Journal of International Financial Markets,
Institutions and Money 8 (1998) 299 – 324
Two months in the life of several gilt-edged
market makers on the London Stock Exchange
Paolo Vitale *
London School of Economics, Department of Accounting and Finance, Houghton Street,
London WC2A 2AE, UK
Received 30 June 1998; accepted 31 July 1998
Abstract
We investigate the micro structure of the UK gilt market studying the behaviour of several
gilt-edged market makers on the London Stock Exchange. Through a structural model of the
price process we can test different microstructural hypotheses, concerning information
asymmetries, transaction and inventory carrying costs, and market liquidity. Our results
suggest that inventories do not alter the price process in the gilt market. Moreover, in
contrast to customer orders, inter-dealer transactions possess an information content.
Transaction costs in the inter-dealer market are also substantially smaller than those for
external customers. © 1998 Elsevier Science B.V. All rights reserved.
Keywords: Liquidity, Micro structure, UK gilt market
JEL classification: G10; G15; G19
1. Introduction
The past 10 years have seen a large number of contributions analysing the micro
structure of securities markets (see Goodhart and O’Hara, 1997 and O’Hara, 1995).
However, a striking aspect of this strand of research is that nearly all the analysis
has concentrated on equity markets. In particular, despite the size and the importance of bond markets, there are virtually no empirical or theoretical studies of
* Corresponding author. Tel.: + 44-171-955-7230; Fax: +44-171-955-7420; e-mail: [email protected].
1042-4431/98/$ - see front matter © 1998 Elsevier Science B.V. All rights reserved.
PII: S 1 0 4 2 - 4 4 3 1 ( 9 8 ) 0 0 0 3 8 - 9
300
P. Vitale / Int. Fin. Markets, Inst. and Money 8 (1998) 299–324
their micro structure1. A reason for this omission might be the lack of proper
data sources — most bond markets do not have a centralised structure, which
impedes the collection of the necessary transaction data. Furthermore, the presumption that in these markets inventory and asymmetric information effects do
not play an important role may have contributed to a lack of interest in the
micro structure of bond markets among researchers. We take an opposite view
and argue that this lack of interest is completely unjustified. In fact, given their
specific organisation, bond markets may shed light on the controversial relation
between market structure and the price process.
The only other study of the UK gilt market was recently carried out by
Proudman (1995). He tested a series of classical predictions of market microstructure theory by applying the VAR approach of Hasbrouck (1991). In this
paper we suggest a rather different strategy, which takes account of the institutional differences between the UK gilt market and the New York Stock Exchange (NYSE) for which the VAR approach was originally proposed. While
the NYSE possesses a completely centralised trading organisation, a multiple
dealer system operates in the UK gilt market. Since its dealers do not reveal
their transactions to other traders, the market is not transparent. As a result, an
analysis of the type suggested by Lyons (1995) for the foreign exchange market,
which is similar in many respects to a bond market, is more appropriate: instead
of estimating an unrestricted VAR model for the transaction price and the
signed order size of the entire market a study of individual dealers is undertaken.
Our analysis of the price process suggests some interesting results. Firstly,
inventory carrying costs are limited and do not affect the transaction price in
the gilt market. Secondly, inter-dealer transactions seem to carry information, as
opposed to customer orders. Thirdly, trading costs are very small, indicating
that this market is extremely deep. Finally, inter-dealer transactions are executed
at more favorable prices.
The paper is organised as follows. In Section 2, we briefly discuss the institutional arrangements regulating the UK gilt market, presenting a preliminary
analysis of its characteristics and regularities. In Section 3, a general structural
model for the determination of the price process is introduced. This model
captures both inventory and asymmetric information effects and takes account
of the decentralised structure of the market. In Section 4, the results of the
estimation of the model are discussed. In Section 5, we study the possibility that
non-linear components play an important role for the price process. In Section
6, the structural model is used to derive estimates of the effective bid-ask spread
and investigate the relationship between the volume of trading, the price volatility and the cost of trading. Section 7 completes the paper.
1
See Fleming and Remolona (1997) for an exception.
P. Vitale / Int. Fin. Markets, Inst. and Money 8 (1998) 299–324
301
2. The structure of the UK gilt market
The UK gilt market is a continuous multiple dealer quote-driven market2. In this
market, several dealers, gilt-edged market-makers (GEMMs), quote on request bids
and asks for a number of different bonds, which differ by maturity and type. In this
activity GEMMs are facilitated by three inter-dealer brokers (IDBs), who allow
them to unwind inventory imbalances anonymously and rapidly, and by eight Stock
Exchange money brokers (SEMBs), which may lend bonds to a GEMM when he
receives a large market order from a client. GEMMs have exclusive access to IDBs
and SEMBs, whilst other members of the Stock Exchange, dealer-brokers, and
simple customers can only trade amongst themselves and with GEMMs. The
market does not have official starting and closing times and dealers are not required
to post firm quotes, though they must stand ready to trade. Moreover, dealers do
not have to disclose their trading activity, although they do have to report any
transaction they complete to the London Stock Exchange (LSE). As a consequence,
dealers cannot observe other GEMMs order flows directly3, so the market remains
opaque.
The UK government securities market is very large, but its activity is not intense,
if compared with the equity market. In particular, in October and November 1994
the total turn-over in UK gilts was £244.4 billion (£5.68 billion per day) compared
to £91.9 billion (£2.1 billion per day) in the domestic equity market, while the
average transaction size was £1.6 million versus £68 thousand in 1994. However,
the daily number of trades is much lower (2500 versus 31 000)4.
Proudman’s estimates of effective spreads suggest that the larger size of the gilt
market is translated into lower trading costs than in the equity market. Despite
these differences, the pattern of intra- and inter-day trading in gilts does not seem
to differ substantially from those observed in other markets5. In fact, Proudman
finds the usual U-shaped form for the intra-day volume and larger turn-over in
mid-week. He also concludes that there is a positive relationship between spreads
and turn-over, in contrast to popular models of asymmetric information, notably
Admati and Pfleiderer (1988) and Foster and Viswanathan (1990), but in line with
empirical studies of other markets (Hsieh and Kleidon, 1992; Mc Inish and Wood,
1993 and Foster and Viswanathan, 1993).
2
This description refers to 1994, the year for which data is available. See British Go6ernment
Securities: The Market in Gilt-Edged Securities, 1993, Bank of England and Dattels (1995), who also
describes other similar Government Securities Markets.
3
In fact, there is a significant difference between reporting data to a central office and revealing it to
the rest of the market. Even if the LSE publishes at the end of any trading day statistics on the volume
and the prices of transactions, this is not sufficient to make the market transparent.
4
See Quality of Markets Monthly Fact Sheet, London Stock Exchange, October and November 1994
and London Stock Exchange Quarterly, Spring 1995.
