circuit breakers as market stability levers ... -...
TRANSCRIPT
Received: 17 April 2018 Revised: 16 August 2018 Accepted: 10 September 2018
RE S EARCH ART I C L E
DOI: 10.1002/ijfe.1709
Circuit breakers as market stability levers: A survey ofresearch, praxis, and challenges
Imtiaz Mohammad Sifat | Azhar Mohamad
Department of Finance, Kulliyyah ofEconomics and Management Sciences,International Islamic University Malaysia,Kuala Lumpur, Malaysia
CorrespondenceImtiaz Mohammad Sifat, Department ofFinance, Kulliyyah of Economics andManagement Sciences, InternationalIslamic University Malaysia, KualaLumpur 53100, Malaysia.Email: [email protected]
Funding informationMinistry of Higher Education Malaysia;Research Management Centre of Interna-tional Islamic University Malaysia, Grant/Award Number: FRGS 15‐232‐0473
JEL Classification: D43; D47; D53of 2007–2008 Crisis and 2010 Flash Crash, the drumbeats for more regulatory
intervention in markets grew louder. Hence, it is unlikely that intervening
[Correction added on 14 November 2018,after first online publication: Thereference Clapham, B., Gomber, P.,Haferkorn, M., & Panz, S. (2017).Managing Excess Volatility: Design andEffectiveness of Circuit Breakers. SSRN.http://dx.doi.org/10.2139/ssrn.2910977have been added to this version].
Int J Fin Econ. 2018;1–40.
Abstract
Circuit breaker, an automated regulatory instrument employed to deter panic,
temper volatility, and prevent crashes, is controversial in financial markets.
Proponents claim it provides a propitious time out when price levels are
stressed and persuades traders to make rational trading decisions. Opponents
demur its potency, dubbing it a barrier to laissez‐faire price discovery process.
Since conceptualization in 1970s and practice from 1980s, researchers focused
mostly on its ability to allay panic, interference in trading, volatility transmis-
sion, prospect of self‐fulfilling prophecy through gravitational pull towards
itself, and delayed dissemination of information. Though financial economists
are forked on circuit breakers' usefulness, they are a clear favourite among reg-
ulators, who downplay the reliability of anti‐circuit breaker findings citing,
inter alia, suspect methodology, and lack of statistical power. In the backdrop
mechanism such as circuit breakers will ebb. But are circuit breakers worth
it? This paper synthesizes three decades of theoretical and empirical works,
underlines the limitations, issues, and methodological shortcomings
undermining findings, attempts to explain regulatory rationale, and provides
direction for future research in an increasingly complex market climate.
KEYWORDS
circuit breakers, financial markets, price limits, trading halts
1 | INTRODUCTION
An efficient market, where participants have access to allinformation, should not incur heavy overreaction orunderreaction leading to unreasonable volatility. Such amarket would facilitate price signals commensurate withchange in fundamentals. Real‐world markets, however,exhibit imperfections, where irrational ex ante and ex posteffects of news are observed. Supply and demand are mis-matched, leading to order imbalance, and potentiallyabnormal volatility. Price discovery is impeded. Althoughthese imperfections are organic in nature, in the sense that
wileyonlinelibrary.com/jour
they arise out of market agent"s' own volition, the imposi-tion of circuit breakers is a regulatory compulsion.Inspired by electrical engineers who use an automatedswitch to protect a circuit from current overload, imposi-tion of collars on security prices as a market stability levergained popularity in the 1980s. Regulators claim its pur-port is to deter overreaction, enforce control, preventcrashes, minimize volatility, and protect liquidity pro-viders. The aftermath of Black Monday crash of October1987 helped propel circuit breaker praxis to limelight asan independent regulatory tool. Attention resurfaced afterthe 2007–2008 financial crisis and again in May 2010
© 2018 John Wiley & Sons, Ltd.nal/ijfe 1
2 SIFAT AND MOHAMAD
following a high‐frequency trading linked flash crash.More recently, flash crashes in cryptocurrency exchangesthrusted cries for circuit breakers. Case in point: the flashcrash in GDAX exchange that saw Ethereum price nose-dive from $317.81 to $0.10 in a matter of 45 ms. In viewof these developments, concern is growing afresh amongregulators, investors, and academics about usefulness ofcircuit breakers, magnified by potential for exacerbatedvolatility due to growing fragmentation of financial mar-kets. Accordingly, stakeholders in advanced financial mar-kets are seeking new mechanisms to handle highuncertainty periods to avert crashes. Meanwhile, inemerging markets, circuit breakers are, in terms of param-eters, relatively vanilla. Yet the rate of adoption since mid‐1990s has been staggering. In fact, the first exhaustivereview of circuit breakers' efficacy contained few prece-dents—mostly North American (Harris, 1997). The nextformal survey by Kim and Yang (2004), detailing whatmakes them so attractive to regulators, included 20venues. Most recently, Abad and Pascual's (2013) bookchapter, focusing on an overarching theme of holdingback volatility in markets via circuit breakers, reports sim-ilar number of venues. This paper's methodology andapproach in organizing empirical literature benefits fromAbad and Pascual's (2013) template and lists over 100active circuit breakers. Our paper advances the discoursefurther by underscoring the challenges in circuit breakerresearch stemming from theoretical difficulties indistinguishing between multifold explanations andstresses the need for experimental studies. Also, we sug-gest avenues for extending the conventional approachand underscore the need for incorporating alternativeand preventive mechanisms from a regulatory standpoint.Lastly, the paper's novelty includes the roles of high fre-quency algorithmic trading nexus within circuit breakerdiscourse, while prognosticating potential sources of dis-ruption from nascent technologies such as Blockchain.
This paper is organized the following way. First, we pro-vide definitions of the key, germane terms used in this field.Next, we discuss the appeal of circuit breakers to regulators,followed by a discussion of its merits and demerits. Then,we detail the theoretical work done in this field, followedby analysis of empirical studies on key hypotheses. Thenwe discuss the methodological constraints plaguingresearch in this area that makes the findings suspect to reg-ulators. Finally, we offer direction for future research.
2 | TERMINOLOGY
Some of the basic, technical jargons used in this paper aredefined here for convenience of non‐specialist economistsand the uninitiated.
2.1 | Circuit breaker
A circuit breaker is an umbrella term in financial eco-nomics for a host of regulatory levers employed by secu-rity market custodians to temper volatility, preventcrash scenarios emanating from inordinate investor over-reaction or malfunctioning algorithm, and to preservemarket integrity. In financial markets, following a sub-stantial price movement, circuit breakers pause or endtrade earlier to allow market participants a time out tocontemplate the fundamentals, gather information, assesspositions, and make rational decisions. Moreover, partic-ipants not yet in the market receive an opportunity toprovide or add liquidity. Regulators hope this would for-fend panic, ease price discovery during market duress,and protect liquidity providers. Most common forms ofcircuit breakers are price limits and trading halts.
2.2 | Price limit
A price limit refers to the maximum stipulated magnitudeby which price may deviate from a reference price. Thus,price limits effectively establish a band of tolerable pricesfor a certain period. Depending on the reference price,which can be last session or day's settlement price, or lastexecuted price within the same session, price limits estab-lish a channel of acceptable percentage (or ticks) bywhich price may vary. Some index futures are subjectedto a price limit designating a deviation band before cashmarket opens. The peak and trough of the channel arecalled “limit up” and “limit down.” Limits can be daily(interday, static) or intraday (dynamic). Some marketsare known to concurrently employ daily and intradaylimits. The earliest documented use of price limit was inDojima exchange in Japan in 18th century (West, 2000).
2.3 | Trading halt
Trading halt refers to a temporary suspension of continu-ous trading for a single security, a group of securities, anexchange, or a group of exchanges usually (but notalways;e.g., the EU) under the ambit of the same regula-tor. It is used to redress—or in anticipation of—marketdisorder; for example, impending corporate announce-ment or news, or to remedy order imbalance. During ahalt, open orders can be cancelled, and options exercised.Halts may be discretionary or rule‐based (automatic). Forthe former, market operator exercises its discretion to halttrading of a security to allow investors equal opportunityto appraise news and make informed decisions on itsbasis—typically ahead of important or relevant news. Itcan also arise out of suspicion over irregular activityregarding the asset's price. Rule‐based halts, contrarily,
SIFAT AND MOHAMAD 3
are activated upon matching predetermined parameters.For example, a stock's trading may be automaticallyhalted once a price limit is reached. These halts are tem-porary in nature and easier to anticipate compared to dis-cretionary halts, which allows participants to alter theirtrading behaviour and strategy keeping in mind prospectsof trade stoppage since. Though rule‐based halts are morecommon compared with discretionary halts, and shorterin duration, exchanges can extend them at theirdiscretion.
2.4 | Volatility interruption
Unlike the circuit breakers popularized in North Amer-ica, European exchanges began experimenting in the1990s with a mechanism which suspends continuoustrading and switches to a call auction (or an extensionof call auction if interruption occurs during the auction)if the next potential price falls beyond a predefined rangebased on a reference price. This mechanism, volatilityinterruption (VI), differs from traditional circuit breakersin several ways. Firstly, VIs are not enforceable market‐wide. Rather, they are enacted on individual securities.This means triggering a VI only impacts that instrumentand not the whole market. Nonetheless, if the affectedsecurity is a bellwether or industry leader, it can poten-tially spillover, making unintended consequences.
Cir
cuit
Bre
aker
s
Price Limits
Equity
Derivatives
Trading Halts
Single Security
Market-wide
Volatility Interruptions
Static and DyTrig
Hybrid Limits / Halts / V
FIGURE 1 Types of circuit breakers in practice in exchanges around
breakers currently in use by exchanges around the world. Although the
outset of regulatory experimentation, nowadays it is common to see a v
price limits and trading halts apply to the same securities, or a group of
and/or rationales for triggering the corresponding circuit breaker mech
Moreover, price discovery during a VI‐triggered auctionoccurs via publishing auction prices and volumes. Lastly,unlike halts which last for considerable amount of timeand sometimes for the whole trading day, VIs typicallylast only a few minutes. In this way, VI can be arguedto be more conducive to derivatives pricing and indexcalculations.
Figure 1 summarizes various forms of circuit breakersemployed around the world.
