Single Stock Circuit Breakers – Issues in
Fragmented Markets1
Peter Gomber* Martin Haferkorn* Marco Lutat* Kai Zimmermann*
Abstract
Since the May 6th, 2010 flash crash in the U.S., appropriate measures ensuring safe, fair and
reliable markets become more relevant from the perspective of investors and regulators.
Circuit breakers in various forms are already implemented for individual markets to ensure
price continuity and prevent potential market failure and crash scenarios. However,
coordinated inter-market safeguards have hardly been adopted, but are essential in a
fragmented environment to prevent situations, where main markets halt trading but stock
prices continue to decline as traders eventually migrate to alternative markets. The objective
of this paper is to provide insight into recent circuit breaker implementations, their individual
specifications and potential coordination between venues. Further, we empirically study the
impact of circuit breakers in a single market and a fragmented market context. We find a
decline in market volatility after the circuit breaker, but at the cost of higher implicit trading
costs. Moreover, by analyzing trading at the satellite market during the home markets CB, we
find market quality and price discovery to be sorely afflicted as traders systematically retreat
from trading. Only with the home market reentering trading, the satellite market restores pre
circuit breaker market conditions.
Keywords: Circuit Breaker, Electronic Trading, Exchanges, Market Coordination, Market
Fragmentation, Market Quality
JEL-Classification: G10, G15, G18
1
This is an extended version of the paper “The Effect of Single-Stock Circuit Breakers on the Quality of
Fragmented Markets” which will be presented at FinanceCom 2012, Barcelona, Spain.
* Goethe-University Frankfurt / E-Finance Lab, Grueneburgplatz 1, 60323 Frankfurt, Germany. Phone: +49(0)69 798-34683, Email: {gomber | haferkorn | lutat | kzimmermann@wiwi.uni-frankfurt.de}
1. Introduction
Fragmentation of investors’ order flow has been a long-time phenomenon in U.S. equity
markets. Competition in European equity markets started later in 2007 after the introduction
of the Markets in Financial Instruments Directive (MiFID) that enabled new venues, called
multilateral trading facilities (MTF), to compete with established exchanges, i.e. Regulated
Markets (RM). Stocks can therefore be traded at multiple venues requiring a high level of
inter-market price coordination in order to ensure holistic price integrity. Additionally, new
technologies such as high frequency trading and smart order routing emerged on both sides of
the Atlantic taking a significant share of the total trading volume and increasing inter-market
connectivity. Against the background of these developments and the May 6th 2010 flash crash
in the U.S. imposed possible drawbacks of these highly fragmented and connected market
systems, where single venue price shocks could encroach and inflict the entire market system
within minutes. Therefore, appropriate measures to ensure safe, fair and reliable markets are
becoming more relevant in the eyes of market participants and regulators. Circuit breakers
(CBs) in various forms are already implemented to ensure price continuity and prevent
potential market failure and crash scenarios. However, coordinated measures between
different market centers were neglected in the regulatory discussion and seem to become
more relevant in the light of these developments.
While academic research on single market CBs is quite extensive, literature on the effects in
fragmented market systems is still scarce, but would provide relevant input to market
participants, exchange operators and regulators. In this paper, based on the current state of
research, we will systematically investigate CBs and their implementations for major
European markets and classify those preventive measures in a systematic framework
regarding their various parameters and specifications. We find that European RM already
implemented various, sophisticated CB systems, whereas European MTFs do not implement
CBs so far. This may induce scenarios where a single venue interrupts trading due to order
imbalances but at the same time trading at alternative venues further proceeds, thus allowing
volatility to cascade onto alternative markets. On this basis, we empirically investigate CBs
and their effects on volatility, at first, in a single market case. We analyze the effects of home
market CBs onto the market’s post-CB market condition. This allows us to determine how
and if current CBs help to improve market. Secondly, we will focus on inter-market
coordination mechanisms during and after the CB. In particular, we concentrate on possible
shifts in trading behavior and market condition on the satellite market during the home
market’s CB. This might pose a systemic risk to the European trading landscape, i.e. if
volatility shocks on one market would allow price cascades to continue in satellite trading
venues and trading migrates between markets as posed by theoretical literature. We find no
evidence for trading migration, instead traders retreat from trading at the satellite market if
the home market halts.
2
Therefore, we provide a deeper understanding of how trading conditions change at the
satellite market. We show that market conditions in periods when the home market is on halt
are sorely affected and only recover with the home market’s reentering. The same does apply
for the satellite markets ability to determine an efficient price within this period. Although
not utterly misguided, prices at the satellite market orientate at the post-CB price level of the
home market, in the second trading is resumed.
The remainder of this paper is structured as follows. In the next section we will provide a
definition of CBs in order to distinguish between the various forms of trading interruptions
and ensure a consistent understanding. Following, we review academic findings related to our
work while indicating the need for more empirical research especially on inter-market CBs
within the European market system. In section 3, we develop a systematic framework to
categorize CB implementations in major European trading venues and discuss possible
consequences from inconsistent implementations throughout the trading venues and the lack
of a mandatory coordination mechanism. Section 4 describes our dataset and some
descriptive statistics for our single and inter-market analysis of CBs while the next section
presents our research approach and findings. Finally, conclusions are given in section 6.
2. Definition and related literature
Since the term “CB” is widely used in different scientific disciplines in this paper we will
solely focus on CBs in the domain of financial markets. Even under this scope there is no
consistent understanding among academic and practitioners’ literature. However, there is a
general agreement, that CBs are a form of a trading halt (Harris, 1998), (Kim & Yang, 2004)
(Engelen, 2006). In general, trading halts can be classified into the following three categories:
regulatory halts, technical halts and market-based halt (see Figure 1). Regulatory halts can be
further categorized by the type of investigation which is mainly in the field of insider trading
and market manipulation, accuracy and availability of public information (SEC, 2011). While
the corresponding supervisory authority is in charge to call out regulatory halts, technical
halts are mostly caused by outages of the market operator’s IT-infrastructure. The third
category which is mainly addressed in this paper contains market-based halts. Pre-trade CBs
are the result of order imbalances in the auction call phase, where trading halts result in an
extension of the auction phase. The equivalents during the trading phase are the continuous
CBs, which are triggered by manifold parameters and lead either a switch to auction call
phase or a complete trading halt.
3
FIGURE 1: CHARACTERIZATION OF TRADING HALTS
Therefore, we define CBs as:
CBs are market-based trading halts which are triggered by a (potential) significant price
disruption and are supposed to ensure price continuity.
Some authors use the synonym “price limit”, which is adequate to our definition of CBs. The
main goal to be accomplished by trading halts is to protect investors and assure market
fairness and market integrity. Following Kim and Rhee (1997), CBs can provide traders with
time in which they can obtain new information, reassess the market price, and avoid or
correct overreaction. Therefore, the uncertainty and irrationality in the market will be less
severe and thus the price volatility after a CB should be less than that before. In contrast, if
CBs only delay the price discovery process and interfere with trading, the price volatility
afterwards may be higher than that before and CBs only disturb the information transmission
process.
Analyses of CBs in the single market case can be found widely in academic literature.
Greenwald and Stein (1991) show how CBs may help to overcome some of the informational
distortion problems caused by volume shocks in continuous trading and thereby to improve
the market's ability to absorb large volume shocks, i.e. abnormal large quantities of orders.