5
See Jain and Joh (1988), Mc Inish and Wood (1993), Foster and Viswanathan (1993), Gerety and
Mulherin (1992) for the NYSE, Chan et al. (1995) for the NASDAQ, De Jong et al. (1995) for the
SEAQ International and Hsieh and Kleidon (1992) for the foreign exchange market.
302
P. Vitale / Int. Fin. Markets, Inst. and Money 8 (1998) 299–324
In synthesis, it appears that the UK gilt, apart from its larger size, its peculiar
structure and regulation, is not significantly different from other securities markets.
In order to examine this in more detail, though, we need to investigate the price
process for gilts. The database of gilt transactions kept by the Quality of Markets
Group (QMG) of the LSE has been used to do this.
2.1. The data
All transactions in gilts completed on the LSE are reported to its QMG. We use
this dataset for the period October to November 1994. It records the time (to the
minute) and date, size, price and value of all trades completed on the LSE. It also
reports codes which permit individuating the counter-parties involved in any trade,
which of the two is buying (selling), if they are market-makers, dealer-brokers, or
simply customers, i.e. traders which are not members of the LSE. In the case of
transactions between market-makers, the dataset determines if they are through an
inter-dealer broker or direct, whilst when trades are amongst members of the LSE,
we can also determine if these are operating on their own or on behalf of a client.
When two members of the LSE complete a transaction directly, they both report
it to the QMG. Similarly, an indirect inter-dealer transaction is recorded four times,
since both market-makers report a trade with a broker, while he communicates the
trade to the QMG as two separate deals. This allows cross-checking for errors. On
the other hand, when a member of the LSE transacts with a non-member the latter
does not communicate the trade, so that there will be less certainty on the accuracy
of that report.
This gain in the quality of the data has a high cost in terms of data processing.
In fact, preliminary to any analysis, double reports have to be matched in order to
obtain a sequence of individual trades. This exercise can be problematic, since
reports can have significant time lags or differences in prices and values, especially
for indirect inter-dealer trades6. Furthermore, the dataset contains contra-trades—
that is, artificial trades which counteract mistakes. In these cases, we have to
affiliate the contra-trades with the original ones and delete them from the dataset.
For the analysis which follows we have decided to concentrate on only one gilt:
the 6% Treasury Stock 1999 (6% TL 99). This security has been selected because it
was by far the most actively traded in 1994 (Proudman, 1995) The 6% TL 99 was
first issued with an auction face value of £3.5 billion on 28 October 1993 and
successively tapped on 28 December 1993 and on 10 October 1994 for the face
value of 0.4 and £0.25 billion, respectively.
The decentralised organisation of a securities market poses an important question
regarding the diffusion of information about dealers order flows. Most empirical
research on decentralised markets implicitly assumes that when a market-maker
receives an order, this is immediately known by the rest of the market so that all
6
When there is a time difference, the moment the transaction was first reported is taken as the time
of the trade. Likewise, if differences in prices and values exist simple arithmetic means are used. Notice,
however, that the share of trades with these differences is minimal.
P. Vitale / Int. Fin. Markets, Inst. and Money 8 (1998) 299–324
303
dealers instantaneously adjust their quotes. However, this in practice is unlikely to
be true in the gilt market, as most transactions are the result of private bilateral
meetings among traders the outcome of which is not observable by other market
participants. This means that the transaction price for the market as a whole may
not adjust to orders in the way described by theory. So examining the trading
activity of all market participants may result in too much ‘noise’ in the price.
Consequently, we have decided to consider the activity of individual market-makers
for the 6% TL 99 gilt.
In particular, we have chosen to study the behaviour of only six of the existing
21 GEMMs. These dealers are the most active and account for more than 60% of
all the trades in the 6% TL 99 gilt for the period covered by our dataset.
Concentrating on a relatively small, but important, sample of GEMMs permits
conducting a significant econometric analysis of the price process in the UK gilt
market.
2.2. Descripti6e statistics
A preliminary analysis of our dataset confirms at an individual level the general
picture of the gilt market we observe in an aggregate study. Table 1 contains some
statistics on the transactions by the six market makers we selected. It clearly
indicates that the activity of GEMMs in the 6% TL 99 gilt is sparse, since the daily
number of trades is small for all dealers. Conversely, their sizes are very large, with
individual averages ranging from 1.29 to £4.60 million. A rough measure of the
liquidity of the market is given by the maximum size of the orders GEMMs can
execute. As Table 1 shows, this almost reaches £100 million, indicating a very deep
market.
The LSE dataset allows us to determine which side of any trade our market-makers take. Thus, in Table 2 we can distinguish between transactions in which the
Table 1
Comparative statistic: trades and turn-over
Dealer
Panel
Total
Daily
Daily
Daily
1
2
b
4
1013
23.6
39
10
1145
26.6
45
17
5
6
A: number of trades
average
maximum
minimum
484
11.3
30
1
Panel B: turn-o6er (in pounds)
Totala
2.23
Trade average sizeb
4.60
Trade maximum sizeb
45.2
Trade minimum size
1979
a
3
Billions of pounds.
Millions of pounds.
369
8.6
23
1
1.62
4.38
45.6
2300
1.88
1.85
44.9
452
2.36
2.06
81.3
398
503
11.7
30
1
2.72
5.40
88.3
1979
1140
26.5
58
10
1.47
1.29
90.5
614
P. Vitale / Int. Fin. Markets, Inst. and Money 8 (1998) 299–324
304
Table 2
Comparative statistic: trade sign decomposition
Dealer
1
Panel A: number of trades
Buy orders
224
Sell orders
260
Panel B: trade size (in pounds)
Buy average sizea
4.53
Sell average sizea
4.68
Buy maximum sizea
45.2
Sell maximum sizea
31.6
Buy minimum size
1979
Sell minimum size
9019
a
2
211
158
3.80
5.16
45.6
22.6
2300
9125
3
4
779
234
899
246
1.08
4.44
31.8
44.9
722
452
1.35
4.67
81.3
81.3
800
398
5
240
263
5.51
5.31
54.0
88.3
1979
9019
6
904
236
0.76
3.35
90.5
45.6
830
614
Millions of pounds.
counter-parties of the GEMMs buy from those in which they sell. Another distinct
feature of the behaviour of these six dealers emerges from this Table. In fact, whilst
the number of buy and sell trades are roughly the same for dealers one, two and
five, GEMMs three, four and six received far more buy orders than sell ones.
Likewise, while for the former group of dealers buy and sell trades have broadly the
same magnitude for the latter sell trades are on average much larger than buy
trades.