3 | REGULATORY PRAXIS
Exchanges have experimented with circuit breaker mech-anisms since the 1970s. However, the practice was dimin-utive in scope and received little attention. Following theBlack Monday crash of 1987, the recommendation ofBrady Commission's 1988 Report catapulted the practiceto prominence, leading to greater adoption by exchangesof asset‐specific and market‐wide variants. Mostexchanges set their own thresholds for halts and/orlimits. Halts can be sudden‐death (once triggered, tradestops for the day/session;e.g., Kuala Lumpur StockExchange in the 1990s) or progressive (multi‐tiered). Anexample of a progressive halt mechanism is the NYSE:If S&P falls by 7%, a Level 1 circuit breaker is triggered,and the entire market's trade is halted for 15 min. Uponresumption, a drop of 13% triggers second halt—also for
Static% Based Trigger
Price Based Trigger
Dynamic % Based Trigger
Price Based Trigger
Contract-Specific
Exchange-Specific
Regulatory
Exchange Triggered
News
Order Imbalance
Discretion
Suddent-death
Progressive Tiered
namic Limit gers
IsCourse: Continuous Auction
Course: Discrete Auction
the world. The above image summarizes the various types of circuit
choice of which mechanism to use remained quite simplistic at the
arieties of circuit breakers to be employed concurrently. This means
securities, or the market as a whole. The grey boxes indicate sources
anism
4 SIFAT AND MOHAMAD
15 min. If the market falls by 20% later, the whole marketshuts down for remainder of the day. As for security‐specific collars, London Stock exchange imposes a 5‐minAutomatic Execution Suspension Period if the acceptableprice band (5% to 10% above or below last automated booktrade—depending on the stock's market capitalization) fora security is breached. These limits can be set for all stocksindiscriminately, or be different based on size of thecompany, price size, industry, or a host of discretionaryreasons. The UK's LSE practice is also known as a VImechanism, which is very popular in Europe. Someexchanges also outright reject orders beyond the stipulatedprice range; for example, Amman Stock Exchange, BursaMalaysia, Colombo Stock Exchange, and Tadawul (SaudiArabia).
The practice of circuit breaks across exchanges vary inother ways as well. For instance, exchanges such as USA'sNYSE, Canada's TSX, and Brazil's BOVESPA exemptmarkets from circuit breakers in the final hour of theday's trading. Some exchanges halt trade for the day uponfirst trigger of the limit, whereas others allow multipletrigger hits. Some exchanges allow pricing of a haltedsecurity via discrete trading mechanism (call auction)upon triggering a daily (static) or intraday (dynamic)limit.
Particularly interesting among the circuit breakermechanisms is the American experimentation with a pilotscheme that later crystallized into a Limit‐up‐Limit‐down(LULD) Breaker. The flash crash in May 2010 isconsidered the main precursor to the LULD mechanism.This pilot scheme enacted a 5‐min halt in instrumentsexhibiting excessive fluctuations within a 5‐min tradingwindow to accommodate better absorption of fundamen-tals and news, with the tolerable limit set at ±10%. Thoughinitially this applied to S&P 500 stocks, a year later thespectrum of subject stocks was expanded to all domesticlistings. Keeping in mind that the flash crash may havebeen triggered by a fat finger error, curiously, the pilotscheme detected that the LULD breaker was beingprompted too often due to trading errors. Nonetheless,the pilot scheme was formalized as an official firewallagainst volatility a year later in May 31, 2012, whereasregulators simultaneously modified 1989's circuit breakerrules for the whole market. The LULD breaker introduceda concept of “limit state,” which comes alive whenqualifying stocks enter a quotation period of 15 s if theNational Best Offer equals the lower price band (but doesnot exceed National Best Bid), or the National Best Bidequals upper price band (without crossing National BestOffer.) When a stock is in a limit state, new referenceprices or bands are calculated. The affected instrumentemerges from limit state when the entire size of all limitstate quotations is executed or withdrawn.
Table 1 provides a continent‐wise overview of circuitbreaker practices in exchanges around the world.
4 | PROS AND CONS OF CIRCUITBREAKERS
The integrity of a financial market relies heavily on theintegrity of pricing. Prices determine worth, dictate wheresavings will be mobilized and channelized, resources willshould be allocated, and liquidity will be sought or pro-vided. Thus, when a market fails to facilitate price discov-ery and signalling to the extent that supply and demandno longer are the key determinants, a problem emerges.Therefore, regulatory intervention to iron out such kinkshas merit. Nonetheless, whether the employed mecha-nisms have untoward consequences or fail to achieve pro-fessed objectives—or worse, impair market quality—warrants examination. The debate of whether circuitbreakers are merited depends on a variety of factors:
• Type of circuit breaker
• Is it a price limit or a trading halt?• Is it a call auction?• Is it discretionary or rule based?• Triggering mechanism• Is it induced by order?• Is it induced by volume?• Is it induced by price?
As of 2018—the time of writing this paper—data col-lected for Table 1 in this paper indicate 48 trading halts,98 price limits, and 31 VI mechanisms active among thestudied 152 exchanges, with some venues opting for mul-tiple, overlapping, and/or discretionary schemes. In 16cases, continuous trading is paused, leading to an auc-tion. Meanwhile, with regard to trigger parameter, 11venues activate the circuit breaker on discretionary basis;meaning preset values are not publicly disclosed.
Historically, the Brady Commission Report, sanc-tioned by Reagan administration in 1988 to uncoverwhy the 1987 crash happened, was the first formal paperto advocate market‐wide and individual circuit breakers.Latter proponents invoked the cooling‐off hypothesispropounded by Ma, Rao, and Sears (1989), which arguedcircuit breakers could enforce price stability by curbinglarge price swings caused by speculative overreaction,avert panic, and dissuade price manipulation. Advocatesalso argue that traders' ability to modify or withdrawstanding limit orders during the halt enables informedtraders to manage their risk without incurring losses,
TABLE1
Overview
ofcircuitbreakerpracticesarou
ndtheworld
Exc
han
geSy
mbo
lCity
Cou
ntry
Static
Lim
itDyn
amic
Lim
itRem
arks
Pan
elA:Asia
Shan
ghai
StockExchan
geSSE
Shan
ghai
China(M
ainland)
±10%
(PL)
Non
eApp
liesto
A‐Shares
andmutua
lfunds
±5%
(PL)
Non
eApp
liesto
specialtreatm
ent(ST)an
dB‐Shares
Non
eNon
eFor
new
listings,closed‐endfunds,shares
whose
listingisresumed
aftersuspen
sion
,an
drelistedshares.
Shen
zenStockExchan
geSZ
SESh
enzen
China(M
ainland)
±10%
(PL)
Non
eApp
liesto
A‐Shares
andmutua
lfunds
±5%
(PL)
Non
eApp
liesto
specialtreatm
ent(ST)an
dB‐Shares
MWTH
becameeffectiveon
01/01/16
subjectto
fluctuationof
theCSI
300(SHSZ
300Index).
Suspen
dedon
08/01/16.
Hon
gKon
gStockExchan
geHKEX
Hon
gKon
gHon
gKon
g±24
nom
inal
spread
s9σ
and±
5%App
liesto
pricedeviationsin
open
ingsessionan
dclosingsessionsrespectively.
(PL)
Nolongerap
pliesto
derivativesas
ofJanua
ry2017.
Tok
yoStockExchan
geTSE
Tok
yoJapa
n±¥30–¥7000000
Non
eAbsoluteYen
values
dependingon
priceda
y'sclosing
priceor
specialqu
ote
(PL)(TH)
TSE
allowstw
o15‐m
inmarket‐widecircuitbreakers
forextrem
epricemoves
atitsdiscretion
Osaka
Securities
Exchan
ge/
JASD
AQ
OSE
Osaka
Japa
n8–21%
Non
eNormal:8%
/13%
forstockexch
ange
futures/op
tion
s(12%
/17%
and16%/21%
forsuccessive
expa
nsion
s).
(PL)(TH)
OSE
/JASD
AQ
has
a2000‐pointabsolute
limit.
Chi‐XJapa
nCHIJ
Tok
yoJapa
n±10%
(PL)
Non
eSingle‐stockcircuitbreaker.
Mon
golia
nStockExchan
geMSE
UlanBator
Mon
golia
Discretionary(TH)
Discretionary
Korea
Exchan
geKRX
Busan
Korea
±15%
(Equ
ities)
(PL)
Non
e10–20–30%
(PL)
Non
eSingleStockFutures
Taiwan
StockExchan
geTSE
Taipei
Taiwan
(ROC)
±10%
(PL)
Non
eApp
liesto
domestican
dforeignstocks,REITs,ETFs,
Futures.R
eferen
cepriceisbasedon
day'sop
eningau
ction
±5%
(PL)
Non
eCorpo
rate
bonds
withou
twarrants.Thosewithwarrants
aresubjectto
alim
itpriceof
open
ingprice*(1
±5%
)+
(lim
it‐upprice
ofun
derlyingsecurity
forthat
day–au
ctionreference
price
atop
eningforun
derlyingsecurity)*Exerciseratio
Cam
bodiaSecurities
Exchan
geCSX
Phnom
Penh
Cam
bodia
±5%
(PL)
Non
e
Indo
nesia
StockExchan
geID
XJaka
rta
Indo
nesia
±10%,±15%.±20%
(TH)
Non
eMarket‐wisetrad
inghalt.Level
1incurs
30‐m
inhalt. (Con
tinues)
SIFAT AND MOHAMAD 5
TABLE1
(Con
tinued)
Exc
han
geSy
mbo
lCity
Cou
ntry
Static
Lim
itDyn
amic
Lim
itRem
arks
Pan
elA:Asia
Level
3causes
blan
ketsuspen
sion
untilap
proval
isreceives
from
regu
lator.
±20%,±25%,±35%
(PL)
Non
eAsset‐specificpricelim
itsin
placeviaau
tomated
orderrejection.
Lao
Securities
Exchan
geLSX
Vientian
eLaos
±10%
(PL)
Non
e
Bursa
Malaysia
MYX
Kuala
Lum
pur
Malaysia
±30%
(PL,TH)
±8%
FBMKLCIissubjectto
1hou
rof
trad
inghaltifdo
wn
by10%.20%
fallthereafter
isada
y‐longhalt.
±30%
appliesto
stocks
priced
aboveMYR1.00.F
orstocks
priced
MYR0.99
orless,lim
itisMYR0.30
(innom
inal
term
s).