The authors propose a temporarily switch to an alternative transactional mechanism in order
to trade immediacy provided by continuous trading for information allocation in auctions.
Kodres and Brien (1994) show in a model-based approach that CBs lessen the order
implementation risk which happens in times of substantial volatility. Brennan (1986) exhibits
that CBs can be understood as a substitute for margin requirements as the down- and upside
risk is limited to the corridor of the CBs. In contrast to the beneficial effects described before
many theoretical works on CB suggest that market activity is only delayed (Coursey & Dyl,
1990) and CBs cause a magnet effect towards the threshold (Ackert, Chruch & Jayaraman,
2001).
Besides these ambiguous theoretical findings, most empirical studies conclude that CBs do
not help decrease volatility (Kim & Yang, 2004). Chen (1993) does not find any support of
the hypothesis that CBs help the market to calm down.
4
Kim and Rhee (1997) and likewise Bildik and Gülay (2006) observed a spillover effect of the
volatility to the near future after a trading halt was put in place. Hassan et al. (2000) found no
significant impact of CB’s on the volatility in the equity market of Bangladesh. Among few,
Kim and Yang (2008) proved that consecutive halts on the Taiwan Stock Exchange dampen
the volatility better than single and closing halts. Further a decreasing volatility could be
observed on Korea Stock Exchange via a portfolio-based approach (Lee & Kim, 1995).
While academic research on CBs in the case of a single market is quite extensive, research on
the coordination of CBs in fragmented markets is still scarce. Focusing on inter-venue
effects, Subrahmanyam (1994) analyzes the utility of CBs implemented in a two-market
perspective. The first market is modeled as a dominant market with a relatively high liquidity
and the second market attracts only a small volume during trading hours. The authors show
that - in the case of a trading halt at the dominant market - liquidity as well as price
variability will shift towards the satellite market. This leads to a negation of any beneficial
effects intended by the implementation of CBs on the primary market. Besides these results,
the authors acknowledge that if a CB is triggered on a coordinated basis across venues, price
variability decreases at the cost of declining liquidity. Morris (1990) concludes in an
argumentative approach that uncoordinated CBs will more harm the market than help due to
higher volatility and a rising demand in liquidity on the non-halting markets. He suggests that
a better coordination across venues is strongly needed to ensure the effectiveness of such
mechanisms.
The first empirical study concerning the two market case is provided by Fabozzi and Ma
(1988). In their research paper, they address market activities during NYSE halts at the
NASDAQ, which is regarded as an OTC market. They find evidence that even if trading
volume declines, volatility spikes significantly during these times. Due to the nature of
volatility, which gives traders no arbitraging opportunity to capitalize on this situation, they
conclude that a halt should not be mandatory for both trading locations. The most recent and
comprehensive research on this matter was presented by Chakrabarty et al. (2011) which is
focused on delayed openings in the U.S. Their dataset consists of 2,461 halts in 1,055 stocks
at the NYSE between 2002 and 2005. During these halts, trading at other venues was
allowed. They find an increase of off-NYSE trading within the observation period and a
significant contribution by off-NYSE trades to the price discovery process. Further they
suggest that off-NYSE trades dampen the abnormal post halt volatility and spreads. This
leads them to the conclusion that continued trading may be beneficial to the market even at
higher spreads.
Research on CBs in two or more market case is scarce and empirical work only addresses the
U.S. market system. This is especially unfavorable as empirical findings can be hardly
compared to other markets due to the specialties of the U.S. trading and post-trading system.
5
Noteworthy in the context is the Regulation NMS and its trade-trough-rule (also known as
order protection rule). This rule prohibits market places to execute trades and forces them to
pass through or cancel the order if the price is below/over the National Best Bid and Best
Offer (NBBO). The implementation of the MiFID in Europe initiated a fragmentation process
which makes the research in this topic necessary as both market systems significantly differ.
Most notable is a less strict best execution requirement, no NBBO and no order protection
rule in Europe. Due to the lack of the European market system analysis, we are –to the best
knowledge of the authors- among the first who provide empirical findings for the European
trading landscape within the single market and inter-market case. The next section will
encompass the individuality of the current European trading landscape with regard to the CB
implementation in place.
3. Framework and implementations of CBs in Europe
In the following we present a systematic framework on CBs along several dimensions in
order to highlight the idiosyncrasy of various implementations. While the actual
implementations of CBs differ among trading venues they can in general be classified along
the categories threshold/ reference prices, halt / auction and duration. These will be explained
in the following subsection first. Beginning with subsection 3.2 we will then present concrete
CB implementations in Europe and apply our categorization. Finally, a table summarizing our
findings will be given.
3.1.
Categorization of CBs
Threshold/ Reference Prices: CBs are triggered by an instrument’s extreme price movement
based on a pre-defined reference price. In order to distinguish between extreme price
movements and normal intraday price volatility, the simplest way is to determine corridors
limiting the maximal licit price deviation. In general, a volatility band is established, allowing
trades to be matched within the pre-determined price corridor. This mechanism could be
applied in continuous trading as well as slightly modified in auction trading periods. Most CB
mechanisms in our analysis are based on these symmetric, relative volatility bands differing
only in their reference price specifications and widths. Actually, besides the exchanges
analyzed in our sample, there are some exchanges which apply asymmetric volatility bands
around the reference price. Considering e.g. the extreme price fluctuations of the Volkswagen
share in 2008, where prices increased up to 1,000 € a share due to a option-driven
exaggerated demand, such one-sided implementations could fail their purpose to ensure
orderly trading (Financial Times Deutschland, 2008).
6
The bands width is determined by the historical intraday volatility for each instrument or
index, each liquidity class or asset class. Current implementations in continuous trading focus
on two different reference price setups. Static Reference Thresholds refer to long-term price
bands active between auctions or whole trading days. In this case the reference price is
mostly determined by the last auction price or the last trading day’s closing price. By
choosing the last auction price, the reference point of the volatility band is updated after each
auction and therefore tolerates greater leeways than a day-constant threshold. A CB is
triggered if a trade’s price lies outside the determined static price band. A Dynamic
Reference Threshold creates a short-term price corridor on a trade-per-trade basis. In general,
dynamic price bands are much narrower than static ones, which are up to 20-25% from the
underlying reference price.2 The actual implementations are based on either one of these
concepts, or use a combination of both.
Halt/Auction and Duration: If a CB is triggered, the trading system faces an interruption.
The kind of interruption can either be (i) a trading halt, (ii) a system change into auction
phase if the CBs occurred in continuous trading. Both phases stop continuous order matching
for a predefined interval in order to give market participants a change to reevaluate the
current information. After the interruption plus a possible individual extension, continuous
trading continues.
The implementations focusing on auction trading phases follow the same mechanics. Since
there are no trades during an auction call phases, there is no dynamic update of the previous
defined reference price so dynamic as well as static reference price remain determined by the
last trade during continuous trading and the last auction price/closing price respectively. In
case the indicative auction match price exceeds the given leeways, most implementations
extent the call phase to a randomized time span. Finally, if the respective indicative auction
price is inside the price corridor, continuous trading restarts. In the following section, the
highlighted characteristics will serve as determinants to categorize nowadays CB
implementations.