This asymmetry in the distribution of the sign of trades for the gilt contradicts
the general intuition that liquidity traders are more likely to be sellers than buyers
in a securities market7, but a simple phenomenon seems to be at work here. Table
3 clearly indicates that most trades with clients, that is agents which are non-members of the LSE, were buy orders, whilst transactions with other GEMMs and
dealer-brokers were equally divided between sells and buys8. In effect, in the UK
gilt market GEMMs buy gilts in large quantities when these are auctioned by the
Bank of England and then unwind their positions selling to investors and institutions. In support of this interpretation notice that the asymmetry in the number of
sales and purchases is particularly relevant for dealers three, four and six, who
mostly trade with non-members of the LSE.
In Fig. 1 the distribution of the size of trades is presented. For the second group
of GEMMs (i.e. dealers three, four and six) this distribution is clearly bimodal,
while there is a single mode for the first. The break-up of the distribution according
to the counter-party type shows that trades with other market-makers are larger.
This feature is of particular interest, because of the debate in the literature about
7
See Allen and Gorton (1992).
The LSE dataset reports whether a dealer–broker transacting with a GEMM is acting on behalf of
a customer or not. In the first case the transaction is classified as a trade between the market-maker and
a client.
8
P. Vitale / Int. Fin. Markets, Inst. and Money 8 (1998) 299–324
305
whether block trades have a greater information content. In the analysis to come,
we study the price process distinguishing between customer and dealer orders and
between large and small trades, so that we will be able to address this question.
In Fig. 2 we decompose the volume of trading and the price volatility of
transactions involving the six GEMMs according to the time of the day. The upper
panels present the share of total turnover per trading hour from 8:00 to 17:00,
whilst the lower panels reproduce the dynamics of the standard deviation of the
transaction price over the same period. For all dealers we find the usual U-shaped
pattern in the volume of trading observed in most markets and reported by
Table 3
Comparative statistic: trades by counter-partya
Dealer
1
2
3
4
5
6
Panel A: total
Clients
164
Gemms
291
Dealers
29
229
133
7
792
201
20
954
172
19
198
273
32
949
158
33
Panel B: buy orders
Clients
74
Gemms
137
Dealers
13
149
58
4
684
88
7
814
73
12
98
126
16
822
71
11
Panel C: sell orders
Clients
90
Gemms
154
Dealers
16
80
75
3
108
113
13
140
99
7
100
147
16
127
87
22
Panel D: turn-o6er
Clientsb
0.79
Gemmsb
1.24
Dealersb
0.20
0.92
0.66
0.04
0.96
0.82
0.10
1.50
0.81
0.04
1.30
1.25
0.16
0.65
0.63
0.20
Panel E: trade mean size
Clientsc
4.81
Gemmsc
4.27
Dealersc
6.75
4.00
4.96
6.17
1.21
4.08
5.02
1.58
4.73
2.32
6.58
4.58
5.13
0.68
3.97
6.04
Panel F: buy orders mean size
Clientsc
4.31
Gemmsc
4.28
Dealersc
8.38
3.44
4.58
6.27
0.71
3.65
4.63
1.04
4.73
1.49
6.35
4.92
5.01
0.41
4.20
4.43
Panel G: sell orders mean size
Clientsc
5.22
Gemmsc
4.27
Dealersc
5.43
5.04
5.25
6.03
4.38
4.41
5.24
4.58
4.73
3.75
6.81
4.30
5.25
2.44
3.79
6.85
a
‘Dealers’ stands for dealer-brokers, ‘clients’ for non-members of the LSE.
Billions of pounds.
c
Million of pounds.
b
306
P. Vitale / Int. Fin. Markets, Inst. and Money 8 (1998) 299–324
Fig. 1. Distribution of transaction size in October – November 1994.
Proudman, with most activity concentrated in the initial hours of the day. Turning
to the dynamics of the price volatility, we do not have a clear picture, even if we
notice some evidence of clustering around lunchtime.
P. Vitale / Int. Fin. Markets, Inst. and Money 8 (1998) 299–324
307
The existing literature on market micro structure suggests that regularities in the
intra-day dynamics of the price volatility and the trading volume can emerge if
some source of asymmetric information is relevant in the activity of market
Fig. 2. Volume and volatility in October – November 1994.
308
P. Vitale / Int. Fin. Markets, Inst. and Money 8 (1998) 299–324
participants. In this respect, the structural model of the price process we develop
and estimate in the following sections will help establish if the usual paradigm of
market microstructure theory applies to the UK gilt market.
3. A structural model of the price process
In market microstructure theory four different factors can account for the cost of
trading. First of all, market-makers can charge an order processing cost, since they
provide immediacy to their clients. An investor, which desires to trade in a security,
may find it inconvenient to post a limit order with a broker, whose execution may
be uncertain or slow. Using a market-maker on the other hand will guarantee the
deal will be executed rapidly (Demsetz, 1968). Secondly, since the dealer absorbs
any momentary imbalance between total demand and supply he faces the risk of
accumulating an undesired short or long position in the security. Then, he may set
his bid and ask prices in order to return his inventory to an optimal level. In other
words, the bid-ask spread is an instrument market-makers use to force their flow of
orders in a determined direction (Ho and Stoll, 1983). Thirdly, when some clients
have superior information on the fundamental value of the security, by imposing a
bid-ask spread dealer’s may transfer losses with informed traders to other customers (Glosten and Milgrom, 1985; Kyle, 1985). Finally, since dealers may possess
monopoly power on their order flow, they can charge a fee even when other factors
are not at work (Copeland and Galai, 1983; Leach and Madhavan, 1992, 1993;
Perraudin and Vitale, 1996).
In practice, there are two main ways we can investigate these four factors.
Hasbrouck (1991) suggests that a simple VAR approach should be used, arguing
that under the assumption that the relations between prices, trades and inventories
are captured by an unconstrained system of auto and cross-correlations, they can
be studied within a general vector autoregressive model. The alternative is a
structural model, which, despite its loss of generality, allows the disentangling of
the aforementioned micro-structural factors in the determination of the price
process. Moreover, using this approach it is easy to test the relevance of different
market microstructure hypotheses.
While Proudman (1995) follows Hasbrouck’s suggestion, we will consider a
general structural model of the price process (Lyons, 1995), which encompasses
several others (Ho and Macris, 1984; Roll, 1984; Glosten and Harris, 1988; Foster
and Viswanathan, 1990 and Madhavan and Smidt, 1991) and takes account of the
decentralised organisation of the UK gilt market. As we proceed with the description of the structural model, the specific reasons why we prefer this to a VAR
model will be clarified.
3.1. The price process model
In a specialist market, a structural model for the price process can be formulated
along the lines proposed by Madhavan and Smidt (1991). They assume that in the
P. Vitale / Int. Fin. Markets, Inst. and Money 8 (1998) 299–324
309
market for a risky security traders transact with a specialist at times t= 1, 2,…, T.