MalaysiaDerivatives
Exchan
geBMDB
Kuala
Lum
pur
Malaysia
±10%
(PL)
Non
eNot
applicable
tospot
mon
thtrad
esof
futures.
Myanmar
Securities
Exchan
geCen
tre
MSE
CYan
gon
Myanmar
Hierarchical
(PL)
Non
eBased
onabsolute
MMK
value.
Philipp
ineStockExchan
gePS
EMan
ila
Philipp
ines
50%
up,40%
down
±10%
Singapo
reStockExchan
geSG
XSingapo
reSingapo
re(PL)
±10%
Circuitbreakeron
singlesecurity
atan
dabove
$0.50:
±10%
from
thereference
price.
5‐min
cooling‐offperiod
follo
wsdu
ringwhichtrad
ing
canon
lytake
placewithin
the±10%
priceband.
StockExchan
geof
Thailand
SET
Ban
gkok
Thailand
±10–20%
(TH)
Non
e30
and60‐m
inmarket‐widetrad
inghalts
upon
first
andsecondtrigger.
±30%
SSPL
(PL)
±1Price
Mainbo
ardstocks.±60%
fluctuationistoleratedfor
foreignstocks.
HoChiMinhStockExchan
geHOSE
HoChi
Minh
Vietnam
±7%
(PL)
Non
e±20%
onfirst‐da
yof
IPO
listing.
Han
oiStockExchan
geHNX
Han
oiVietnam
±10%
(PL)
Non
e±30%
onfirstda
yof
IPO
listing.
Afghan
istanStockExchan
geAFX
Kabul
Afghan
istan
Non
eNon
e
ChittagongStockExchan
geCSE
Chittagong
Ban
glad
esh
±3.75–10%
(PL)
Non
eLim
itsvary
accordingto
pricesize
andcan
beap
pliedon
nom
inal
values.
Dhak
aStockExchan
geDSE
Dhak
aBan
glad
esh
±3.75–10%
(PL)
Non
eLim
itsvary
accordingto
pricesize
andcan
beap
pliedon
nom
inal
values.
Royal
Securities
Exchan
geof
Bhutan
RSE
BL
Thim
phu
Bhutan
±15%
(PL)
Non
e
(Con
tinues)
6 SIFAT AND MOHAMAD
TABLE1
(Con
tinued)
Exc
han
geSy
mbo
lCity
Cou
ntry
Static
Lim
itDyn
amic
Lim
itRem
arks
Pan
elA:Asia
Bom
bayStockExchan
geBSE
Mumbai
India
±10%,±15%,±20%
(PL)
Non
eDum
mypricelim
itsareavailableforderivatives
National
StockExchan
geof
India
NSE
Mumbai
India
±2%
,±5%
,±10%
Non
eMarket‐widecircuitbreakerof
10%,15%,an
d20%.
Stock‐widecircuitbreakers
applyon
lyto
scrips
withnoderivative
prod
ucts.
(PL,TH)
±20%
limitsap
plyto
debenturesan
dpreferen
ceshares.
MaldivesStockExchan
geMSE
Male
Maldives
Non
e(TH)
Non
eDiscretionaryhaltprovisionsexist
Nepal
StockExchan
geNEPS
EKathman
duNepal
3%,4%
,5%
(TH)
Non
eMarket‐widecircuitbreaker.App
liedforthefirsttime
onMarch
28.Still
room
forch
anges.
Pak
istan
PSX
Karachi
Pakistan
±5%
,7.5%
,10%
(PL)
Non
e
Colom
boStockExchan
geXCOL
Colom
boSriLan
ka±10%
(PL,TH)
Non
e±5%
market‐widecircuitbreaker.
Arm
enianStockExchan
geNASD
AQ.
AM
Yerevan
Arm
enia
Non
eNon
e
Bak
uStockExchan
geBFB
Bak
uAzerbajan
Non
eNon
e
Bah
rain
StockExchan
geBFEX
Man
ama
Bah
rain
±10%
(PL)
Non
eNocircuitbreakerformutua
lfunds
orbo
nds.
Cyp
rusStockExchan
geCSE
Nicosia
Cyp
rus
±10%
(PL)
±3%
GeorgianStockExchan
geSSB
Tblisi
Georgia
Non
eNon
e
Teh
ranStockExchan
geTSE
Teh
ran
Iran
±5%
(PL)
Non
e
Iran
FaraBou
rse
IFB
Teh
ran
Iran
±5%
(PL)
Non
e
Iraq
StockExchan
geISX
Baghda
dIraq
±20%
(PL)
Non
e
Tel
AvivStockExchan
geTASE
Tel
Aviv
Israel
±8%
,±12%
Non
eIfTA‐25moves
by±8%
inrelation
tobsic
index,
market‐widehaltfor45
min
occurs.
(PL,TH)
IfTA‐25moves
by±12%
inrelation
tobsic
index,
suspen
sion
laststillnextbu
sinessda
y.±35%
(PL)
For
equities
andin
convertible
bonds
duringop
eningph
ase.
Amman
StockExchan
geASE
Amman
Jordan
±5%
,±7%
,±10%
(PL,
TH)
Non
e±5%
for2n
d/3rdmarkets;±7.5%
for1stmarket,
and±
10%
forOTCmarket.
Bou
rsaKuw
ait
BK
Safat
Kuw
ait
±20%
(PL)
Non
eExchan
geisin
theprocessof
employingdy
nam
iclim
its
innom
inal
term
s(K
WD
orfils).
BeirutStockExchan
geBSE
Beirut
Leban
on±10%
(PL)
Non
e±15%
forSo
lidereshares
Muscat
Securities
Market
MSM
Muscat
Oman
±10%
(PL)
Non
e
PalestineSecurities
Exchan
gePS
ENablus
Palestine
±5%
(PL)
Non
e
(Con
tinues)
SIFAT AND MOHAMAD 7
TABLE1
(Con
tinue
d)
Exc
han
geSy
mbo
lCity
Cou
ntry
Static
Lim
itDyn
amic
Lim
itRem
arks
Pan
elA:Asia
Doh
aSecurities
Market
DSM
Doh
aQatar
±10%
(PL,TH)
Non
eApp
liesbo
thto
stocks
andindex.
Tad
awul
XSA
URiyad
hSaud
iArabia
±10%
(PL)
Non
eSettlemen
tisexpected
tobe
T+
2from
late‐2017.
Dam
ascusSecurities
Exchan
geDSE
Dam
ascus
Syria
±5%
(PL)
Non
ePreviously
±2%
Borsa
Istanbu
lISE
Istanbu
lTurkey
±20%
(PL)
Non
eApp
liesto
equities
andETFs.±50%
forpreemptiverigh
ts.
Nolim
itforwarrants.
±10%
(TH)
Non
eMarket‐wide.
For
derivativesmarket(V
IOP),da
ilypricelim
itisdefined
incontractspecifications
Abu
DhabiSecurities
Market
XADS
Abu
Dhabi
UAE
15%
up,10%
down(PL)
Non
eWhen
astockfalls
5%,thestockgoes
toau
ctionfor5mins,
andifitfalls
9%itgoes
toau
ctionfor10
min.
Dub
aiFinan
cial
Market
DFM
Dub
aiUAE
15%
up,10%
down(PL)
Non
e±5%
forinactive
stocks
NASD
AQ
Dub
aiDIFX
Dub
aiUAE
Variable(PL)
Non
e±50%
forAED
0to
0.1;
±20%
forAED
±0.1to
0.25;±15%
forAED
0.25
to0.5;
±10%
for>
AED
0.5.
Kazak
hstan
StockExchan
geKASE
Alm
aty
Kazak
hstan
±15%
(PL)
Non
eLim
itsforfuturesvary
betw
een±30%
to±50%.
KyrgyzStockExchan
geKSE
Bishkek
Kyrgyzstan
Non
eNon
e
TashkentStockExchan
geTSE
Tashkent
Uzbekistan
Non
eNon
e
Pan
elB:Australia(C
ontinen
t)
Australia
nSecurities
Exchan
geASX
Sydn
eyAustralia
Non
eNon
eProvisionsforhalton
discretion
arybasisor
attherequ
est
ofacompa
nyor
anticipa
tingan
nou
ncemen
t.
Chi‐XAustralia
CHIA
Sydn
eyAustralia
Non
eNon
e
South
Pacific
StockExchan
geSP
SESu
vaFiji
Non
eNon
e
New
ZealandStockExchan
geNZSX
Wellington
New
Zealand
Variable(PL,TH)
Non
eAsymmetricasset‐specific
limits.
Port
Moresby
StockExchan
gePortMoresby
Papu
aNew
Guinea
Non
eNon
e
Pan
elC:Africa
Algiers
StockExchan
geSG
BV
Algiers
Algeria
Non
eNon
e
Botsw
anaStockExchan
geBSE
Gaboron
eBotsw
ana
Non
eNon
e
Bou
rseRegionaledesValeu
rsMob
ilieres
BVRM
Abidjan
CoteD'Iv
oire
±15%
(PL)
Non
eOvernight
TheEgyptianStockExchan
geEGX
EgyptianExchan
geEgypt
±10%
(VI,TH)
±5%
30‐m
insuspen
sion
fordy
nam
iclim
ittriggers
(Con
tinues)
8 SIFAT AND MOHAMAD
TABLE1
(Con
tinue
d)
Pan
elC:Africa
±5%
,±10%
(TH)
Non
eTem
porary
market‐widetrad
inghaltifEGX100
moves
by±5%
.Day‐lon
gsuspen
sion
if±10%.
Ghan
aStockExchan
geGSE
Accra
Ghan
a±7.5%
(PL)
Non
e
Nairobi
Securities
Exchan
geNSE
Nairobi
Ken
ya±10%
(PL)
Non
eOvernight
LibyanStockMarket
LSM
Tripo
liLibya
Non
eNon
e
Malaw
iStockExchan
geMSE
Blantyre
Malaw
iNon
eNon
e
StockExchan
geof
Mau
ritius
SEM
Port
Lou
isMau
ritius
±8%
(PL)
Non
e
Casablanca
StockExchan
geCasaSE
Casablanca
Morocco
±10%
(VI,TH)
±6%
Dyn
amic
hittriggers
5‐min
suspen
sion
.Upo
nresumptionan
other
4%movein
samedirection
trad
eishalted.