3.2.
EU – current situation
Starting in November 2007, MiFID triggered a major process of fragmentation in the
European trading landscape. The abolishment of national concentration rule intensified
competition for existing national primary markets. Thus, in contrast to previous concentration
or default rules, member states are not able to privilege RM against other trading venues any
longer. Now RM compete with MTFs for order flow as the number of trading venues
increases significantly.
2
See Table 1.
7
While transparency and trading requirements do not significantly differ for both types of
trading venues, a closer look at the implemented CBs reveals systematic differences. The
European regulatory bodies so far did not address standards for the above mentioned venues
to implement CBs as well as a possible coordination. MiFID did not set up uniform
guidelines on a market’s requirement for CBs nor a holistic approach to coordinate panEuropean trading suspension triggered by extreme price movements. Neither RM nor MTF
are forced to implement CB or to coordinate actions in volatile markets as of today. Alone
MiFID article 50 concedes the right to suspend trading on an instrument base - however only
in the national jurisdiction of the respective regulatory agency. On October 20th, 2011 the
European Commission published proposals on the modification of the MiFID framework
(called MiFID Review or MiFID II) and addresses the implementation of single venue CB.
The proposed new article 51 requires member states to assure regulated markets to have in
place effective systems “to reject orders that exceed pre-determined volume and price
thresholds or are clearly erroneous and to be able to temporarily halt trading if there is a
significant price movement in a financial instrument on that market or a related market
during a short period and, in exceptional cases, to be able to cancel, vary or correct any
transaction“ (European Commission, 2011). However, the current proposal does not address
market-wide coordination mechanisms and therefore neglects possible trading migration
scenarios. So far, European RM and MTF provided individual in-house CB based on
individual price thresholds and instruments. These solutions differ in the mechanism as well
as in the process following the interruption. Only a few exchanges publish exact thresholds,
others avoid publishing in order to prevent a possible deliberate triggering of the CB by
market participants. The mechanisms of the CBs itself are also published based on a different
level of detail (e.g. Deutsche Boerse publishes a detailed guide on the functionality of their
volatility interruption) while information by other exchanges remains limited.
The following section will summarize the different solutions currently in place in major
European exchanges. We show, while RM, due to their longer operating history, established
up-to-date solutions, where similar approaches are actually discussed in the U.S. National
Market System. On the other hand, MTFs lack individual implementations to cut extreme
volatility movements so far. We find that major European MTFs implemented systems to
prevent the insertion of trades where the price difference with the previous trade is too high.
But there is no system to prevent a short-term price disruption. Considering the regulatory
lack of coordination mechanisms between the trading venues, we find that nearly all MTFs
declare individual actions regarding a possible trading interruption on an instruments’
primary market.
8
3.3.
European regulated markets
Table 1 shows actual implementations at the major European RM. The sample represents the
major European RM considering aggregated equity turnovers in 2011 and additionally
smaller ones to compare for difference (Thomson Reuters, 2011). Although currently not
regulatory required, all analyzed primary markets have implemented mechanisms to prevent
and exit sudden price movements. Based on the presented mechanisms nearly all venues
differ in the explicit design but use the above acknowledged mechanic. All venues use a
combination of static and dynamic price bands referencing to the last action price (last trade
price) for the static (dynamic) threshold. The Swiss SIX exchange uses a slight modification
in the static threshold: The “Avalanche Stop Trading” mechanism stretches the dynamic price
threshold to a retention period of 10 seconds. Within this time period, not only the next trade,
but also the execution prices of all trades within these 10 seconds have to meet this threshold.
Fundamentally, this mechanism shares the same characteristic of a static threshold but with
thresholds updated more frequently. Static threshold levels are often linked to the respective
liquidity or sector class of each instrument, i.e. exhibit thinner bands for more liquid stocks.
Also for dynamic thresholds, price bands breadth decreases with the stock’s liquidity.
Whenever a CB is triggered, the current market phase changes into a call auction or halt
phase in order to interrupt market hastiness and allow market participants to reevaluate the
current situation and information. A triggered call auction is designed heterogeneously across
exchanges. Market participants are allowed to modify, delete or insert new orders. If the
market model requires additional liquidity through market makers or designated sponsors, the
exchange maintain heterogeneous approaches if they have to quote during the CB. While e.g.
Deutsche Boerse’s Xetra system beholds the relevant designated sponsor to maintain minimal
one quote during the time of the call auction (Deutsche Boerse, 2010), the London Stock
Exchange maintains no obligations for market makers to quote during CB (LSE, 2011b).
3.4.
European multilateral trading facilities
Analyzing the three major European MTFs in respect of equity turnover in 2011, Chi-X,
BATS and Turquoise, we find no CB currently in place. Based on the venue’s rulebooks, all
MTFs implemented systems to hinder orders that would result in a trade with extreme price
deviation compared to the previous trade, to be routed to the order book (pre-emptive
mechanisms). While these implementations do prevent sudden price movements caused by
erroneous or abnormal order limits, they do not terminate a price decline loop triggered by a
sequence of trades. A homogeneous coordination mechanism, that stops trading on MTFs in
case of a primary markets CB would prevent trading migration across venues and would
literally break any downstream price circuits. We find no homogeneous coordination
mechanisms between the RMs and the MTFs. Based on the venue’s rulebooks we find each
MTF states individual procedures regarding a CB at the primary market.
9
In fact, only one MTF in our analysis does provide clear guidance on what happens if an
instrument’s primary market halts in case of a CB, while the other MTFs remain opaque
about their respective actions. While we find no respective paragraph in the BATS Europe
rulebook3, Chi-X Europe’s Rulebook acknowledges: “Chi-X will not normally suspend
trading in any security which is subject to any non-regulatory suspensions, such as a
volatility halt.” (Chi-X, 2011). This statement indicates that Chi-X Europe will suspend
trading only due to Chi-X’s own assessment. These findings allow for further investigation
on trade intensity and quality on Chi-X during a home market’s CB.
3
BATS Europe states trading suspension in case of a relevant listing markets suspension of a security as well as
for the maintenance of a fair and orderly market (BATS, 2011).
10
TABLE 1: DETAILED CB IMPLEMENTATIONS OF MAJOR EUROPEAN EXCHANGES
Static Price Threshold
London Stock
Exchange
Reference price
Dynamic Price Threshold
Swiss SIX
Exchange
NASDAQ
OMX
Irish Stock
Exchange
Duration
extension
Duration
Last auction price
Ranges between 10% and
25% due to stock sector
level
Last traded
price
Call Auction
Not published
Yes randomized
Not published
Last auction price
Not published
Last traded
price
Call Auction
Not published
Yes randomized
Not published
Last auction price
Not published
Last traded
price
Call Auction
2 min
Yes randomized
±1,5% - Blue Chips
±2%(1.5%) - Mid/Small
Caps
Avalanche Stop
Trading – 10 sec. last
traded price
±1,5% - Blue Chips
±2%(1.5%) - Mid/Small
Caps
Last traded
price
Call Auction
5 Min. - Blue Chips;
15Min - Mid/Small
Caps
No
± 10% Index Shares
±15% Others
Last auction price
± 3% Index Shares
±5% Others
Last traded
price
Call Auction
60 sec- dynamic
180sec – static
No
Not published
Last auction price
Not published
Last traded
price
Call Auction
5 min
Yes -Random
30 sec
shares making up the
FTSE MIB index ±5%;
other shares ±10%;
Last auction price
shares making up the
FTSE/MIB index ± 3.5%;
other shares ± 5%;
Last traded
price
Call Auction
Not published
Yes randomized
dependent on the Band and
Liquidity Class
Last auction price
dependent on the Band and
Liquidity Class
Last traded
price
Call Auction
Not published
Yes randomized
BME
Borsa Italiana
Halt/Auction
Ranges between 5% and
25% due to stock sector
level
Euronext
Deutsche
Boerse
Reference
price
Sources: (Borsa Italiana, 2011), (Euronext, 2011), (SIX Swiss Exchange, 2010), (LSE, 2011a), (Deutsche Boerse, 2011), (NASDAQ OMX Nordic, 2011a), (ISE, 2011),
(BME, 2011).