The liquidation value of the security at time T, fT, is the sum of T+ 1 ‘dividends’,
fT =STt = 0dt. These ‘dividends’ are independent and identically distributed random
variables with zero expected value. Thus, the fundamental value of the security at
time t is given by fT =Stt = 0di. Then, assuming transaction and inventory carrying
costs exist, the pricing rule of the specialist at time t will be linear in his expected
fundamental value, m st , the deviation of his inventory with respect to an optimal
position, It −Io, and the direction of the customer order, Ct :9
pt =m st −g(It −Io ) +cCt
(1)
where Ct =1 if the order is a buy and −1 if it is a sell. Before any transaction with
a trader, the specialist receives a public signal on the fundamental value, s pu
t :
pu
s pu
t = ft +e t
where e pu
is an idiosyncratic shock of variance s 2pu. Simultaneously, a trader
t
pu
observes s t and a private signal, s pr
t , also. This is given by:
pr
s pr
t = ft + e t
2
where e pr
t is also an idiosyncratic shock of variance s pr. Then, under the assumption of normality and independence of the errors, the trader will use the projection
theorem to update her expectation of the fundamental value, m tr
t :
pr
pu
m tr
t = us t +(1 −u)s t ,
where
u=
s 2pu
s 2pu +s 2pr
Finally, if she maximises a mean variance utility, given Eq. (1), her market order,
tr
q tr
t , will be a function of the security mix-pricing, m t − pt. Nevertheless, an
l
2
unpredictable liquidity component, et, of variance s l is also present in her order, so
that this becomes:
tr
l
q tr
t = a(m t −pt ) + e t
(2)
pu
t
The specialist employs the public signal, s , and the ‘information’ contained in the
client order to update his expectations of the fundamental value of the security and
fix the transaction price. Madhavan and Smidt prove that the corresponding
changes in the transaction price respect the following structural model:
g
c
Dpt =A + lq tr
t − It +gIt − 1 + Ct − cCt − 1 + ht,
p
p
(3)
9
Note that Eq. (1) is not derived from the solution of any utility maximisation problem, so that the
authors refer to it as a prototypical formulation, although it can be proved that under a very general
specification the optimal pricing strategy of the specialist is in fact given by Eq. (1). See Madhavan and
Smidt (1993).
P. Vitale / Int. Fin. Markets, Inst. and Money 8 (1998) 299–324
310
where
A= −g(1 −1/p)Io,
l=
1 −p
ap
and p is a coefficient which depends on the various parameters of the model. The
coefficient p determines the information content of the market orders of the
traders: for p small (l large) traders have a large informational advantage, that is
reflected in a relevant impact of trades on the transaction price. Conversely, for p
large (l small) the market orders do not convey any new information.
In synthesis, in Eq. (3) the coefficients of q tr
t , It − 1 and Ct − 1 capture three
different factors accounting for the cost of trading: l measures the information
content of the customer orders received by the specialist, whilst c and g are the
transaction and inventory carrying costs, respectively. In particular, g will be
positive if dealers are risk-averse and inventory-carrying costs are relevant, while
c accounts for all fixed transaction costs. This means that using this model we
can verify directly the relevance of the various market micro-structure hypotheses
on the price process, by testing for the significance of the coefficients in Eq. (3).
Madhavan and Smidt apply this model to the NYSE, which is a fully centralised market, because the specialist can observe all transactions and orders. As
mentioned above, this is not the case for the UK gilt market. Therefore, an
application of this structural model is only possible with respect to the activity of
individual market-makers, since they can only partially observe the order flows of
other dealers. In particular, through the connection with inter-dealer brokers they
can also obtain information on all trades between market-makers which are not
direct.
Lyons (1995) has proposed a modified version of the Madhavan and Smidt
model which can be applied to decentralised markets with inter-dealer brokers. In
this version, several market-makers deal in the same risky security amongst
themselves and with customers. For the price process of a single dealer, m, a
model similar to that discussed by Madhavan and Smidt applies, with the difference that the selected market-maker will observe before any trade both a public
signal and one on the total volume of inter-dealer trading, q bt which is given by:
q bt = %q dt +y bt ,
d
where y is an idiosyncratic shock of variance s 2n, which accounts for some noise
in the electronic communication system dealers are attached to, and q dt indicates a
single transaction between dealers completed through a broker. Lyons assumes
that these dealers have received informative orders from other traders and that
they place market orders similar to those of customers. In other words, if a
market-maker d posts an order with a broker, this is given by the following
expression:
d
t
q dt = a(m dt −pt ) +e id
t ,
P. Vitale / Int. Fin. Markets, Inst. and Money 8 (1998) 299–324
311
where m dt is his expectation of the fundamental value and e id
t is the usual idiosyncratic liquidity shock of variance s 2id. Lyons then proves that the structural model
for the transaction price modifies in the following way:
g
c
Dpt =A + fq bt +lq tr
t − It +gIt − 1 + Ct − cCt − 1 + ht
p
p
(4)
Here the coefficient of q bt , f, is the equivalent of l for the inter-dealer transactions:
when other market makers possess superior information on the value of the
security, these transactions (q bt ) convey a signal and influence the transaction price.
4. Model estimation for the gilt market
We can now consider the estimation of Eq. (4) for the gilt we have analysed in
Section 2. In order to do so, we need first to prepare the data. In particular, we
need to determine the direction of all trades, that is if transactions are buyer or
seller initiated. Trades between market-makers and customers are straightforward
to assign, because they are necessarily started by the latter. Thus, since our dataset
reports for any transaction if our GEMMs are buying or selling, when a transaction
is with a client the determination of the corresponding direction of trade is
automatic: if the client is recorded as the buyer, the trade is buyer initiated and vice
versa if the client is the seller.
However, the determination of the direction of inter-dealer trades is not so
simple, since it is always possible that our GEMMs initiate trades with other
market-makers. When quotes from market-makers are recorded, the direction of
inter-dealer trades can be determined using the method put forward by Lee and
Ready (1991), which is based on a comparison between the transaction price and
the prevailing best quotes. For the gilt market no information on quotes is available
and we therefore have to rely on the tick test.
In the application of the tick test inter-dealer trades are isolated, so that the
comparison between transaction prices is carried out using only trades amongst
market-makers. In other words, to classify the direction of a trade we compare the
current transaction price with that of the previous trade that involve market-makers. The reason for this is simple: spreads in the inter-dealer market are generally
smaller than those for normal customers, so that when applying the tick test, if we
used all the transactions we would undergo a systematic error in the classification
of the direction of trades.
In the inter-dealer market, transactions are often mediated by an IDB10. In this
case, it could be possible to detect the direction of trades from the record of
different times the three parties involved, the dealers and the broker, report the
transaction. If the market-maker posting a limit order with a broker reported it
first, it would be possible to determine which of the two dealers initiated the
10
Dattels (1995) found that 96% of inter-dealer trades in the UK gilt market are through an IDB.