Bolsa
deValores
deMozam
biqu
eBVM
Map
uto
Mozam
biqu
e±15%
(PL)
Non
e
Nam
ibianStockExchan
geNSX
Windh
oek
Nam
ibia
Non
eNon
e
NigerianStockExchan
geNSE
Lagos
Nigeria
±10%
(PL)
Non
eCom
poun
ded
Abu
jaSecurities
andCom
mod
itiesExchan
geASC
EAbu
jaNigeria
Non
eNon
e
Rwan
daStockExchan
geRSE
Kigali
Rwan
daNon
eNon
e
SeychellesSecurities
Exchan
ge(Trop‐X)
SSE
Victoria
Seychelles
Non
eNon
e
Johan
nesbu
rgStockExchan
geJSE
Johan
nesbu
rgSo
uthAfrica
Non
eNon
e
Khartoum
StockExchan
geKSE
Khartoum
Suda
nNon
eNon
e
Swazila
nd
SSX
Mbabane
Swazila
nd
Non
eNon
e
Dar‐es‐Salam
StockExchan
geDSE
Dar
esSalaam
Tan
zania
Non
eNon
e
Bou
rsedesCaleu
rsMob
ilieres
deTun
isBVMT
Tun
isTun
isia
±6%
(PL)
Non
e
Uganda
StockExchan
geUSE
Kam
pala
Uganda
Non
eNon
e
Lusak
aStockExchan
geLuS
ELusak
aZam
bia
±10%
(PL)
Non
eOvernight
WestAfrican
StockExchan
geBRVM
Abidjan
CoteD'Iv
oire
±7.5%
(PL)
Non
e
Zim
babw
eStockExchan
geZSE
Harare
Zim
babw
eUndisclosed
Non
eIm
plem
entedfrom
Summer
2016
aspa
rtof
migration
toau
tomated
trad
ing.
Pan
elC:North
America
Bah
amas
Securities
Exchan
geBISX
Nassau
Bah
amas
±10%
(PL)
Non
e
(Con
tinues)
SIFAT AND MOHAMAD 9
TABLE1
(Con
tinued)
Pan
elC:North
America
Barbado
sStockExchan
geBSE
Bridg
etow
nBarbado
s±15%
(TH)
Non
eMarket‐widetempo
rary
suspen
sion
;curren
tly
underreview
±30%
(PL)
±10%
Stock‐specific.O
rder
rejectionin
placeforvariations
beyond10%.
Bermuda
StockExchan
geBSX
Ham
ilton
Bermud
aNon
e(PL)
Non
e
Toron
toStockExchan
geTSX
Toron
toCan
ada
7%,13%,20%
±10%
Market‐widetrad
inghalts
occurifS&
P500or
TSX
drop
sby
7%(Level
1),13%
(Level
2),an
d20%
(Level
3)(V
I,TH)
Breachof
dynam
iclim
itwithin
5‐min
lead
sto
a5‐min
halt.
Mon
trealExchan
geMX
Mon
treal
Can
ada
Discretionary
(TH)
Non
eSeton
amon
thly
basisaftercolla
boratingwith
clearingcorporation
Bolsa
deValores
deElSalvad
orBVES
SanSalvad
orElSalvad
orNon
eNon
e
Bolsa
Nacional
deValores
BNV
Gua
temalaCity
Gua
temala
Non
eNon
e
Haitian
StockExchan
geHSE
Port‐au‐Prince
Haiti
Non
eNon
e
Bolsa
Cen
troamerican
ade
Valores
BCV
Tegucialpa
Hon
duras
Non
eNon
e
Jamaica
StockExchan
geJSE
Kingston
Jamaica
±15%
(TH)
Non
eMarket‐widetempo
rary
suspen
sion
±30%
(PL)
Non
eStock‐specific
Bolsa
Mexican
ade
Valores
BMV
MexicoCity
Mexico
±15%
(TH)
Non
eHaltingisdiscretion
ary.
Trinidad
andTob
agoStockExchan
geTTSE
Port
ofSp
ain
Trinidad
andTob
ago
±15%
(TH)
Non
eMarket‐widetempo
rary
suspen
sion
±30%
(PL)
±10%
Stock‐specific.O
rder
rejectionin
place
forvariationsbeyond10%.
NASD
AQ
NASD
AQ
New
YorkCity
USA
7%,13%,20%
Non
eMarket‐widetrad
inghalts
occurifS&
P500drop
sby
7%(Level
1),13%
(Level
2),
and20%
(Level
3)(TH)
Level
1an
dLevel
2aretempo
rary
halts,whereas
Level
3resultsin
suspen
sion
fortheda
y's
remaininghou
rs.
Level
1an
dLevel
2halts
arefor15
min
ifthey
occurbefore
3.25
PM.
LULD
Break
erPriceBan
d=
(Referen
cePrice)
+/−
((Referen
cePrice)
x(Percentage
Parameter))
Up/Dow
nBan
dscomefrom
multiplyingReferen
cePrice
by%
Parameter
andthen
addingor
subtractingthat
valuefrom
RP.
Theresultingvalues
arethen
roun
dedto
thenearest
penny.
(Con
tinues)
10 SIFAT AND MOHAMAD
TABLE1
(Con
tinue
d)
Pan
elC:North
America
New
YorkStockExchan
geNYSE
New
YorkCity
USA
7%,13%,20%
Non
eMarket‐widetrad
inghalts
occurifS&
P500drop
sby
7%(Level
1),13%
(Level
2),an
d20%
(Level
3)(TH)
Level
1an
dLevel
2aretempo
rary
halts,whereasLevel
3resultsin
suspen
sion
fortheda
y'sremaininghou
rs.
Level
1an
dLevel
2halts
arefor15
min
ifthey
occur
before
3.25
PM.
LULD
Break
erPriceBan
d=
(Referen
cePrice)
+/−
((Referen
cePrice)
x(Percentage
Parameter))
Up/Dow
nBan
dscomefrom
multiplyingReferen
cePriceby
%Pa
rameter
andthen
addingor
subtractingthat
valuefrom
RP.
Theresultingvalues
arethen
roun
dedto
thenearest
penny.
Level
1an
dLevel
2halts
arefor15
min
ifthey
occurbefore
3.25
PM.
Pan
elD:So
uth
America
Bue
nos
Aires
StockExchan
geBCBA
Buen
osAires
Argen
tina
±10%,±15%
(TH)
Non
eMarket‐widetrad
inghalts.
Bolsa
Bolivianade
Valores
BVB
LaPaz
Bolivia
Undisclosed(TH)
Non
e
BM&FBovespa
BOVESP
ASaoPau
loBrazil
Undisclosed(V
I,TH)
Undisclosed
Market‐widetrad
inghaltoccurs
at3levels:
10%,15%,an
d20%.Level
1an
d2resultin
30an
d60‐m
inhalts.
Level
3shutsdo
wnthemarketfortherest
ofthe
day.
Dyn
amic
volatilityinterrup
tion
slast
2min.
Rio
deJaneiro
StockExchan
geBVRJ
Rio
deJaneiro
Brazil
Undisclosed(V
I,TH)
Undisclosed
Market‐widetrad
inghaltoccurs
at3levels:10%,15%,
and20%.L
evel
1an
d2resultin
30an
d60‐m
inhalts.
Level
3shutsdo
wnthemarketfortherest
oftheda
y.Dyn
amic
volatilityinterrup
tion
slast
2min.
Bolsa
Com
erciode
Santiago
SSE
Santiago
Chile
Discretionary(TH)
Non
e
Bolsa
deValores
deColom
bia
BVC
Bogota
Colom
bia
±10%;±15%
(TH)
Non
eMarket‐widetrad
inghaltat
±10%
for30
min.Second
levellead
sto
day‐longsuspen
sion
±6.5%
,±7.5%
,±10%
(TH)
Non
e2.5‐min
stock‐specific
trad
inghalts.
Bolsa
deValores
deLim
aXLIM
Lim
aPe
ru±7%
,±10%
(TH)
Non
eMarket‐widetrad
inghalts
basedon
BVLindex.
±15%
(PL,VI)
Non
eSingleStockcircuitbreakerforequities.ELEXtrad
ing
platform
rejectsorders
ifpricevolatilityissevere.
(Con
tinues)
SIFAT AND MOHAMAD 11
TABLE1
(Con
tinue
d)
Pan
elD:So
uth
America
Bolsa
deValores
deMon
tevideo
BVL
Mon
tevideo
Urugu
ayNon
eNon
e
Bolsa
deValores
deCaracas
BVC
Caracas
Ven
ezue
laNon
eNon
e
Pan
elE:Europe
Tiran
aStockExchan
geXTIR
Tiran
aAlban
ia
Wiener
Borse
XWBO
Vienna
Austria
Undisclosed(V
I)Undisclosed
Volatility
interrup
tion
istriggeredup
onbreach
ingan
undisclosedpriceband(assum
ed2–4%
).Callau
ctionen
sues,lasting2–5min.
BelarusianCurren
cyan
dStock
Exchan
geBCSE
Minsk
Belarus
Euron
extBrussels
XBRU
Brussels
Belgium
±10%
(VI,TH)
±5%
±6%
static
and±
3%dy
nam
iclim
itforBEL20
stocks.
Sarajevo
StockExchan
geXSSE
Sarajevo
Bosnia
&Herzegovina
±20%;±50%
(PL)
±3%
ST1segm
ent,comprising30
mostliq
uidshares,is
subjectto
±20%
static
limit
ST2segm
ent,comprisingallshares
sansST
1,issubject
to±50%
static
limit
BothST
1an
dST
2aresubjectto
±3%
dynam
iclim
it,
whichtriggers
a15‐m
involatilityinterrup
tion
Bulgarian
StockExchan
geXBUL
Sofia
Bulgaria
±10%
(PL)
±5%
Prem
ium
equities
asdesign
ated
byBSE
,non
‐leveraged
ETFs,an
dspecialpu
rposevehicles.
±20%
(PL)
±10%
Stan
dard
equities
asdesign
ated
byBSE
,compensatory
instrumen
ts,an
dleveragedETFs.
±5%
(PL)
±2.5%
Bon
ds±30%
(PL)
±15%
BaSEmarketan
dshares
trad
edat
scheduled
auctions.
Dyn
amic
limitdo
esnot
applyto
thelatter.
