likely to render futures prices somewhat lower at DTB.
after the start-up phase markets are well integrated and arbitrage between both exchanges works reasonably well.
3.3. Estimating price Õolatility 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 s high–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 TMS s ln MS r 1 y MS in all
t t
t
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 .
For a survey on autoregressive conditional heteroskedasticity models, see Bollerslev et al. 1992 .
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
a R
BG a
R BG
a R
BG
2 2
2
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: TMS s a q a t q a x q a x
q a TMS q e ,
t 1
2 t
3 ty1
4 ty1
t
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 R
2
, and the Breusch–Godfrey
2
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.
The test results of Hypothesis 4 are somewhat weaker. After the start up-phase and before June 1994, DTB’s market share is positively related to the average time
between successive transactions, i.e. negatively related to the trading frequency. Again, this relation fades away after June 1994. This may be explained by the fact
that the daily trading frequency at DTB reached a fairly high level of about four
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
TV s a q b HL
q c ATV
q h ,
Ý Ý
i t i
it tyt
it tyt
i t
ts0 ts1
i s D DTB , L LIFFE 3.1
Ž .
Ž .
Ž .
5 5
HL s a q b HL
q c ATV
q d TMS q h .
Ý Ý
t 3
3t tyt
3t tyt
3 t
3 t
ts1 ts0
t3,4
TV denotes the log trading volume at exchange i at day t, HL the log ratio of
i t i t
the highest over the lowest transaction price at day t and ATV the log aggregate
t
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
Ž .
15
3.1 by GMM. Since 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 Ž
. excluding 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 b y b is significant at the
L0 D 0
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 .
As instruments we use the predetermined variables in system 3.1 and daily trading volumes and daily volatility estimates from GILT Futures trade at the LIFFE and from DAX trade at the Frankfurt
floor. DAX trading volume is the market value of all trades in stocks included in the DAX.
Table 2 Ž
. Estimation results for the system 3.1
TV b
b c
BG J
D D,0
D,1 D,1
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
TV b
b c
BG
L L,0
L,1 L,1
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 b
c d
BG
3,1 3,0
3
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–low
D 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
TMS s a q b t q c HL
q d TMS q e ;
Ý
t 1
1 1t
tyt 1
ty1 1 t
ts0 3
HL s a q b t q c HL
q d TMS q e . 3.2
Ž .
Ý
t 2
2 2t
tyt 2
t 2 t
ts1
Ž .
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 c
1,0
and d are displayed in Table 7 in Appendix C. As hypothesized, the high–low
2
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 .
Although aggregate trading volume is excluded in system 3.2 , the negative relation between market share and volatility might be driven by the volume effect.
We would like to separate volatility and volume effects. Therefore, we add a two-step regression as an additional test. In the first step, we regress log aggregate
trading volume on the standard deviation of price changes and on lagged trading volume:
ATV s a q a t q a UGV q a UGV q a ATV
q j , 3.3
Ž .
t 1
2 t
3 ty1
4 ty1
t
with UGV denoting unconditional GARCH volatility estimates. j is the de-
t t
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, on
t
the aggregate trading volume not explained by volatility, j , and on the lagged
t
market share: TMS s b q b t q b UGV q b UGV
q b j q b TMS q e .
3.4
Ž .
t 1
2 t
3 ty1
4 t
5 ty1
t
Table 3 presents the parameter estimates for UGV and j . Most parameters are
t t
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 R
2
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
2
Contract b
b R
BG
2 4
Mar-91 y0.121
y0.204 0.44
3.40 Jun-91
y0.273 y0.136
0.29 1.26
Sep-91 y0.307
y0.275 0.22
2.53 Dec-91
y0.337 y0.083
0.75 4.08
Mar-92 y0.911
y0.508 0.81
3.81 Jun-92
y0.378 y0.315
0.39 5.99
Sep-92 y0.153
y0.158 0.18
0.90 Dec-92
y0.232 y0.330
0.40 3.27
Mar-93 y0.561
y0.111 0.22
9.21 Jun-93
y0.282 y0.375
0.50 4.22
Sep-93 y0.103
y0.087 0.20
1.93 Dec-93
y0.099 y0.329
0.31 6.89
Mar-94 y0.069
y0.219 0.60
6.44 Jun-94
y0.063 y0.357
0.47 4.73
Sep-94 y0.010
y0.009 y0.03
5.14 Dec-94
0.009 0.035
0.16 1.52
Mar-95 y0.167
y0.180 0.73
3.16 Jun-95
y0.072 0.071
0.08 6.20
Sep-95 0.034
0.004 0.24
3.04 Dec-95
0.018 y0.081
0.24 3.54
Estimates of b and b are shown. In addition, the adjusted R
2
, and the Breusch–Godfrey test statistic
2 4
for autocorrelated residuals are displayed.
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.
Until 1994, trading volume at the LIFFE reacts clearly stronger to price volatility than at the DTB. In addition, price volatility is strongly driven by the
aggregate trading volume. This might be explained by large orders that preferably
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 volume
16
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