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P. Vitale / Int. Fin. Markets, Inst. and Money 8 (1998) 299–324
corresponding trade. But, as market participants in the UK gilt market have 15 min
(5 min for block trades) to communicate a trade to the QMG, the timing of the
reports is not useful in determining the direction of trades. In practice, we often
observe in the dataset that the broker communicates a transaction between marketmakers before the counter-parties do.
Another important transformation of the data we need before estimating our
structural model concerns transactions reported at the same moment. Since we use
transaction time rather than calendar time in the formulation of the model, the
observations we use have to be filed in the sequential order that trades are
completed. Thus, when in our dataset two trades are time-stamped in the same
minute we cannot determine which one comes first. As a consequence, they do not
represent distinct transactions and hence they are consolidated, in that a unique
total transaction is derived using the average price and the total signed order.
Finally, we had to eliminate changes in the price that occur during the night,
because our model concerns only the intra-day price process. Thus, after excluding
trades at the beginning of any day, trades among dealers in which our GEMMs act
as clients, and consolidating transactions reported in the same minutes, we ended
up with six files for the six dealers containing 215, 203, 773, 900, 223 and 816
observations, respectively. Notice that, inter-dealer trades are still used to determine
the inventory positions of the selected market-makers. This mitigates the problem
of multi-collinearity in the estimation of Eq. (4), because their inventories change
both when the GEMMs act as clients and as market-makers.
Each file contains the change in the transaction price charged by the GEMM,
Dpt, the direction and the signed quantity of any customer trade, Ct and q tr
t , where
is
positive
(negative)
for
a
buy
(sell)
order,
the
total
signed
quantity
of
q tr
t
inter-dealer trading since last customer order excluding trades with our selected
market-maker, q bt , his inventory position, It, the time any customer trade is
completed and a dummy variable indicating if the client is another dealer or not.
When we turn to the estimation of model (4) we need to consider the properties
of its error term, ht. It is not difficult to see that ht follows an MA(1) process. Thus,
the OLS estimator is not efficient and the maximum likelihood method should be
used to estimate the coefficients of the model11. In Table 4 we report the results of
the estimations of model (4) for the six GEMMs. We indicate the estimated
coefficients along side some specification statistics, as the corrected coefficient of
multiple correlation, R( 2, the Durbin – Watson statistic, DW, for the error term, ht,
and other diagnostic tests.
In Table 4 the MA(1) coefficient is significant and, as suggested, negative for all
regressions, apart from dealer one, while the presence of negative serial correlation
of, ht, is also signalled by the high values of the Durbin–Watson statistic. As we
11
Given the ergodicity of the process and the large number of observations, we can use a quasi-maximum likelihood method based on the Gauss–Newton algorithm (Harvey, 1989). Notice that for the
efficiency of the estimator we need to start the Gauss – Newton algorithm from consistent estimates of
the coefficients and then just run one iteration. The OLS estimator of the coefficients of an MA(1) model
is not efficient but consistent, so we can use it as a starting point.
P. Vitale / Int. Fin. Markets, Inst. and Money 8 (1998) 299–324
313
Table 4
Quasi-maximum likelihood estimation of the linear model with MA(1) errorsa,b,c
Dealer
1
2
b0
b1
b2
b3
b4
b5
b6
b7
0.9688**
0.1577**
−0.1072**
−0.0856**
0.0910**
1.3886**
−1.0316**
0.0242**
0.8850**
0.1994**
−0.2303**
−0.0350**
0.0303**
4.7932**
−2.6575**
−0.0560**
0.081
1.859
15.56
3.179**
20.06**
0.606
0.257
2.113
12.94
2.16*
16.32**
0.993
Diagnostic tests
R( 2
DW
Serial correlation
Skewness
Kurtosis
Heteroscedasticity
a
3
−0.5534**
0.1319**
−0.0722**
−0.0869**
0.0842**
4.0380**
−1.9859**
−0.8315**
0.002
3.003
198**
207**
2574**
93.4**
4
5
0.3874**
0.1459**
−0.0219**
0.0103**
−0.0080*
2.9880**
−2.8114**
−0.2233**
1.83**
0.1205**
−0.0012
−0.0320**
0.0366**
−0.0651
−0.5592**
−0.0879**
0.165
2.399
39.34**
13.85**
130.8**
6.592*
6
0.031
2.117
8.180
5.015**
13.85**
0.120
0.3409**
0.1166**
0.5478**
0.1587**
−0.1527**
2.8533**
−3.6374**
−0.4790**
0.231
2.804
138.4**
7323.4**
412.6**
186.5**
Dpt = b0+b1q bt +b2q tr
t +b3It+b4It−1+b5Ct+b6Ct−1+ht
pu
ht =h pu
t +b7e t−1.
The coefficients b1, b2, b3 and b4 are multipled by 106.
Prices are in pence.
* Indicates a significance level at 5%.
** Indicates a significance level at 1%.
b
c
incorporate in the model a transaction cost term, a negative value for the
MA(1) coefficient b7 cannot be the consequence of the bid-ask bounce. On the
contrary, if the specification of the model is correct, b7 measures the degree of
asymmetry between public and private information. So the larger value of b7
for dealers three, four and six signals a higher level of information asymmetries.
Notice that these dealers are exactly those who trade the most with external
customers.
However, the diagnostic tests for the Gaussian linear model with MA(1) errors clearly casts some doubts on the validity of the estimates of the coefficients
of Eq. (4). In fact, if the assumption of Section 3.1 were correct the error term,
ht, should not present serial correlation beyond the second lag. Yet, the Portamentau test clearly shows that serial correlation up to the tenth lag is significant
for some of the regressions. Furthermore, the usual tests of the departure from
indicate both skewness and kurtosis. This sugthe normality of the shock e pu
t
gests the presence of fat tails in the distribution of e pu
t , a very common phenomenon in financial time series. The same argument applies for the heteroscedasticity we detect in e pu
t . In our dataset observations are not equally spaced in
calendar time, which may be one reason why we observe the departure from
homoscedasticity.
P. Vitale / Int. Fin. Markets, Inst. and Money 8 (1998) 299–324
314
Provided that the specification of model (4) is correct, even if its error term does
present serial correlation and heteroscedasticity, the OLS method is more appropriate and inference on the model coefficients can be carried out using the Newey–
West heteroscedasticity and auto correlation consistent estimator of their standard
errors. In Table 5 we report these OLS estimates. Their standard errors, which have
been estimated assuming that the maximum order of serial correlation in the errors
is ten, are generally much larger than those of in Table 4.
The regression results of Table 5 propose some interesting conclusions. All in all,
we do not reject the following restrictions on the model coefficients: b0 = 0, b2 = 0,
b3 =0 and b4 =0. Although, we do reject the null hypothesis of b1 = 0 against the
alternative of b1 \0. Likewise, we can reject the combined null hypothesis that
b5 =0 and b6 =0. In practice, these results are consistent with the following values
for the underlying parameters of the structural model:
p =1, g= 0, f \ 0, c \ 0.