ZagrebStockExchan
geXZAG
Zagreb
Croatia
±10%
(PL)
Non
eApp
liesto
shares
whichtrad
eon
75%
trad
ingda
yswith
averageda
ilyturnover
>HRK
100,000.
±15%
(PL)
Non
eApp
liesto
shares
whichtrad
eon
50%
trad
ingda
yswith
averageda
ilyturnover
>HRK
50,000.
±25%
(PL)
Non
eRem
ainingshares.
Pragu
eStockExchan
geXPR
APrague
Czech
Repub
licVariable(V
I)Variable
Ifthemidpo
intof
theallowable
spread
deviates
bymore
than
20%
from
themidpo
intat
thestartof
theop
enph
asean
ddo
esnot
Returnto
within
spread
<2min,trad
eissuspen
dedfor
5min.Ifat
least3marketmak
erscompete
forqu
otes
duringhalt
(Con
tinues)
12 SIFAT AND MOHAMAD
TABLE1
(Con
tinued)
Pan
elE:Europe
Theperm
issiblespread
isextendedby
10%
post‐resum
ption,
withprovisionforup
to±50%.
Cop
enhagen
StockExchan
geXCSE
Coepn
hagen
Den
mark
±15%
(PL,VI)
±5%
For
OMXC20
stocks,static
anddy
nam
iclim
itsare
±10%
and±
3%Static
anddy
nam
iclim
itslead
to3an
d1‐min
suspen
sion
s,follo
wed
bydiscrete
callau
ctions.
Tallin
nStockExchan
geXTAL
Tallin
nEston
ia±15%
(PL)
Non
e
Helsinki
StockExchan
geXHEL
Helsinki
Finland
±15%
(PL,VI)
±5%
For
OMXC20
stocks,static
anddy
nam
iclim
itsare
±10%
and±
3%Static
anddy
nam
iclim
itslead
to3an
d1‐min
suspen
sion
s,follo
wed
bydiscrete
callau
ctions.
Euron
extPa
ris
XPA
RParis
France
±10%
(TH,VI)
±2%
5‐min
coolingperiod
fordy
nam
iclim
ithit.
GeorgianStockExchan
ge
Deu
tsch
eBorse
Group
XETR
Frankfurt
German
yUndisclosed(V
I)Undisclosed
Volatility
interrup
tion
auctionlasts2min.
Eurex
Exchan
geXEUR
Frankfurt
German
yUndisclosed(V
I)Undisclosed
Openingau
ction,volatilityinterrup
tion
auction,
andclosingau
ctionsaresubjectto
freezes.
Athen
sStockExchan
geATHEX
Athen
sGreece
±10%;±18%
(PL)
Non
eDoesnot
applyto
first3da
ysof
IPO
stocks.
For
allequities
onFTSE
/ASE
20Index:
10%
up/dow
nfor15
min
andnolim
itthereafter.
Bud
apestStockExchan
geBUX
Buda
pest
Hun
gary
±10%,±15%
(TH,
VI)
±5%
Tradingpa
uses
last
betw
een2to
15min.B
SEhalts
aninstrumen
ton
ceon
lyin
ada
y.
NordicExchan
geof
Icelan
dXIC
EReyjkjavik
Icelan
dVariable(TH,VI)
Variable
OMXC20,OMXH25,an
dOMXS30aresubjectto
±10%
static
and±
2%dy
nam
iclim
its
Dyn
amic
±3%
forOMXI15,
and±
5%lim
itforinvestmen
tfunds,ETFs,First
North
andInternational
OMXSshares.
±15%
static
limitforFirst
North
andInternational
OMXSshares.
±10%
dynam
ican
d±
20%
static
limitforpennystocks
andilliquidshares.
IrishStockExchan
geXDUB
Dublin
Irelan
dVariable(V
I)±2%
Volatility
interrup
tion
scanbe
prolon
gedin
stressful
marketcircum
stan
ces.
Borsa
Italiana
XMIL
Mila
nItaly
±10%
(PL)
Variable
Pricebanddepends
onmarketsegm
entan
dindu
stry
RigaStockExchan
geXRIS
Riga
Latvia
±15%
(PL)
Non
e
Nasda
qOMXViln
iusStockExchan
geVILSE
Vilnius
Lithua
nia
±15%
(PL)
Non
e
(Con
tinues)
SIFAT AND MOHAMAD 13
TABLE1
(Con
tinued)
Pan
elE:Europe
Luxembo
urg
StockExchan
geLUXXX
Lux
embo
urg
Lux
embo
urg
±10%
(VI,PL
)±5%
±6%
static
and±
3%dy
nam
iclim
itforBEL20
stocks.
Macedon
ianStockExchan
geXMAE
Skop
jeMacedon
ia±10%
(PL)
Non
e
Borza
Malta
XMAL
Valletta
Malta
Undisclosed(PL)
Undisclosed
Ineffect
since
regu
latory
chan
gesin
2014.
Moldo
vaStockExchan
geMSE
Chisinau
Moldo
va±25%
(PL)
Non
e
Mon
tenegro
StockExchan
geMSE
Podg
orica
Mon
tenegro
±10%
(PL)
Non
e
New
Securities
StockExchan
geNEX
Podg
orica
Mon
tenegro
±10%
(PL)
Non
e
Euron
extAmsterda
mXAMS
Amsterda
mNetherlands
±10%
(VI,TH)
±5%
Referen
cepriceisre‐adjustedon
lyafteran
incoming
orderhas
been
match
edagainst
orders
inthe
Cen
tral
Order
Boo
k.
OsloStockExchan
geXOSL
Oslo
Norway
±15%
(VI,TH)
±5%
OBXshares
only.O
ther
shares
aresubjectto
±25%
static
and±
15%
dynam
iclim
it.Im
plem
ents
match
inghalts
andspecialob
servations.
Warsaw
StockExchan
geXWAR
Warsaw
Poland
Undisclosed(PL)
Undisclosed
Euron
extLisbo
nXLIS
Lisbo
nPo
rtug
al±10%
(VI,TH)
±5%
Referen
cepriceisre‐adjustedon
lyafteran
incomingorder
has
been
match
edagainst
orders
intheCen
tral
Order
Boo
k.
BucharestStockExchan
geXBSE
Bucharest
Rom
ania
±15%
(PL)
Non
e
Moscow
Exchan
geMISX
Moscow
Russia
±20%
(TH,VI)
Non
eStock‐specific.Callau
ctionscanbe
holdintrad
ayif
limitisbreach
ed.
Belgrad
eStockExchan
geXBEL
Belgrad
eSerbia
Undisclosed(PL)
Undisclosed
Implem
entedfrom
Decem
ber2016.
BratislavaStockExchan
geSK
SMBratislava
Slovak
ia±10%
(PL)
Non
e
LjubljanaStockExchan
geLjubljana
Sloven
ia±10%
(PL)
Non
ePrim
emarket(bluech
ip)fluctuationsaretoleratedtill±30%.
Bolsa
Valores
deBarcelona
BMEX
Barcelona
Spain
Variable(V
I,PL
)Variable
Volatility
interrup
tion
inplaceifeither
limitisbreach
ed.C
all
auctionlasts5min
with30‐s
rando
men
d.Stan
dardized
categories
forpo
ssible
static
rangesare±4%
,±5%
,±6%
,±7%
,an
d±
8%.
Stan
dardized
categories
forpo
ssible
dynam
icrangesare1%
,1.5%
,2%
,2.5%
,3%
,3.5%
,4%
and8%
.
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deMad
rid
BMEX
Mad
rid
Spain
Variable(V
I,PL
)Variable
Volatility
interrup
tion
inplaceifeither
limitisbreach
ed.C
all
auctionlasts5min
with30‐s
rando
men
d.Stan
dardized
categories
forpo
ssible
static
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,±5%
,±6%
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,an
d±
8%.
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dardized
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forpo
ssible
dynam
icrangesare1%
,1.5%
,2%
,2.5%
,3%
,3.5%
,4%
and8%
.
(Con
tinues)
14 SIFAT AND MOHAMAD
TABLE1
(Con
tinued)
Pan
elE:Europe
Stockh
olm
StockExchan
geXST
OStockh
olm
Sweden
±15%
(VI,PL
)±5%
OMXS30stocks
aresubjectto
±10%
static
and±
3%dy
nam
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its.
Static
anddy
nam
iclim
itslead
to3an
d1‐min
suspen
sion
s,follo
wed
bydiscrete
callau
ctions.
SIXSw
issExchan
geXSW
SZurich
Switzerlan
dNon
e(V
I,TH)
±1.5%
,±2.5%
NoMWCB.Large
andmid‐smallcapstocks
face
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d15‐m
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inghalts
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ing
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geXUAX
Kiev
Ukraine
Non
eNon
e
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donStockExchan
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gesfrom
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dependingon
market
segm
ent,liq
uidity,an
dsize
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anAutom
atic
Execution
Suspen
sion
Period
(AESP
).
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isExchan
geAQXE
Lon
don
Pan‐Europ
eVariable(V
I,TH,
PL)
Variable
Collars
aresetbasedon
marketsegm
ents.
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supp
ortedinclud
eGBP,
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SEK,an
dCHF.
BATSChi‐XEurope
BXE
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don
Pan‐Europ
e±10%
(VI,TH,PL
)±5%
Currencies
of15
participatingmarkets
aresupp
orted.
Note.Thistablech
roniclesthevariou
scircuitbreakers
employed
bystockexch
angesarou
ndtheworld.T
heinform
ationisprocured
from
avarietyof
sources
includingpu
blicdo
maininform
ation,voluntary
disclosure
byexch
anges,fact
book
s,an
dcorrespo
ndence
withexch
ange
person
nel.P
articipa
ntsof
theLULD
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eBATSExchan
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ATSY‐Exchan
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hicagoBoard
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geIncorporated
(CBOE),
ChicagoStockExchan
ge,E
DGAExchan
ge,E
DGXExchan
ge,N
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ASD
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ational
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YSE
,NYSE
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ARCA.F
ortheLULDbreakerschem
e,please
referto
Section3in
mainbo
dyof
thispa
per.Theacronym
sPL
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dVIcorrespo
ndto
pricelimits,trad
inghalts,an
dvolatility
interrup
tion
s.