11
4. Data setup
In order to analyze not only the efficiency of the current European implementations but also
possible inter-market effects, we focus on European instruments tradable in multiple venues,
i.e. RM and MTF. Based on the German blue chips DAX 30 index, we analyze CBs in the
year 2009 in Deutsche Boerse’s electronic order book Xetra (home market). As satellite
trading venue, we choose the most relevant MTF for German blue chips in respect of trading
volume - the London based MTF Chi-X. The 2009 scenario provided relatively calm market
conditions, after the 2008 financial crisis which was accompanied by tremendous market
turmoil. Due to a volatility flag in the dataset, we are able to identify all CBs during
continuous trading over the given time period. The sample consists of the millisecond-precise
start and end point of each interruption on Xetra with a total sample size of 522 single stock
CBs. Modifications within the DAX 30 index composition in 2009 and Volkswagen’s
volatile price movement caused by the merger attempt by Porsche made it necessary to
exclude three instruments (58 CBs). This leads to a total sample of 464 CBs in 2009
distributed over 27 stocks. Table 2 provides the respective summary statistics for our sample.
TABLE 2: SUMMARY STATISTICS OF THE SAMPLE
Number of Circuit Breakers
464
Number of Instruments
27
Mean (Median) Number of CB per Instrument
17.19
Maximum CBs per Instrument
73
Minimum CBs per Instrument
4
Mean (Median) Duration in Minutes
02:16
(13.00)
(02:16)
Figure 2 shows the sample distribution plotted by month of appearance. In the same Figure,
illustrated as line, we plotted the German VDAX volatility-index. The calculation of this
index is based on notional options on the DAX 30 index as underlying asset, indicating the
price fluctuations within the DAX 30 instruments.
12
90
45
80
40
70
35
60
30
50
25
40
20
30
15
20
10
10
5
0
0
2009/012009/022009/032009/042009/052009/062009/072009/082009/092009/102009/112009/12
FIGURE 2: CBS BY MONTH OF APPEARANCE;
LEFT SIDE (PILLARS): NUMBER OF CBS; RIGHT SIDE (LINE): VDAX (MONTHLY)
The VDAX rises with increasing price fluctuation inside the DAX 30 instruments, and drops
as prices remain stable. We find high linear dependency between DAX volatility and CB
appearance. Over the trading day (see Figure 3), we approve a high correlation with intraday
trading activity and volatility pattern analyzed by Harris (1986) as well as Foster and
Viswanathan (1990).
60
50
40
30
20
10
0
FIGURE 3: APPEARANCE OF CBS BY TIME OF DAY
13
We retrieved reference data from the Thomson Reuters Tick History covering all Chi-X DAX
30 trades in the respective time horizon of each CB. Since the CB data includes the start and
end point of each interruption, time and date mapping will reveal trading activity on Chi-X
during the home market’s halt due to a CB. Based on our data, in 16% of all CBs we observe
no trades on Chi-X, i.e. in 84% of all cases at least one trade occurred. As stated above, ChiX will generally not halt their electronic order book in case of a reference market’s CB.
Nevertheless, Chi-X provides no information whether trading was halted during the home
markets interruption (Chi-X, 2011). In order to get consistent analysis and due to the
competitive structure of the European trading market, we assume that Chi-X will continue
trading whenever the trading halt is not triggered by a regulatory intervention. This
assumption is strengthened by the results in Table 3 which depicts aggregated stock by stock
statistics, displaying the total number of interruptions, mean trades per interruption and
median trades across all interruptions.
TABLE 3: DESCRIPTIVE STATISTICS OF THE CB SAMPLE
Instr.
#
Mean
Median
Instr.
#
Mean
Median
ADS
ALV
BASF
BAY
BEI
BMW
CBK
DAI
DB1
DBK
DPW
DTE
EON
FME
14
25
9
6
7
13
73
36
22
42
7
4
9
9
5.1
6.2
9.9
18.2
0.9
8.2
5.7
14.5
2.1
12.1
18.6
8.3
16.7
16.3
2.5
5
8
15.5
1
7
3
11.5
1
7
13
6.5
8
2
HEI
IFX
LHA
LIN
MAN
MEO
MRC
MUV
RWE
SAP
SDF
SIEG
TKA
18
43
15
7
20
7
4
14
4
3
27
6
19
0.7
7.2
9.3
3
5.4
5
11.5
2.4
20.3
2.7
4
23.4
6.4
0
4
6
2
4.5
4
4.5
1
9
1
2
21.5
2
Descriptive statistics of the CB sample. From the left: Instrument identifier, number of CBs, average and median number of
trades during the CB on Chi-X.
14
5. Results
In the following subsections we present our findings for the home and eventually for the
interaction between the home and the satellite market.
5.1.
Home market
A trade price reaching either the last price’s dynamic threshold or the last auction price’s
static threshold will trigger a CB and the trading phase will switch to auction mode. Thereby,
this 2 minute interruption is meant to calm down unreasonable price movement caused by
information overload. Previous ambiguous empirical findings question the efficiency of CB
and indicate only a delaying effect. Therefore, we determine if CBs are actually capable of
calming down above-average trading activity by analyzing market quality parameters before
and after a CB at the home market. To do so, we look at price volatility around the CB as
proposed by Kim and Yang (2008). If CBs provide traders with enough time reassess market
information and dampen overreaction, the degree of price volatility after a CB should be
significantly lower than that in a comparable time period before the CB. We measure price
volatility in two ways. High variability in asset prices indicates large uncertainty about the
value of the underlying asset, thus alienating an investor’s valuation and potentially resulting
in incorrect investment decisions when price variability is high (Harris 2003). We obtain the
standard deviation of trade prices to account for the average prices’ fluctuation around its
mean. On the other hand we use a high to low ratio in order to take account for the prices’
maximum deviation. Since both values outline different characteristics of volatility, both are
included in the analysis.
Further we calculate the relative spread, i.e. the differences between the best bid and best ask
quote relative to the midpoint, measuring the risk premium market participants require for
being exposed to market risk while submitting orders to the order book. Harris (2003) relates
relative spread with overall market liquidity and effectiveness, therefore we also take into
account the relative spread before and after the CB. All measures are calculated before a CB
was triggered and after continuous trading restarted upon different intervals to test the CB
effect’s duration. We choose a short term 2 minutes interval, a medium term 5 minutes
interval and a long term 10 minutes interval. These intervals where chosen, because the
average CB in our sample interrupts trading for 02:16 minutes, we find it unreasonable to
check for market quality changes in a future distance, since these changes could not be
directly related to the CB. Pre- and post-CB market quality parameters are compared via
Wilcoxon sign rank test for equality of the pre- and post-CB sample (Wilcoxon, 1945). Table
4 depicts results for each measure.