Thus, Table 5 suggests that the inventory effect is not detected for any GEMM
in our sample, i.e. g =0. This result is in line with those of Madhavan and Smidt
(1991) and Proudman (1995) and might be explained by the existence of various
instruments to hedge large inventory imbalances. In particular, GEMMs can trade
in future markets and sell (buy) bonds to (from) SEMBs, they can also combine
opposite inventories in strongly correlated bonds. Although, if the optimal level of
the inventory, Io, varies over time we can obtain insignificant values for b3 and b4
Table 5
OLS estimation with Newey–West standard errorsa,b,c
Dealer
1
2
3
4
5
6
b0
b1
b2
b3
b4
b5
b6
R( 2
DW
F-Test
0.9838
0.1577**
−0.1028
−0.0863**
0.0916**
1.3556***
−1.0260*
0.081
1.860
4.159***
0.8690
0.1980***
−0.2265
−0.0294
0.0247
4.8704***
−2.6796***
0.257
2.107
12.65***
−0.1818
0.1848***
−0.0010
0.0685
−0.0704
4.0640***
−2.8639***
0.003
3.003
1.39
0.3827
0.1634***
−0.0141
0.0219
−0.0195
2.8781***
−2.7123***
0.166
2.392
30.80***
1.8298***
0.1109**
−0.0131
−0.0278
0.0333
1.590
−0.5746
0.032
2.095
2.215**
1.4804
0.1178***
0.8120*
−0.1035
0.1009*
1.9609***
−3.3210***
0.264
2.626
49.6***
a
Dpt = b0+b1q bt +b2q tr
t +b3It+b4It−1+b5Ct+b6Ct−1+ht.
The coefficients b1, b2, b3 and b4 are multipled by 106.
Prices are in pence.
* Indicate levels of significance at 10%.
** Indicate levels of significance at 5%.
*** Indicate levels of significance at 1%.
b
c
P. Vitale / Int. Fin. Markets, Inst. and Money 8 (1998) 299–324
315
even when an inventory effect is at work (i.e. g\ o).12 Snell and Tonks (1995, 1996)
allowing for changes in Io find some evidence of inventory effects on the LSE.
Table 5 also signals the absence of an information content in customer orders.
This result is in contrast with findings of several empirical investigations for the
NYSE (for instance Hasbrouck, 1991; Madhavan and Smidt, 1991), but is not
dissimilar from those of Snell and Tonks (1995) for the UK equity market.
Although, even here, we can observe an insignificant value of b2 when p" 1,
because of a time-varying optimal inventory level13. An alternative explanation,
which does not involve speculative motives, is that block trades have an information content while small trades do not. We will investigate this possibility in Section
5, where we study non-linear components in the price process.
In his analysis of the UK gilt market Proudman has considered the market as a
whole. Assuming that GEMMs trade among themselves merely to adjust their
inventory positions, he stripped out all inter-dealer trades. Our regressions suggest
that this may lead to a loss of valuable information: the positive value of b1
indicates that inter-dealer trading may be an effective way for market-makers to
‘sell’ information to each other. In this situation, as Perraudin and Vitale (1996)
suggest, decentralisation may increase the speed with which information is disseminated in the market, since informative inter-dealer trades may stimulate price
experimentation on the part of dealers. This thesis contrasts with that of other
researchers, notably Flood (1994), who claim that decentralisation brings about
inefficiency and distortion.
A problem with this interpretation is that it is not clear why orders from
market-makers are informative, but customer orders are not. According to the
market micro-structure literature, inter-dealer trades are informative only insofar as
they reflect information dealers have previously gathered from customer orders.
12
Suppose in model (4) the market-maker sets the optimal level of the inventory for speculative
reasons. Hence Io varies with the value of the security,Iot =v(m m
t −pt ) and Eq. (1) becomes:
pt = m m
t − g%It + c%Ct,
where
g% =g/(1+gv),
c% =c/(1+ gv)
So in Eq. (4) the constant disappears and b4 underestimates g. Moreover, an error-in-the-variable
problem can also plague our estimates and standard errors, in that, because the data is not available, we
do not know the initial inventory position of our GEMMs.
13
tr
Suppose, in fact, that Iot = v(m m
t − mt ), then the coefficient of the customer order in Eq. (4), q t ,
becomes:
l=
(1−p) (1− ag)
ap
a
Thus, for g close to 1/a we can have a zero value for l even when p is different from 1.
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P. Vitale / Int. Fin. Markets, Inst. and Money 8 (1998) 299–324
Nevertheless, dealers can gather information from other private sources: for
example, transactions in related derivatives products or proprietary trading.
5. Analysis of non-linear components
Another issue which has been debated in the market microstructure theory
concerns the effect of block trades on the transaction price and the cost of trading.
In fact, while Demsetz (1968) suggests that transaction costs decrease with the order
size, Easley and O’Hara (1987) claim that informed traders have an incentive to
place large market orders, so that block trades have a more pronounced information content and a larger impact on the transaction price.
A unified method to assess the relevance of non-linearities in the price process
does not exist: researchers have used different strategies. Holthausen et al. (1987),
Gemmill (1996), Board and Sutcliffe (1995) and Proudman (1995) employ an
event-study approach to see if block trades have permanent effects on the transaction price. Hasbrouck (1991) and Proudman (1995) consider quadratic terms in the
specification of their VAR models. Madhavan and Smidt (1991) and Breedon
(1993) analyse piecewise linear functions of the order size, while Easley et al. (1994)
estimate an extended version of the original model discussed by Easley and O’Hara
(1987).
Since so far we have used a structural model to investigate the price process, the
application of a piecewise linear function is a natural candidate for the analysis of
block trades. In this respect, we estimate the following extension of Eq. (4)
H
tr
− tr
Dpt =b0 +b1q bt +b2q tr
t + % dhxh (q t − q h )+ b3It + b4It − 1 + b5Ct
h=1
+b6Ct − 1 +ht
(5)
where q̄ tr
h indicates a knot of the piecewise linear relation, H is the number of knots
tr
tr
and xh an indicative function, in that for q̄ tr
h positive xh = 1 if q h \ q̄ h and zero
tr
tr
tr
otherwise, for q̄ h negative xh =1 if q h B q̄ h and zero otherwise.
In Table 6 we report the OLS estimates of the coefficients of model (5) with
Newey–West standard errors for the six GEMMs. In these regressions two knots
have been included in order to capture the difference between large and small, buy
and sell orders. The selected knots for the regressions are q̄ tr
1 = − £1 million and
=£1
million
face
value.
The
selection
of
these
knots
is
based
on the distribution
q̄ tr
2
of the order size of the trades with other dealers: as Fig. 1 indicates, in our dataset
most of the transactions with other GEMMs are larger than £1 million face value.
This selection is probably a reasonable way to distinguish between large and small
orders, although regressions with other choices of the knots give similar results.