SIFAT AND MOHAMAD 15
16 SIFAT AND MOHAMAD
which in turn will increase liquidity and participationaround equilibrium price upon resumption (Copeland &Galai, 1983). Moreover, circuit breakers are hoped to edu-cate the market when channels of information transmis-sion (i.e., quotes) are absent (Greenwald & Stein, 1988)and in so doing promote price discovery and decreaseinformation asymmetry.
Both price limits and trading halts brake the pricechange mechanism. Whether this slowing down is benefi-cial depends on the source of volatility. If the source isnewly available fundamental information, the halt is onlydelaying the inevitable. Trade stoppage conveys no infor-mation on price expectations and could fuel further panicspeculation resulting in greater transitory volatility whentrade resumes. However, halt of trade before noise tradersexecute panic‐driven orders would reduce transitory vola-tility and hence be desirable.1 Similarly, if excessive noisetrades cause an order imbalance, halts could benefit themarket by protecting noise traders from losses stemmingfrom operating in a market with suboptimal depth. More-over, this allows market makers a chance to enter themarket and provide liquidity (Kodres & O'Brien, 1994).In this scenario, Greenwald and Stein's (1991) theoreticalmodel shows informed traders' transactional risk dropswhen prices move fast due to uninformed trades asvalue‐seeking traders cautiously retreat since they areunsure at what price their trades will execute. Conse-quently, market participants have a greater incentive tobe more informed before opening or closing a position.
Trading halts can also give brokers more time to col-lect margins. Brennan (1986) argued circuit breakerscan act as a partial substitute for margin requirements ifmarket participants are unsure about the eventual equi-librium prices during the time out period. For example,a commodity trader posting 8% margin will lose all ifprice goes down by 20%. Should an 8% fall happen imme-diately, though the trader should lose his whole position,in practice, he only loses the 8% margin, and the brokerstands to collect additional 12% later. However, with acircuit breaker of 10% in place, and neither the tradernor the broker knowing the eventual price trajectory tobe 20% lower, the trader has an opportunity to attend tothe first margin call voluntarily. This allows the brokermultiple opportunities to collect margin when circuitbreakers are in place. Failure to meet the margin callgives brokers more time to trade to stop the loss. More-over, when a security or the market is in duress, stop‐loss
1The term “noise” comes from Black's (1986) definition: “Noise in thesense of a large number of small events is often a cause factor muchmore powerful than a small number of large events can be.” Financialeconomics literature regards noise a result of sudden liquidity basedand frequently inelastic demand on the part of a market participant.
orders aggravate an already stressed order‐book floodedwith uninformed orders. In this way, trading halts candecrease transitory volatility. Moreover, an order‐drivenmarket may boast higher liquidity with a trading haltmechanism. In these markets, traders offering standinglimit order suffuse liquidity. In normal circumstances, ifprice drops fast, a trader with standing limit orders willincur loss as the price continues to drop. However, a haltchanges the mechanism from continuous to single‐priceauction upon resumption, when all orders are executedat the same settlement price. If a large selling orderimbalance exists, all limit order buyers will receive it atthe low clearing price. This protects the limit order buyersand encourages them to provide more liquidity in calmermarket circumstances.
Contrarily, price limits and halts can increase transi-tory volatility if traders are afraid that trading will stopbefore they can submit order, leading to a hastening oforder placement to increase the likelihood of execution.This triggers greater volatility, and rational traders recoilfrom trading amid fast‐changing quotes. This phenome-non is known as the magnet effect, coined bySubrahmanyam (1994), who expanded on Lehmann's(1989) predictions and later theoretically demonstratedthis effect. He later postulated that rule‐based halts aremore susceptible to magnet effect due to higherpredictability compared with discretion‐based halts(Subrahmanyam, 1997).
There is a possibility that informed traders may devoteless time to monitoring the market if they know that theywill be notified if trade is halted. Therefore, market liquid-ity may worsen in between trade halts, exacerbatingtransitory volatility, and leading—eventually—to moretrade halts. For countries with multiple exchanges, a cir-cuit breaker trigger in one market may cause perils inother exchanges. If only one market's trade stops, orderflow diverts to the remaining open market(s). Thus,solitary circuit breaker regimes may be counterproductive.Hence, many early researchers suggested coordination ofregulation among exchanges to facilitate meeting higherdemand for liquidity in multiple markets instead of one(Lauterbach & Ben‐Zion, 1993).
Through a sequential microstructure trade model,Glosten and Milgrom (1985) demonstrate that unin-formed traders acquire information by observing thetrade process. Thus, trade contains an informationalcontent, which is learnable only when trade is active.This leads to opponents arguing that absence of tradedelays price discovery by postponing informed and unin-formed agents' reactions to new information (Fama,1989). Moreover, if large price moves are induced byheavy one‐sided order flow (i.e., order imbalance) andtrigger a halt, informed traders are forced to temporize
SIFAT AND MOHAMAD 17
partial or full trading strategies, and whatever volatilitywas due to take place is splattered over subsequent trad-ing sessions, typically with reduced liquidity (Chordia,Roll, & Subrahmanyam, 2002; Seasholes & Wu, 2007).Regarding this, Roll (1989) remarks: “… most investorswould see little difference between a market that wentdown 20 percent in one day and a market that hit a 5 per-cent down limit four days in a row. Indeed, the formermight very well be preferable.”
To sum up, opponents' view on circuit breakers can becondensed into four points: volatility spillover across sub-sequent trading session, trading interference, delayedinformation transmission, and gravitational pull or mag-net effect hypothesis. After the flash crash of 2010, freshquestions surfaced as to whether the trading halt devicesdesigned in rather simpler trading environments threedecades ago are still relevant in today's high frequencyzeitgeist and, if not, to what extent should they be tai-lored, or should the regulators go back to the drawingboard and start anew. The computerization of tradingand liquidity provision, coupled with trade decentraliza-tion, has led to a distinct rise in volume and volatility inthe new climate (Brogaard, 2011). Regulators and mar-kets in the United States coordinated on a large‐scalepilot project after the crash to investigate the efficacy ofthe classical circuit breaker regime versus recalibratednarrow band of single stock price limits. Concurrently,European regulators took steps to move away from theendemic discrete circuit breaker regimes towards a uni-fied framework to allow circuit breakers to operate acrossvenues. To what extent this will succeed remains to beseen because exchanges have a vested interest in settingindividual rules in a competitive environment to attractorder flow. Nonetheless, Biais and Woolley (2011) positthat without tailor‐made cross‐platform streamliningacross markets, circuit breakers cannot be effective any-more since in modern age of high‐frequency trading arbi-trage occurs across markets and suspending trade in theunderlying spot while allowing the derivative trade canbe dangerous.
5 | THEORETICAL BACKGROUND
Barring Brennan's (1986) conjecture on using price limitsas a substitute to margin requirements in the futures mar-kets to confirm contract compliance, prior to the BradyCommission Report, theoretical discourse on circuit brea-kers was absent from academia. The earliest discussants,Kyle (1988), Greenwald and Stein (1988), Lehmann(1989), Fama (1989), and Moser (1990), provide theoreti-cal discussions on why circuit breakers can be reasonedto be a good or bad idea. These discussions laid the
groundwork for later economists to build a theoreticalframework for circuit breakers.
5.1 | Pioneer models
First, Greenwald and Stein's (1991) model operates underthe assumption that circuit breakers strive to re‐establishthe information flow when information transmission isinterrupted somehow. In this model, traders are disin-clined to participate when heightened uncertainty sur-rounds true value of a security. Moreover, a randomand exogenous value was assigned to value‐seeking inves-tors who respond to a volume shock from noise traders.This is partially attributable to the uncertainty surround-ing the number of traders engaged in market surveillanceat that point and magnifies the transactional risk whichmakes traders withdraw from the market whenever unin-formed traders cause quick price movements. The effectof circuit breaker on distribution of number of firstvalue‐seeking responders is missing from this model.Nonetheless, the authors' conclusion that the value‐seek-ing informed traders benefit from a superior understand-ing of what is transpiring in the market with activetrading halts provided fodder for future theoreticalmodels.
Kodres and O'Brien's (1994) model promotes Pareto‐optimal risk sharing by decreasing unanticipated largeprice swings and suggests that limits may be effective inpreventing liquidity providers who don't continuouslysurvey the market incur large losses. Slezak's (1994) theo-retical framework supposes trade cessation to delay dis-semination of private information. Thus, the modelshows that trading halts increase risk premia and volatil-ity by restricting public flow of information. Chowdhryand Nanda's (1998) model proposes circuit breakers as amarket stabilizing instrument by eliminating potentiallytroublemaking prices. Their model argues that pricelimits coupled with flexible margin requirement can con-siderably stabilize the market.
Chou, Lin, and Yu's (2003) model shows that pricelimits can alleviate default risk and lower the effectivemargin requirement. This model, an extension ofBrennan's (1986), supports the Brady Commission'ssuggestion that coordination of circuit breakers acrossmarkets will facilitate spot and futures price limits to bepartial substitutes for each other and aid contract fulfil-ment. For speculative markets, Westerhoff's (2003) modelfinds price limits to promote social welfare and placatevolatility. It should be pointed out here that among theearliest discussants, Fama (1989), Kyle (1988), Lehmann(1989), Telser (1989), and Moser (1990) do not formulatea model to support their arguments. Their primaryconcerns with circuit breakers are—as mentioned
18 SIFAT AND MOHAMAD
earlier—regarding volatility spillover, price discoverydelay, restricted access to liquidity, and tradinginterference.
5.2 | Discrete trading
Although the theoretical discussions so far rely on simple,continuous trading‐based circuit breakers, Madhavan(1992) proposes a switch to call auctions in times ofmarket duress. His model shows that continuous marketsmay not be viable or desirable when information asym-metry is high. Hence, adopting a pure trading halt canworsen the original problem because once trading ishalted, resuming continuous trade with re‐establishedinformation flow may be difficult or even impossible.The model contends periodic trading mechanisms to bemore robust in redressing information asymmetry andhence suggests a temporary switch to a call auction toavert market failure. He further argues that a batch mar-ket is better than a trade halt because the discrete tradingsystem can stay active when dealers refuse to play marketmakers and thereby provide information signals thatallow resumption of continuous trading. The model alsoproposes a bid‐spread higher than a preset critical level—set stochastically based on recent volume and spreads—to trigger the switch. Madhavan remarks that thistriggering mechanism should outperform the popularone (at the time) because big price moves may be dueto change in firm fundamentals. Later, using a Bayesianmodel, Harel and Harpaz (2006) predict that tightenedlimit regulations would impede the price discoveryprocess and diminish social welfare.