15
TABLE 4: MARKET QUALITY PARAMETER BEFORE AND AFTER THE CB ON XETRA
10min standard deviation
5min standard deviation
2min standard deviation
10min high low ratio
5min high low ratio
2min high low ratio
10min relative spread (in bps)
5min relative spread (in bps)
2min relative spread (in bps)
Before
0.124
(0.08)
0.093
(0.059)
0.064
(0.035)
0.020
(0.014)
0.015
(0.010)
0.010
(0.007)
18.64
(11.25)
18.82
(11.36)
19.33
(11.77)
After
0.092
(0.056)
0.074
(0.050)
0.058
(0.035)
0.017
(0.010)
0.013
(0.008)
0.010
(0.006)
21.27
(12.05)
20.68
(12.10)
22.06
(12.65)
Z-Value
8.794***
6.225***
2.811***
6.667***
4.861***
1.362
-4.592***
-5.130***
-4.730***
Mean (median) market quality parameters before and after the CB on Xetra for all DAX 30 instruments. Z-Values for Wilcoxon
sign rank testing for the null that samples are drawn from the same population. Significance levels are 1%(***), 5%(**) and
10%(*).
The results illustrate a highly significant drop in the market volatility parameters after the
CB. Except for the 2min high low ratio, we find all standard deviations as well as high low
ratios on a lower level within the 2 minutes, the 5 minutes and the 10 minutes interval,
indicating significantly calmer trading conditions after the CB in the short- as well as in the
long-run. As indicated by the significant higher relative spread, the volatility reduction comes
by the cost of a higher implicit risk premium for market participants after the CB, i.e. a higher
spread level. We find the relative spread significantly increased in all analyzed intervals
revealing an adoption of the overall market premium after the CB. First intuition for this
market change one would suspect a decline in trading activity to be the driver of this shift.
French and Roll (1986), Harris (1986), Karpoff (1987), Schwert (1989), and Stoll and
Whaley (1990), among others, document a positive relation between volatility and trading
volume. Therefore we look at the respective number of trades in each measurement period.
By comparing the pre and post-CB trading activity for each instrument this intuition is
rejected, since the numbers of trades do systematically rise after the CB. Calmer market
volatility is achieved although trading continues at higher activity level. Table 5 denotes
median trading activity by stock before and after the CB. Negative Z-Values obtained from a
Wilcoxon sign rank test at the bottom of the table indicate a systematical increase in the
number of trades after the CB. This is in contradiction to the findings of recent studies.
16
Since market conditions are calmer without a decrease in trading activity, we conclude that
CBs succeed to calm market hastiness by lowering market volatility after the CB.
TABLE 5: TRADING ACTIVITY BEFORE AND AFTER THE CB ON XETRA
Instr.
ADS
ALV
BASF
BAY
BEI
BMW
CBK
DAI
DB1
DBK
DPW
DTE
EON
FME
HEI
IFX
LHA
LIN
MAN
MEO
MRC
MUV
RWE
SAP
SDF
SIEG
TKA
2 minute median
Before
After
27
25
97
110
66
90
59
85
87
158
121
123
72
102
114
147
60
60.5
193
229
26
55
154
192.5
54
111
27
46
19.5
22
42
86
36
52
22
28
49.5
69.5
28.5
33.5
113.5
35
47.5
83
37
83.5
52.5
62
66
88
68.5
101.5
54
49
5 minute median
Before
After
82
56.5
218
197
153
182
136
167
200
306
176
169
143
231
248
273
107
123.5
392
463
37
121
314.5
330
213
204
76
84
40.5
29.5
71
126
81
115
69
49
94
130
51.5
57.5
129
227
97.5
153.5
73
171
74
115
152
177
164.5
212.5
102
83
10 minute median
Before
After
153.5
120.5
421
366
272
375
223
262
342
459
296
323
292
394
440.5
475.5
184.5
194
636
715
87
173
556.5
544
305
357
113
117
55.5
46
110
192
159
168
108
92
147
189.5
84
88.5
146
364.5
168.5
216.5
148.5
255.5
99.5
212
197
270
285.5
338.5
159
154
Z-Value
-7.616***
-6.789***
-6.170***
Median number of trades before and after the CB on Xetra for all DAX 30 instruments. Z-Values for the equality of pre- and postCB sample, significance levels are 1%(***), 5%(**) and 10%(*).
5.2.
Inter-market coordination
While our previous analysis focused on the venue specific impact of CBs on market quality,
the following subsections will highlight inter-market coordination of trading behavior and
respective changes in the market condition during and after the CB. First, we analyze trading
Chi-X during the CB. This issue is motivated by theoretical models about trading migration
during CBs. The results will essentially stipulate our analysis of market quality on Chi-X
during the CB.
17
In the second step, we investigate the satellite market’s ability to discover efficient prices in
the absence of the home market. A two step approach to analyze trade prices (i) during the
CB and (ii) when the home market restarts trading discredits the satellite market’s
contribution to price discovery.
5.2.1. Trading migration - abnormal number of trades during CBs
According to Subrahmanyam (1994), volatility shocks would allow price cascades to
continue on alternative trading venues and negate any positive CB effect on the home market
if traders migrated to satellite venues in order to continue trading. Since the European market
system does not force common halts in case of a CB triggered in a single venue, there is
potential for this scenario. In order to control for abnormal trading behavior on Chi-X, i.e. a
significant change in the number of trades, we calculated “normal” trading statistics within a
symmetrical 30 trading day interval of the event. For each of the 30 trading days we gathered
number of trades for each instrument within the corresponding CB time span.
TABLE 6: MEDIAN/MEAN COMPARISON OF THE INTERRUPTION AND REFERENCE SAMPLE
Instrument
ADS
ALV
BASF
BAY
BEI
BMW
CBK
DAI
DB1
DBK
DPW
DTE
EON
FME
HEI
IFX
LHA
LIN
MAN
MEO
#
14
25
9
6
7
13
73
36
22
42
7
4
9
9
18
43
15
7
20
7
Interruption
Average
Median
5.1
2.5
6.2
5
9.9
8
18.2
15.5
0.9
1
8.2
7
5.7
3
14.5
11.5
2.1
1
12.1
7
18.6
13
8.3
6.5
16.7
8
16.3
2
0.7
0
7.2
4
9.3
6
3
2
5.4
4.5
5
4
Reference +/-3d
Average
Median
12.83
8.0
16.61
11.0
18.05
16.0
17.50
16.0
2.85
1.8
14.97
10.0
11.61
7.5
27.93
20.8
7.30
5.0
32.04
21.5
11.60
7.5
23.50
23.5
29.30
20.5
9.17
4.0
1.31
0.0
4.57
2.0
8.42
4.5
10.31
8.0
12.51
10.5
6.56
3.8
Reference +/-15d
Average
Median
9.31
4.8
17.14
12.5
18.29
14.0
20.14
15.8
2.42
1.5
13.86
7.0
9.47
6.0
27.66
17.0
7.70
4.5
29.94
17.0
11.09
7.0
21.54
17.8
26.83
17.0
9.86
5.0
1.28
0.0
4.18
1.5
8.89
5.0
10.07
4.0
12.13
9.0
5.20
3.0
18
MRC
MUV
RWE
SAP
SDF
SIEG
TKA
4
14
4
3
27
6
19
11.5
2.4
20.3
2.7
4
23.4
6.4
4.5
1
9
1
2
21.5
2
7.96
15.38
16.83
7.08
8.44
29.22
12.41
5.5
11.0
13.8
6.5
5.5
23.0
9.0
6.13
16.01
21.21
9.78
6.91
29.76
11.59
2.8
10.0
17.8
6.8
4.5
25.0
7.5
From the left: Instrument identifier, number of volatility interruptions on Xetra, average/median number of trades within
the interruption; avg/median within a +/-3 day interval during the interruption period; average/median within a +/-15 day
interval during the interruption period.