Table 6 contains some interesting results. Firstly, most of the conclusions
regarding model (4) hold even when non-linear components are inserted. Thus, the
inventory effect remains insignificant. At the same time, the coefficients of the
P. Vitale / Int. Fin. Markets, Inst. and Money 8 (1998) 299–324
317
Table 6
OLS estimation of spline model with Newey–West standard errorsa,b,c
Dealer
1
2
3
4
5
6
b0
b1
b2
b1
b2
b3
b4
b5
b6
R( 2
DW
F-Test
1.5505*
0.1612***
−2.6488
2.6905
2.4756
−0.0848*
0.0890**
3.4615**
−1.1073**
0.082
1.893
3.385***
2.1658***
0.1965***
−1.0237
1.1476
0.4632
−0.0253
0.0213
5.1473***
−2.6666***
0.285
2.079
11.07***
0.4699
0.1821***
0.2083
−0.0401
−0.4273
0.0828
−0.0852
3.6858***
−2.9139***
0.001
3.002
1.09
0.7500
0.1685***
−2.2937**
2.7588***
2.2773**
0.0228
−0.0189
2.9020***
−2.6559***
0.190
2.362
27.28***
2.1646***
0.1115**
−0.7355
0.7765
0.6497
−0.0304
0.0340
0.8930
−0.6004
0.028
2.092
1.794*
−0.7010
0.0982**
−5.5253***
5.7511***
6.8101***
−0.0931*
0.0888*
5.0057***
−3.5815***
0.343
2.341
54.1***
a
tr
tr
Dpt = b0+b1q bt +b2q tr
t +%hdhxh (q t −q̄ t )+b3It+b4It−1+b5Ct+b6Ct−1+ht
The coefficients, b1, b2, b3 and b4 are multipled by 106.
Prices are in pence.
* Indicate levels of significance at 10%.
** Indicate levels of significance at 5%.
*** Indicate levels of significance at 1%.
b
c
direction of trades, b5 and b6, are significant and have the right sign for the
regressions apart from that of dealer five. The coefficient of the signed quantity of
inter-dealer trading, bl, is also always positive and significant.
Secondly, new results emerge. In particular, when we introduce the terms
tr
dhxh (q tr
t −q̄ h ) in the regressions for dealers four and six, we observe that b2
becomes significantly smaller than zero, while the coefficients of the non-linear
terms, dl and d2, are significantly larger than zero. For other dealers this is not true
and, therefore, introducing non-linearities does not improve the fitting of the
model.
tr
the impact on the transaction
Now, in model (5) for a buy order exceeding q −
2
tr
tr
tr
price is b2q t +d2(q t −q̄ 2 ). This means that block trades have a larger effect on the
transaction price if d2 \0. An analogous argument applies to sell orders. Breedon
(1993) and Gemmill (1996) find evidence that block trades have a greater impact on
prices for the UK equity market, while Proudman (1995) obtains contrasting
conclusions in the gilt market. Our results are clearly mixed and might suggest
different styles of market making on the part of our GEMMs.
We can interpret the values of the coefficient b2, d1 and d2 for dealers four and
six as suggesting that large customer orders have an information content, but small
ones do not. Thus, for small trades transaction costs are decreasing in the order
size, while for large ones asymmetric information augments the cost of trading.
However, if dealers act strategically and engage in price experimentation, they
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P. Vitale / Int. Fin. Markets, Inst. and Money 8 (1998) 299–324
might find it convenient to offer discounts to informed customers when these place
small orders, as suggested in the literature by Leach and Madhavan (1992, 1993),
Naik et al. (1994), Perraudin and Vitale (1996). Hansch and Neuberger (1996) find
that dealers act strategically on the LSE, supporting this thesis. Moreover, dealers
four and six trade mostly with external customers who might have a smaller
bargaining power and to which GEMMs can impose larger transaction costs.
Finally, Table 6 indicates for all dealers that dl is not significantly different from
d2. This means that we do not observe significant asymmetries between purchases
and sales. This is rather surprising, since it is common opinion that buy orders are
potentially more informative than sell ones.
Before we employ our structural model to investigate other aspects of the micro
structure of the gilt market, we need to stress an important aspect of our
regressions. Only part of the volatility of the transaction price for the six GEMMs
is captured by our models (4) and (5). In particular, the values of the coefficient of
multiple determination, R( 2, for dealers three and five are extremely low and in
general, for all regressions, at least 2/3 of the variability of the dependent variable
remains unexplained. In other words, there are other factors behind the determination of the transaction price in these markets we do not take into account. This is
however common to most of the empirical research on market micro structure
(Madhavan and Smidt, 1991; Lyons, 1995).
Another point regarding our structural model is worth making before we turn to
its applications. While there has been a lot of discussion on the information content
of market orders and their impact on the transaction price, they do not seem to
contribute a great deal to the fit of our regressions. In fact, even if the corresponding coefficients are significant, when we eliminate the order size term from the
regressions of Tables 5 and 6 we do not observe a large reduction in R( 2. Madhavan
and Smidt (1991) discuss a number of institutional features of the NYSE which
may cause a downward bias in the estimation of the coefficient of the order size.
Even if these features are also at work on the UK gilt market, we should still
conclude that information asymmetries are not so relevant in its functioning.
Consequently, the decentralisation of the UK gilt market should not have any
important effect on its performance.
6. Applications
6.1. The effecti6e cost of trading
The estimation of the effective cost of trading represents a question of great interest
regarding the micro structure of securities markets. In principle trading costs in a
dealer market are measured by the difference between ask and bid prices. Anyway,
in measuring spreads it is important to distinguish between quoted and effective (or
realised) spreads, since dealers quotes are not firm and actual prices generally fall
well within quotes (Reiss and Werner, 1994; Board and Sutcliffe, 1995). As a result,
P. Vitale / Int. Fin. Markets, Inst. and Money 8 (1998) 299–324
319
even when quoted spreads are observable, they do not necessarily represent an
unbiased estimator of the effective costs of trading.
In the past several ways to estimate the effective bid-ask spread have been
suggested. Roll (1984) proposed a very simple estimator based on the first order
autocovariance of price returns. This estimator is unbiased only if the spread
represents the cost of immediacy. When spreads are affected by inventory and
asymmetric information costs (Stoll, 1989), this estimator can be corrected using
the conditional probabilities that consecutive trades are in the same direction
(Choi et al., 1988). But even when corrected, this estimator can be very noisy, if
the fundamental value of the security is subject to idiosyncratic shocks (Harris,
1990).
Using our structural model we can derive a simple measure of the effective
bid-ask spread. According to a common definition, this is given by the cost of an
immediate round-trip transaction, which is a buy order followed by a sell order of
the same size. Since this measure depends on the order size, we actually define an
estimator of a function, s(q tr) = p(q tr) −p(− q tr).