5.3 | Magnet effect
Focusing on trading strategies of informed traders andliquidity providers, Subrahmanyam (1994) proposes amodel suggesting the possibility of a magnet effect, firstdiscussed by Lehmann (1989). In this model, rule‐basedhalts incentivize uninformed traders to accelerate theirtrades in a concentrated way, leading to higher ex antevolatility as the price moves closer to the limit, thoughnot necessarily due to depressed liquidity. In a follow‐uppaper, Subrahmanyam (1995) shows that discretion‐based halts outperform rule‐based halts in easing themagnet effect. In a later paper, Subrahmanyam (1997)shows that when facing a realistic likelihood of a trig-ger‐hit, informed traders postpone their orders to preventa halt. Because this dampens liquidity, the model suggestsintroduction of discretionary randomness in the trigger-ing mechanism to encourage higher liquidity. In a round-about way, this phenomenon was shown by Slezak (1994)through a multiperiod market closure model, whereby
trade interruption parries resolution of informationuncertainty and levies undue risk on both informed anduninformed investors. In experimental simulationsettings, Ackert, Church, and Jayaraman (2001) find thattraders accelerate their orders when faced with imminenttrade barrier—implying magnet effect.
5.4 | Agent‐modelling approach
Using a market model with heterogeneously informedagents, Anshuman and Subrahmanyam (1999) show thatcircuit breakers reduce the bid‐ask spreads as informedtraders need to procure less information, though thiscomes at a cost of diminished price efficiency. Therefore,the authors conclude the optimal price limit to be theresult of a trade‐off between liquidity and informationalefficiency. Spiegel and Subrahmanyam (2000) proposean adverse selection‐based model and contend that atrade halt trigger for any stock poses large informationasymmetry risk for connected stocks, for example, sameindustry or correlated stocks. Kim and Sweeney's (2002)model shows that informed traders may be reluctant to“show their hands” when price nears the limit butremains distant from equilibrium as the opportunity costwould be too high. Thus, circuit breakers may protract anexisting information asymmetry.
5.5 | Price abuse and manipulation
Working on a manipulation angle, Edelen and Gervais(2003) extend assumptions of agency theory to circuitbreakers and reason that trading halts can aid principals(exchanges, regulators) monitor and prevent abusive pric-ing by agents (market specialists, operators). Kim andPark's (2010) model makes similar claims. Their triperiodmodel of private and public information arrivals showsthat when unanticipated private information arrives,circuit breakers minimize profit potentials of large,informed investors who would otherwise gain by pricemanipulation, often through disseminating false informa-tion beforehand. This restraint on price manipulation,however, sacrifices pricing efficiency.
6 | EMPIRICAL WORKS
Many studies have examined circuit breakers from a widearray of angles. In this section, we categorize the studiesaccording to the four major points of contention betweenproponents and opponents. The discussions in Subsec-tions 6.1 to 6.4 are complemented by Table 2, whichdepict summarized findings of the major empirical worksin this field. Interestingly, from the tables, some
TABLE 2 Matrix of empirical works on circuit breakers
Study Year Data period Market S/FVolatilityspillover
Pricediscovery
Tradinginterference
Magneteffect
Panel A: Empirical Studies on Daily Price Limits
Ma, Rao, & Sears 1989 1975–1988 USA F Improved
Chung 1991 1979–1987 Korea S Deteriorated
Kao & Ma 1992 1979–1987 Currency F Improved
Chen 1993 1987–1990 Taiwan S Deteriorated Delayed
Lee & Kim 1995 1980–1989 Korea S Deteriorated
Chen & Jeng 1996 1979–1991 Currency F Improved Delayed
Arak & Cook 1997 1980–1987 Treasury Bonds F Improved Absent
Evans & Mahoney 1997 1995 NYCE F Mixed Mixed
Kim & Rhee 1997 1989–1992 Tokyo S Deteriorated Delayed Deteriorated
Berkman & Steenbeek 1998 1992 Nikkei‐225 Index F Deteriorated Absent
Chen 1998 1968–1994 Commodity F Delayed
Shen & Wang 1998 1988–1995 Taiwan S Delayed
Phylaktis 1999 1990–1996 Athens S Mixed
Park 2000 1986–1998 Commodity F Mixed
Hall & Kofman 2001 1988 Commodity F Absent
Huang, Fu, & Ke 2001 1990–1996 Taiwan S Absent
Kim & Rhee 2001 1975–1996 Taiwan S Deteriorated
1998–2001 Mauritius S Deteriorated Delayed Deteriorated
Berkman & Lee 2002 1994–1996 Korea S Improved
Feng 2002 1995–1997 Shanghai S Mixed Mixed Mixed Mixed
Kim & Sweeney 2002 1991–1994 Taiwan S Delayed Deteriorated
Ryoo & Smith 2002 1988–1998 Korea S Delayed
Yang et al. 2003 1989–2000 Taiwan S Present
Cho et al. 2003 1998–1999 Taiwan S Present
Veld‐Merkoulova 2003 1968–1998 Commodity F Mixed Delayed Deteriorated
Lee & Chou 2004 1997 Taiwan S Deteriorated
Chen, Rui, & Wang 2005a 1996–2003 China S Improved Improved
Chan 2005 1995–1996 Kuala Lumpur S Delayed Deteriorated Present
Diacogiannis et al. 2005 1995–1998 Athens S Deteriorated Delayed
Henke & Voronkova 2005 1996–2000 Warsaw S Deteriorated Delayed
Bildik & Gulay 2006 1998–2002 Istanbul S Deteriorated Delayed Deteriorated Present
Kim, Yagüe, & Yang 2008 1998–2001 Spain S Deteriorated Delayed Deteriorated
Anolli & Petrella 2007 2000–2003 Italy S Mixed Improved Mixed
Stamatiou 2007 1998–2001 Athens S Deteriorated Mixed Deteriorated
Hong et al. 2007 2001–2006 Shanghai S Deteriorated Delayed Deteriorated Present
Chang & Hsieh 2008 2002 Taiwan S Deteriorated Delayed Deteriorated
Kim & Yang 2008 2000 Taiwan S Improved Deteriorated
Kim & Yang 2009 1989–2000 Taiwan S Deteriorated Delayed Mixed
Du, Liu & Rhee 2009 1998–1999 Korea S Present
(Continues)
SIFAT AND MOHAMAD 19
TABLE 2 (Continued)
Study Year Data period Market S/FVolatilityspillover
Pricediscovery
Tradinginterference
Magneteffect
Panel A: Empirical Studies on Daily Price Limits
Hsieh, Kim, & Yang 2009 2000 Taiwan S Present
Wong, Chang, & Tu 2009 2004 Taiwan S Present
2010 Taiwan S Deteriorated Deteriorated
Wong, Liu, & Zeng 2009 2002 Shanghai S Deteriorated Present
Reiffen & Buyuksahin 2010 2003–2004 Commodity F Delayed Deteriorated
Tooma 2011 1997–2002 Egypt S Present
Omar 2012 2003–2009 Egypt S Deteriorated Delayed
Chou, Chou, Ko, &Chao
2012 1997–200 Taiwan S Delayed
Kim, Liu, & Yang 2013 1992–2000 China S Improved Improved Improved Absent
Dabbou 2013 2007 Tunisia S Deteriorated Improved Deteriorated Absent
Damoori & Zarei 2013 2007–2011 Iran S Deteriorated
Gomber et al. 2013 2009 Multiple S
Lin 2013 2010 Taiwan S Present
Thomadakis et al. 2014 1990–2013 Athens S Improved
Apergis 2014 2011 NYMEX F Deteriorated Delayed Deteriorated
Wang, Chong, & Chan 2014 1997–2009 China S Deteriorated Delayed
Guo, Chang, & Hung 2015 2004–2013 Taiwan S Mixed Mixed
Wan et al. 2015 2000–2011 Shanghai + Shenzen S Delayed Absent
Ye 2016 2003–2012 Shanghai+Shenzen S Deteriorated Delayed Deteriorated
Goldstein 2015 1988–1997 NYSE S Improved
Wong, Kong, & Li 2016 2016 China S Present
Brogaard & Roshak 2016 2010–2013 USA S Improved Delayed Improved
Danisoglu & Guner 2016 1995–2013 Istanbul S Deteriorated Delayed Deteriorated Absent
Brugler & Linton 2014 2011 London S Deteriorated Delayed Deteriorated
Deb, Kalev, &Marisetty
2016 2001–2005 Tokyo S Improved Improved
Aktas 2016 2008–2009 Istanbul S Deteriorated Improved Improved Present
Wang, Ding, & Hsin 2017 2003–2007 Taiwan S Mixed
Chu, Ko, Lee, & Yang 2017 1971–2015 Taiwan S Improved Improved Improved
Sifat & Mohamad 2018 2015–2017 Malaysia S Present
Panel B: Empirical Studies on Rule‐Based Trading Halts
Martens & Steenbeek 2001 1991–1995 Nikkei‐225 F Deteriorated Delayed Improved
Abad & Pascual 2007 2001–2006 Spain S Absent
Kim et al. 2008 1998–2001 Spain S Improved Improved Improved
Charoenwong et al. 2010 1999–2007 Thailand S Improved Improved Improved
Abad & Pascual 2007 2001–2006 Spain S Deteriorated Improved
Tooma 2011 1997–2002 Egypt S Present
Reboredo 2012 2001–2002 Spain S Improved Improved
(Continues)
20 SIFAT AND MOHAMAD
TABLE 2 (Continued)
Panel B: Empirical Studies on Rule‐Based Trading Halts
Gomber et al. 2013 2009 Multiple S Improved Delayed Deteriorated
Zimmerman 2014 2009–2012 Span & Germany S Improved Improved
Xu, Zhang, & Liu 2014 2009–2011 China S Mixed
Brugler & Linton 2014 2011 London S Deteriorated Deteriorated
Cui & Gozluklu 2016 2010–2013 USA S Deteriorated Delayed Deteriorated
Castro et al. 2017 2010–2012 Colombia S Improved Mixed Mixed
Clapham, Gomber,Haferkorn, & Panz
2017 2011–2015 Spain & Germany S Improved Delayed Deteriorated
Panel C: Empirical Studies on Discretion‐based Trading Halts (Stock and Futures)
Ferris 1992 1963–1987 NYSE + AMEX S Deteriorated Improved
Kabir 1994 1970–1988 London S Delayed
Lee 1995 1988 NYSE S Deteriorated Delayed Improved
Fong 1996 1988–1989 NYSE + AMEX S Deteriorated
Kryzanowski & Nemiroff 1998 1988–1990 Montreal S Deteriorated Delayed Improved
Wu 2000 1986–1993 Hong Kong S Deteriorated Delayed Improved
Corwin & Lipson 2000 1995–1996 NYSE S Deteriorated Delayed Improved
Kryzanowski & Nemiroff 2001 1988–1990 Montreal + Toronto S Improved
Christie, Corwin, & Harris 2002 1997–1998 NASDAQ S Deteriorated Improved
Chen 2003 1992 NYSE S Improved
Tan & Yeo 2003 1986–1995 Singapore S Deteriorated Delayed Improved
Engelen & Kabir 2006 1992–2000 Euronext S Improved Improved
Hauser et al. 2006 2001 Tel‐Aviv S Improved
Madura et al. 2006 1998 NASDAQ S Improved
Frino, Lecce, & Segara 2011 2005–2006 Sydney (ASX) S Deteriorated Delayed Deteriorated
Panel D: Empirical Studies on Market‐wide Trading Halts (Stock and Futures)
Kuhn et al. 1991 1989 NYSE S Deteriorated
Lauterbach & Ben‐Zion 1993 1987 Tel‐Aviv S Deteriorated
Goldstein & Kavajecz 2004 1997 NYSE S Improved Present
Basher et al. 2007 1993 Bangladesh S Mixed Improved
Farag 2013 2009 Egypt S Deteriorated Delayed
Wong, Kong, & Li 2016 2016 China S Present
Panel E: Experimental Studies
Ackert, Church, &Jayraman
2001 Simulation Present
Ehrenstein & Westerhoff 2006 Percolation model Modified Cont‐BouchaudModel
Improved Improved Improved
Lin & Chou 2011 Prospect theory Improved Improved Improved
Yeh & Yang 2010 Agent‐based artificial market Traders areboundedlyrational
Mixed Improved Mixed
(Continues)
SIFAT AND MOHAMAD 21
TABLE 2 (Continued)
Panel E: Experimental Studies
Learning behaviour through geneticprogramming
Heterogenous risk‐appetite
Draus & Van Achter 2012 Market run similar to bank runs Ideal
macroeconomic climate& tight limits
Improved Improved Improved
Brugler & Linton 2014 Placebo Counterfactual Improved Improved
Brogaard & Roshak 2015 Difference in Difference Design Deteriorated
Note. This table shows major hypotheses tested by various empirical studies (Panels A–D) as well as experimental studies undertaken since inception of circuitbreakers. The first four panels record works on price limits, rule‐based trading halts, discretionary trading halts, and market‐wide trading halts. The final panel
—though not comprehensive—is a selected choice of artificial, experimental, or model‐based approaches that the authors feel show promise in advancing thefield of research further. S and F indicate stock and futures markets, respectively.