This way we can determine whether the trading behavior during halts is “abnormal”. Table 6
presents instruments, number of CBs, average trades on Chi-X during CBs and average trades
within +/-3 and +/-15 trading day intervals. We chose a short and long term perspective, in
order to control for short term effects shortly after/before the CB and a long term “ordinary”
trading behavior. Considering the variability of the trading data within these extreme short
moments of the trading day, we also calculated respective medians for the same intervals. For
nearly every instrument, we observe a major shift in the trading behavior caused by the CB.
Our findings indicate a significant deviation in the number of trades for almost every stock on
the alternative market compared to “normal” trading behavior. Nearly all instruments
experience a major decline in their number of trades during the home markets halt
considering median and average. For only four instruments we observe an increase in the
number of trades during interruptions.
TABLE 7: TRADING MIGRATION REGRESSION
Coefficient
Clustered
Std. Err.
t-Value
R2
+/- 3 day median
0.608
0.148
-2.66***
0.3389
+/- 15 day median
0.691
0.166
-1.86**
0.3242
Trading migration regression to determine trading migration to Chi-X during the CB. The number on trades on Chi-X
during the CB are regressed on the 3 day (15day) median number of trades. T-Values result from a T-Test on the null
hypothesis of the coefficient being one. Significance levels are 1%(***), 5%(**) and 10%(*).
Due to the extreme variability within the reference samples, parametric t-testing is
impractical as hypothesis for normality within the data are rejected. To strengthen our
implication, we perform a regression analysis instead. Thereby, the number of trades on ChiX during the home market’s CB are regressed on the on the 3 day (15 day) median number of
trades of the reference period.
19
If there is actual, systematic trading migration toward the satellite market, we would expect
the coefficient to be statistically significant larger than one, i.e. the number of trades during
the CB lager than the median of the reference prices. On the other hand, a coefficient
significantly smaller one would indicate a retreat from trading. We perform ordinary least
square regression, suppressing the constant term and clustering for each instrument. Clustered
standard errors are obtained in order to test for the null, that the coefficient equals one. If the
null could not be refused, we conclude that there is no difference between the number of
trades during the CB and the reference period. In both cases, the null is rejected and thus our
assumption is strengthened that traders retreat from trading in the satellite market when the
home market halts continuous trading (Table 7).
The trader’s retreat from the satellite market strongly imposes the question whether market
quality during the absence of the home market remains at an anticipated level. Following
French and Roll (1986), the reduction in trading volume may lead to calmer market
condition, i.e. a reduced volatility. We address this question by analyzing the above presented
market quality parameters for the satellite market. We measure standard deviation, high low
ratio and the relative spread during the home markets interruption in order to provide insight
into trading quality in this period. In this case, we analyze CB trading in relation to the
previous and subsequent CB period. As the latter section illustrated, during the CB the
trading activity on Chi-X is massively reduced, so if we compare volatility on Chi-X during
the CB with pre- or post-CB intervals of the same length, the results will be seriously biased
as volatility will be significantly lower due to the reduced trading volume. Therefore, we
recalculate the volatility measures in a per-trade manner. This way, we compare values based
on a uniform number of trades and we do not suffer from a systematic bias. The before,
during and after CB samples are tested by non-parametric Wilcoxon test for equality of
medians, testing for the null that all samples are retrieved from the same population. Since
every sample is tested twice, alpha levels are corrected by Bonferroni’s approach. While a
given alpha value may be appropriate for each individual comparison, it is not for the set of
all comparisons. In order to avoid a lot of spurious positives, the alpha value needs to be
lowered to account for the number of comparisons being performed (Bonferroni, 1936).
Table 8 summarizes the results of the pre-and post-CB comparison as well as the between
comparison approach.
20
TABLE 8: MARKET QUALITY PARAMETER BEFORE, DURING AND AFTER THE CB ON CHI-X
standard deviation
high low ratio
relative spread (in bps)
Before
During
After
0.027
(0.016)
0.012
(0.002)
50.20
(13.19)
0.054
(0.017)
0.011
(0.003)
133.22
(50.83)
0.019
(0.011)
0.005
(0.002)
37.76
(14.99)
standard deviation
-3.353***
-6.099***
high low ratio
-3.949***
-4.740***
relative spread
-13.459***
-12.773***
H0: MQPpre CB = MQPduring CB vs.
H1: MQPpre CB ≠ MQPduring CB
H0: MQPpost CB = MQPduring CB vs.
H1: MQPpost CB ≠ MQPduring CB
Mean (median) market quality parameters before, during and after the CB on Chi-X for all DAX 30 instruments. Z-Values
and significance values for Wilcoxon sign rank test for the equality of the pre-CB and during-CB market quality parameters
(MQP), as well as the post-CB and during-CB sample. Benferroni modified significance levels are 0.5%(***), 2.5%(**) and
5%(*) for each test.
By looking at the time period during the home market’s CB, depicted in Table 8, we find a
high degree of uncertainty reflected in the prices, compared to the period before and after the
CB. Standard deviation as well as the high low ratio exceeds prior and past trading levels
significantly. We also find the relative spread to be significantly increased. Market
participants demand a higher level of compensation for bearing market risk than they do in
the periods that the home market is active. The relative spread during the CB is on average
three times as high as it is on average within the next ten minutes, and two times as high, as it
was on average ten minutes before. Acknowledging, that this comparison controls for
different trading volumes, our data provides evidence of a massive disturbance within the
satellite market’s trading quality during the home market halt. While afterwards, with the
home market proceeding continuous trading, market quality is reassuring to a level, lower
than the pre-CB period.
21
5.2.2. Price efficiency and coordination
In the previous subsection we provided empirical evidence that with the home market’s
eruption the satellite market suffers significantly in market quality. Furthermore, trading
activity is significantly reduced indicating that participants retreat from trading. Our
observations raise the question if the satellite market retains its ability to determine efficient
prices during the home market’s CB, or if the satellite market systematically fails in this
ability. Therefore we propose a model for price efficiency based on the approach from
Chakrabaty et al. (2011). They propose a two stage regression model in order to distinguish
the contribution of Off-NYSE trade prices during a NYSE CB to a reference in the future.