While for normal size orders this is given both in model (4) and (5) by:
s(q tr) = 2(b5 +b2q tr),
for large trades we will have in the second case:
tr
tr
s(q tr) = 2(b5 +b2q tr) +d1(q tr +q̄ tr
1 )+ d2(q − q̄ 2 )
In Table 7 we report the estimates of s(q tr) for several values of q tr
t . These are
obtained using model (4) for dealers one, two, three and five, and model (5) for
dealers four and six. We also report simple estimates obtained using the method
suggested by Roll (1984). Table 7 shows that Rolls estimate clearly underestimates
the effective cost of trading and confirms that the gilt market presents very small
trading costs: in fact, the estimates of the effective spread are very close to 2/32.
Table 7
Estimation of the Effective Spread
Dealer
1
Madha6an and Smidt’s estimate a
10th percentilec
0.0284
25th percentile
0.0268
50th percentile
0.0186
75th percentile
0.0141
Average trade size
0.0174
2
3
4
5
6
0.1076
0.1065
0.0945
0.0737
0.0846
0.0899
0.0899
0.0899
0.0899
0.0899
0.0640
0.0637
0.0630
0.0135
nab
0.0329
0.0295
0.0208
0.0006
0.0015
0.1101
0.1096
0.1084
0.0435
na
0.0086
0.0157
0.0106
0.0032
0.0138
Roll’s estimator a
0.0025
a
As percentage of the average size.
na indicates a negative spread.
c
From the distribution of the trade size.
b
P. Vitale / Int. Fin. Markets, Inst. and Money 8 (1998) 299–324
320
Table 8
The dynamics of the spread during the daya
Dealer
1
2
3
4
5
6
8:00–11:30
11:30–14:30
14:30–17:00
nab
0.0200
0.0074
0.0459
0.0109
0.0068
0.0449
0.0463
0.0549
0.1001
0.0203
0.0481
na
na
0.0143
0.1442
0.0885
0.0553
a
b
As percentage of the average price, calculated for the median order size.
na indicates a negative spread.
Apart from dealer three, we observe a limited decline in the spread with the order
size. However, for dealers four and six, for which model (5) applies, extremely large
trades will be executed at less favourable prices and transaction costs will be
increasing with size. Moreover, despite the common decline of transaction costs
with the order size, Table 7 also indicates a substantial difference in the cost of
transacting charged by the six GEMMs. Again, we notice that the dealers who
mostly trade with external customers tend to charge larger transaction costs.
6.2. Volume, 6olatility and trading costs
A final question which deserves our attention concerns the relationship between
the price volatility, the volume of trading and the cost of trading. According to
Admati and Pfleiderer (1988) and Foster and Viswanathan (1990) trading costs
should be small when volatility and volume are high. Their models predict that, in
the presence of information asymmetries, strategic liquidity traders concentrate
their activity when trading costs are low. In Table 8 we report some estimates of the
effective spread for different periods of the day. These values have been calculated
for the median of the distribution of the order size, q tr, using models (4) and (5)
as in Table 7.
Table 8 is suggestive that spreads in the gilt market are higher in the morning
than in the rest of the day. Notice that a similar pattern of the spread has been
found by Chan et al. for the NASDAQ, another decentralised market. Comparing
Table 8 with Fig. 2 of Section 2, we conclude that trading costs and trading volume
are not negatively correlated, as suggested by the theory. However the slight decline
of the spread during the day is in line with other empirical investigations (Hsieh and
Kleidon, 1992; Mc Inish and Wood, 1993 and Foster and Viswanathan, 1993) and
may indicate that as new information is processed over time its adverse selection
effect reduces.
7. Conclusion
This paper has provided an empirical analysis of the UK gilt market based on the
study of the activity of several market makers during October to November 1994.
Our analysis suggests the following results.
P. Vitale / Int. Fin. Markets, Inst. and Money 8 (1998) 299–324
321
1. The UK gilt market is very large and deep. Furthermore, trading activity is
concentrated early in the morning and after lunch. Moreover, GEMMs seem to
specialize as some deal mostly with other dealers, while others make the market
for external customers. Clients tend to submit smaller market orders and the
number of their buy orders prevails over that of their sell orders.
2. Turning to the analysis of the price process, we have considered a structural
model which captures transaction and inventory carrying costs, information
asymmetries and takes account of the decentralisation of the UK gilt market.
The estimation of this model suggests that imbalances in inventories do not
condition the transaction price set by market-makers. This result is consistent
with the conclusions of other investigations and can be explained by the
availability of several hedging instruments.
3. Market microstructure theory claims that trading costs may increase with order
size when market orders carry an information content. While most empirical
analysis of equity markets show that customer orders are informative, Proudman (1995) finds that this is not the case for gilts. Our regressions of the
structural model are consistent with Proudman’s finding as long as we do not
consider non-linear terms.
4. Non-linearities in the relationship between transaction price and order size may
emerge for block trades. When we consider them, we find that non-linear terms
in the structural model of the price process are significant and signal that block
trades have a positive impact on the transaction price for those market makers
who are mostly involved in transactions with external customers. This may be
interpreted as indicating that only large customer trades are informative.
5. One of our more interesting results concerns the effect of inter-dealer trading on
the price process of individual market-makers. As suggested by Lyons (1995), in
decentralised markets transactions amongst dealers may signal information
market-makers gather from customer orders and other private sources. We
confirm this thesis, since we find that the signed total quantity of inter-dealer
trading has a significant and positive impact on the transaction price of
individual market-makers. This also suggests that access to IDBs open to all
traders would increase the efficiency of the market.
6. Estimates of the effective spread can be obtained from the regressions of the
structural model. They show that the cost of trading is very small in the bond
market. However, substantial differences in the cost of liquidity charged by
dealers are observed. In particular, GEMMs who deal mostly with non-members of the LSE charge larger transaction costs.
7. Finally, trading costs in the gilt market are larger in the morning than in the rest
of the day, corresponding to periods of higher trading volume. This might
indicate that adverse selection problems are less severe in the later hours of the
day.
8. To complete our comments on the results of this paper, we should emphasize
that they cannot be taken as conclusive and a more extensive analysis covering
more gilts and a longer period should be considered. This has not been possible,
P. Vitale / Int. Fin. Markets, Inst. and Money 8 (1998) 299–324
322
given the limits of the database we had access to, but it provides a valuable
direction for further research. This research should also extend our analysis in
other directions. A long list of micro-structural phenomena which deserve
consideration could be indicated here. Nevertheless, given the relative lack of
work on the microstructure of bond markets, we believe our work represents a
solid step forward.
Acknowledgements
I wish to thank Francis Breedon, who introduced me to the study of the UK
Government Securities market, Ian Twinn, for his assistance and comments, the
London Stock Exchange, which provided the data and useful information. I
received valuable comments by an anonymous referee and the Editor. I am
responsible for all remaining errors.
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