Although this approach is quite capable, it only indicates possible price over- or
undershooting. Instead, our objective is to reveal the price discovery during a CB relative to
the last price before the CB. This way we measure the capability of the satellite market to
transform information into prices without the help of the home market. The prices on Chi-X
during the CB are benchmarked with the last trade price in continuous trading in Chi-X
before the CB. We apply a regression approach, based on Chakrabaty et al. (2011), to
benchmark pre-CB and CB prices:
P,
P,
− P,
=α +β ∗
P,
P,
− P,
+ε
(1)
In this stage, the total return from the CB’s first trade price to the home market’s auction
price is regressed on the return from the last pre-halt trade to the home markets auction price,
where Pi, auction denotes the home market’s auction price, Pi,pre is the last price in continuous
trading on the alternative market and, at first, we take Pi,CB as the first trade price during the
CB on the alternative market for each CB i. We apply absolute return values as we are only
interested in the absolute deviation from the auction price, no matter if there is under- or
overshooting. For a second and third regression the first trade price in the alternative market
during the CB (Pi,CB) is replaced by the average trade price and the last trade price. The home
market’s auction price is used as a reference price in (1).
Our regression model’s outcome potentially addresses three scenarios: First, assuming preCB and prices during a CB contain exact the same information about the home market’s
auction price, the regression intercept will equal zero and the slope will equal one.
Consequently, the R2 will be close to one. In this case the CB trade prices contain no more
information than the pre-CB price.
22
A regression outcome with intercept zero and a slope significantly greater than one will
indicate that prices during a home markets CB lead on average to a lager diversion from the
auction price than the last price in continuous trading did and therefore this last price contains
further information than the following prices during the CB. In this scenario the trade prices
during the CB could be considered irrelevant. The third outcome, an intercept equal to zero
and a slope smaller than one, would indicate that on average the CB trade prices reveal more
information than the last trade in the alternative market. Thereby, the slope could be
interpreted as a lever for the excessive information added. Thus, in our regression model the
slope will measure the additional information from trading during the CB. Results become
more complex when the intercept deviates from zero and in this case our findings will differ
for positive and negative returns.
It is worth mentioning that we accept Pi,auction to be part of the endogenous and exogenous
variable. This may cause serious bias to the coefficients’ standard errors and make
significance testing obsolete. Likewise Chakrabaty et al. (2011) we do only use ordinary least
square methodology to estimate the slope and intercept in order to determine the relationship
between Pi,CB and Pi,pre by minimizing the squared error term.
TABLE 9: PRICE EFFICIENCY REGRESSION
P,
Intercept
Slope
R2
First Price on Chi-X during CB
0.001
0.825
0.62
Average price on Chi-X during CB
0.001
0.660
0.56
Last Price on Chi-X during CB
0.001
0.426
0.25
Price efficiency regression during home market’s CB. The Table lists coefficient estimates from the regression of price
efficiency based on (1). Return Pre-Halt contains the return from the last pre-halt trade to the home markets auction price (Pi,
auction - Pi, pre)/Pi, pre.
Table 9 presents the outcome of the three regressions. We find the slope to decrease from the
first to the last regression. Likewise Chakrabaty et al. (2011) our interpretation is that trade
prices during the CB gradually approach the home market’s auction price. Since the slope of
the first price of the CB is smaller than one, this indicates a systematical reduction in the
difference between this price and the home market’s auction price in comparison to the
difference between the last price on Chi-X in continuous trading and the home market’s
auction price. This difference gets even lower by regressing on the average price and the last
price on Chi-X, indicated by a decreasing slope. Thus, we conclude that trade prices in the
satellite market process at least some information leading to a narrowing of both venue
prices.
23
12 bps
Circuit Breaker
10 bps
8 bps
6 bps
4 bps
2 bps
0 bps
Mean
Median
FIGURE 4: MEAN AND MEDIAN DIFFERENCES WITHIN AN EIGHT SECOUND INTERVAL BEFORE AND
AFTER THE CB
Since this approach only captures the relative narrowing of the satellite market during the
CB, we proceed by measuring price coordination after the CB where both markets trade
continuously. We investigate which price (home market’s auction price or prices from the
satellite market) are more relevant for post-CB trading by comparing prices in both markets
before and after the CB. Since a trade-by-trade analysis is unreasonable due to timely unharmonized trade occurrences, we compute a venue’s average price-per-second and therefore
rely on comparison of both markets by second.
Figure 4 illustrates the average and median differences for up to six seconds before and after
the CB. Right after the CB, the difference in prices exceeds the pre-CB level but reverts over
time. Table 10 summarizes findings from a Wilcoxon sign rank test for these differences. Our
results indicate that at least four seconds are required after a CB until prices from both venues
have consolidated. While trade prices align during CBs, a second coordination interval after
the CB is apparently required for consolidating prices from both markets. This interesting fact
raises the question which price is the more relevant for post-CB trading?
24
TABLE 10: TRADE PRICE DIFFERENCES BETWEEN XETRA AND CHI-X BEFORE AND AFTER THE CB
Relative differences in bps
mean (median)
Z-Value
Pre-CB level
8.96 (1.20)
-
CB + 1 second period
11.80 (2.14)
-5.340***
CB + 2 second period
10.84 (1.69)
-3.798***
CB + 3 second period
9.84 (1.63)
-2.154**
CB + 4 second period
9.71 (1.34)
-1.335
CB + 5 second period
9.37 (1.33)
-1.611
CB + 6 second period
9.12 (1.26)
-1.657
Relative differences in bps between the home and satellite market after the CB. Z-Value contains statistics for a Wilcoxon
sign rank test with the null hypothesis that samples are obtained from the same population. Significance levels are
1%(***), 5%(**) and 10%(*).
We assume two possible scenarios for the post-CB price coordination. On the one hand, if
only one price, either the home market’s auction price or the satellite market’s last price
during the CB, provides superior information, we would expect the other venue’s price to
approach this price level as trading in both markets continues. On the other hand, if both
prices contain different and relevant information about future prices, we would expect both
venues’ prices to adjust to a new level. We apply a future reference price for each market and
regress both reference prices on the home market’s auction price as well as the satellite
market’s last price during the CB. If there is one price with superior information, we would
expect this price to be crucial in explaining both markets’ reference prices. Since the home
market’s auction price is highly correlated with the satellite market’s last price, we expect all
coefficients to equal one, so a simple test of differences might not indicate structural
differences. Instead, we systematically compare regression accuracy and model efficiency. If
one price provides more explanatory power or less deviation than the other, we expect this
model to be superior. Regression accuracy is measured by root-mean-square error (RMSE),
and a model’s explanatory power is measured by Akaike information criterion (AIC).
25
The regressions take the following form:
P
P
=β
=β
,
,
∗P +ε
∗P +ε
,
,
, ∈{
!"#$%, & "'((#"'}
(2)
Pj denotes either the home markets auction price or the satellite market’s last price during the
CB and PXetra and PChi-X denote a venue specific reference price, i.e the ten minute average
price after the CB. Again we cluster for instruments.
TABLE 11: POST CB PRICE DETERMINATION REGRESSION
Home market’s auction price (P
Satellite market’s last CB price (P&
!"#$%
)
"'((#"'
)
Satellite Market’s
Home Market’s price
determination
(P+'", )
price determination
(P.ℎ#−+ )
0.406 (432.99)
0.408 (436.23)
0.205 (-138.91)
0.203 (-146.43)
Xetra and Chi-X reference prices are regressed on the home markets auction price and the last trade price on Chi-X during
the CB. Table shows RMSE (AIC) for each regression.
By looking at the individual information criteria and the RMSE, we find the home market’s
auction price to be more relevant for both markets’ future prices (Table 11). Even the home
market’s auction price explains the future Chi-X price more accurately than the satellite
market’s last price during the CB. Next, we estimating the auction price’s and satellite price’s
contribution in one regression model. Multicollinearity may indeed inflate our standard
errors, but if the home market’s auction price systematically delivers additional explanatory
power next to the information both prices share, the auction price should indicate significance
in regression (3) while the last price during the CB should not.
P = β0, ∗ P
+β1, ∗ P2
33
+ ε ,# ∈ {+'", , .ℎ# − +}
(3)
Pi denotes either the home or satellite market’s 10 minutes average reference price, Pauction and
Psatellite are the home market’s auction price and the satellite market’s last price during the CB.
26
TABLE 12: COMBINED POST CB PRICE DETERMINATION REGRESSION
Home market’s auction price (P
Satellite market’s last CB price (P&
!"#$%
)
"'((#"'
)
Home Market’s price
determination
(P+'", )
Satellite Market’s
price determination
(P.ℎ#−+ )
0.087
0.080
0.913***
0.920***
Xetra and Chi-X reference prices are regressed on the home markets auction price and the last trade price on Chi-X during
the CB. Table shows coefficients and significance levels 1%(***), 5%(**) and 10%(*).
The results shown in Table 12 support our findings that the home market’s auction price
contains additional information in predicting both markets’ future prices and therefore turns
out to be significant. The satellite market’s price information are therefore redundant, i.e. not
significant. This effect is only possible, if the satellite market’s last price during the CB
experienced a systematic shift towards the home markets price level after the CB. Therefore,
we conclude that the home market’s auction price is the dominant after a home markets CB.
In summary, the significantly reduced trading activity during the CB compared to regular
trading activity within the reference days levels the spread market makers demand to
continue quoting during the CB. Interestingly, the reduced trading activity does not calm
down price volatility. We find standard deviation as well as maximum price uncertainty
dramatically increased. This also impacts price determination process at the satellite market.
The results indicate that the price revelation process on the satellite market is seriously
restricted during the CB. Although trading during the CB is not completely misguided as
shown by continuously approaching towards the home markets auction price, we see a
significant coordination step after the home market continues trading. Only with the restart of
the home market and the return of the traders, the satellite market regains the ability to
effectively reveal prices and approaches to the home market’s price level.
6. Conclusions
While the U.S. trading landscape has been fragmented since the late 1990s, the trading
industry in Europe only recently experienced a sea change from regulatory and technological
developments. Regulatory changes in the form of the Markets in Financial Instruments
Directive (MiFID) laid the foundation for more competition between execution venues in late
2007, while trading was traditionally consolidated in national exchanges before. Since then, a
multitude of Multilateral Trading Facilities (MTFs) emerged offering pan-European trading.
It is common to exchanges and satellite trading platforms to apply some form of CB to ensure
price continuity.
27
Despite the numerous implementations of CBs in European RM, implementations at MTFs
are completely missing as of 2012. Further, there is no mandatory coordination enforced by
the regulatory body to stop trading at MTFs if the home market triggers a CB. As shown in
some theoretical models, this situation could lead to price-falling-cascades, i.e. if traders
migrate from a market on halt to satellite venues which keep their systems open for investors’
order flow. Against this background, we analyzed trading of German blue chip stocks in two
markets, namely Xetra and the most relevant European MTF Chi-X. We especially
investigated those periods when the Xetra trading system was on a market based halt and
Chi-X sustained its trading services.
Asking for the CBs efficiency within the single market setup, we find evidence that price
volatility declines significantly at the home market after a CB, while trading activity remains
at a high level. In contrast to this, relative spreads do not revert to their pre-CB levels,
surprisingly they even increase after a trading halt. Therefore we conclude, that CBs do
“break the circuit”, but there is no such thing as a free lunch. The price is paid by a higher
relative spread. These findings are in line with the findings of Kim and Yang (2008) as well
as Lee and Kim (1995) and therefore support the volatility hypothesis proposed by Kim and
Rhee (1997). Hence, CBs can provide traders with a time period during which they can
obtain information, reassess the market price, and avoid or correct overreaction. The
uncertainty and irrationality in the market will be less severe and thus the price volatility after
a CB is dampened.
By analyzing trading at the satellite market during the CB, we find trading in the satellite
market to be sorely afflicted. We find no trading migration between venues in terms of
number of trades. Instead, the number of trades significantly drops below an anticipated
normal level during a stock’s trade interruption while price volatility and relative spreads
tremendously increase. Evidently, traders retreat from the satellite venue. In respect to
previous academic work on the issue of CBs we reject the finding of the inter-venue
migration model by Subrahmanyam (1994) in case of non-coordinated market safeguards in
the European market system. A potential explanation for our empirical observation is that
investors are reluctant to trade when the dominant market is absent as a liquidity pool. Due to
this absence of the market of last resort, i.e., the home market, this leads to a higher
anticipated risk. Additionally, momentum traders and statistical arbitrage strategies depend
on inter-market price differences. These types of market participants also have no incentive to
continue trading, resulting in an elimination of another significant part of the original order
flow. Our findings are in line with Goldstein and Kavajecz (2004), who investigate liquidity
providing strategies during extreme market movements and provide another explanation for
investors’ market withdrawal.
28
Asking for the efficiency of the satellite market’s price discovery process in this situation, we
analyze price formation before and after the home market reopens continuous trading.
Although, trade prices at the satellite market seem to readjust the same way the home market
does, we actually observe abnormally wide price differences between both markets after the
CB. We therefore conclude that price determination at the satellite market is sorely disturbed
making price coordination with the home market inevitable. By comparing satellite and home
market reference prices, we find the satellite market’s price level to adjust towards the home
market as trading activity continues at the home market, indicating the home market’s auction
price to be the dominant price for future trading.
In particular, our findings are interesting considering the contrary findings within the U.S.
market system. Hereby, the differences between the U.S. market system and the European
market system yield diverse effects in the case of the CB’s effects in fragmented markets.
With its order protection rule in place, the U.S. system seems to be more susceptible to
cascade effects than its European counterpart as trading is forced to alternative venues in the
former. The obligation to pass orders to alternative venues in order to achieve the NBBO
seems to negate any cascade-resolving effects, whereas the European MiFID system without
similar strict best execution obligation appears to be less prone as shown by our analysis.
Further, considering the satellite market’s quality during the CB as well as its restricted price
discovery, it is highly questionable if trading during a home market’s CB in the current
European market system is comprehensible. If trading could be considered useful in the
absence of the home market remains to be answered by the market participants active in such
situations.
While this paper delivers some empirical findings on CBs in the multi-market case, research
in this area should be extended in the future. For instance, the spectrum of markets for an
analysis could be expanded to derive some generalized results for the interplay of different
venues before, during and after trading halts. On the instruments side it would be interesting
to investigate if certain characteristics of a stock, e.g. the degree of liquidity or fragmentation
of order flow, affect the choice of investors to migrate to a different venue.
29
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