22 SIFAT AND MOHAMAD
geographical idiosyncrasies emerge; for example, empiri-cal works on trading halt are popular in advanced mar-kets (North America and Europe), while emerging(mostly Asian) market studies are more common for pricelimits. In the upcoming subsections, we chronicle empir-ical works studying different hypotheses relevant tocircuit breakers' efficacy, which we present dividedaccording to the nature of markets and/or instrumentsunder study.
6.1 | Circuit breakers' ability to combatvolatility
6.1.1 | Stock markets
Earliest empirical works on circuit breakers' ability todeflate volatility followed after the implementation ofthe 1990 NYSE Rule 80A, which intended to add frictionto the interconnection between the cash and derivativesmarkets. Goldstein (2015) calls it an implicit attempt tostop the tail from wagging the dog by discouraging indexarbitrageurs from depressing stock prices in bullish orbearish markets. Kuhn, Kurserk, and Locke (1991) studythe mini‐crash of 1989 U.S. market and find that circuitbreakers were inefficient in reducing volatility in bothspot and futures markets. Santoni and Liu (1993) findmixed success of Rule 80A in diminishing volatility sinceits activation up until May 1991. Once the authors adjustfor ARCH effects in return series, unconditional vari-ances fall on the day trigger is hit, but in 1‐min returnanalysis, they find no evidence that the decline of vari-ance is associated with trigger hit. A year later, Lee,Ready, and Seguin (1994) find that volatility waxes onthe day after an individual stock is halted on NYSE.Studying the rule 80A's performance using 1‐min datafrom 1988 to 1997, Goldstein (2015) finds a small but sta-tistically significant success of the rule in reducing
volatility. Moreover, he finds rule 80A was more effectivein a rising market than a falling market. This finding waslater supported by Fong (1996) who used pseudo‐haltcontrol period of continuous trading for the same securityand with identical (but not same) net‐market returns.Ferris, Kumar, and Wolfe (1992) study the impact ofSEC suspensions on volatility in NYSE and AMEX andfind that volatility is unusually high for 20 days aftersuspension.
First non‐U.S. studies come from Chung (1991) andChen (1993), who study multi‐tiered circuit breakers inAsian stocks, where different price levels have differentlimits. The studies attempt to examine volatility levelsafter stocks re‐emerge from terminus and find, counterin-tuitively, that tighter limits do not result in less volatility.Though Chung controlled for time‐varying volatility inthe Taiwanese market, Chen's study did not. Comparingvolatility in disparate subsamples comprising terminusand pseudo‐terminus stocks from Korea, Kim and Rhee(1997) conclude that price limits do not reduce volatility;instead, the volatility spills over the subsequent tradingsessions. Similar findings were reported by Henke andVoronkova (2005) in the Polish market. In MontrealStock Exchange, Kryzanowski and Nemiroff (1998) findevidence of significant volatility spillover. In Hong Kong,Wu (1998) compares mandatory versus discretionary sus-pension mechanisms and finds volatility to be higher formandatory halts. The same was found in Singapore StockExchange by Tan and Yeo (2003), who further reportedelevated volatility after both good and bad news suspen-sions. Looking at alternative post‐halt reopening proce-dures in NASDAQ, Christie et al. (2002) find unusuallyhigh volatility upon resumption of trade even thoughthe halt coincided with dissemination of significantprice‐sensitive information.
Aradhyula and Ergün (2004) employ polynomial spec-ification and GARCH estimates to find in NYSE that
SIFAT AND MOHAMAD 23
volatility is higher in active circuit breaker periods, andvolatility is higher in a bearish market. Following Kimand Rhee's approach, studies on Istanbul Stock Exchangefind evidence against price limits' ability to reduce volatil-ity (Bildik & Gulay, 2006; Danışoğlu & Güner, 2016).
Building stock portfolios according to circuit breakerregimes through a cross‐sectional approach, Lee and Kim(1995) find in favour of proponents' view. Analysing datafrom Taiwan Stock Exchange, a market known for tightprice limit and frequent limit hits, Kim and Yang (2008)build subsamples for single‐trigger and multi‐trigger daysand find that volatility decreases in the days followingmultiple hits. Meanwhile, for Korean, Thai, and Egyptianmarkets, employing EGARCH and PARCH time‐varyingconditional variance models, Farag (2013) finds that wid-ening a tight price limit performs better in reducing vola-tility and that negative externalities impact volatilitymore than positive. Similarly, in China, Kim, Liu, andYang (2013) employ a before–after event study and findthat price limits moderately lessen transitory volatility.Using classical event study and regression analysis basedon data from 2007 to 2012, Tao, Yingying, and Jingyi(2017) find exaggeration of market volatility in post‐hitperiods in Chinese markets leading to volatility spillover.
A host of other studies focus on modifications in cir-cuit breaker regimes and the concomitant effect on vola-tility. Studying the newly introduced circuit breaker inGreece in 1992, Phylaktis, Kavussanos, and Manalis(1999) find no significant improvement in volatilityresults. Similar report came from Kim (2001), who exam-ined six price limit changes in Taiwan—ranging from2.5% to 7%—over a period of one decade. Berkman andLee's (2002) study on Korean price limits contradicts thisfinding, who additionally conclude that smaller stockssuffer more volatility on a weekly basis in a wider regime.
In VI varieties of circuit breakers, studying call auctionportion of Warsaw Stock Exchange in accordance withMadhavan's (1992) theoretical model, Henke andVoronkova (2005) find that price limits are suboptimal inreducing volatility. In the Spanish market, Abad andPascual (2007) study a single‐stock circuit breaker mecha-nism, where a price limit hit leads to periodic 5‐min callaction instead of a pure halt. They find that though thestocks behave in a highly volatile fashion immediately afterlimit‐hit, the volatility level regresses to themean after about30 min. Having chronicled the studies examining circuitbreakers' efficacy in holding back volatility in stockmarkets,we turn attention to derivatives in the next subsection.
6.1.2 | Derivatives markets
Findings in the derivatives markets are equally mixed.Although Ma et al. (1989) find in favour of volatility
reduction, Chen and Jeng (1996) find the opposite in cur-rency futures using data from 1985. Several studies findno effects of price limits in agricultural futures (Park,2000; Veld‐Merkoulova, 2003). Meanwhile, studyingcross‐listed Nikkei‐225 futures, Martens and Steenbeek(2001) find that access to an alternative trading mecha-nism reduces the effectiveness of circuit breakers inOsaka, though the effect is absent in Singapore.
Overall, circuit breakers' role in inducing (or aggra-vating) volatility spillover has, by now, been extensivelystudied. Bulk of the focus in literature has thus far beenon across‐security or across‐market spillover effects. Lessis known about cross‐sectional and intermarket migra-tion of volatility and its concomitant effect on liquidity.Also noticeable is a lack of bifurcation of sample sizesaccording to crisis and tranquil times. After all, from aregulatory perspective, circuit breakers' assistance inforestalling turbulence in stressed markets is the greatestjustification for the mechanism.
6.2 | Circuit breakers' ability to enhanceprice discovery
6.2.1 | Stock markets
Price discovery is an indicator of a market's efficiency andfairness. It is also argued to be a key justification for exis-tence of markets. Market administrators hope that in afunctional market, the forces of demand and supply willconverge in a way that produces a fair equilibrium, whichthen serves as a guide for resource allocation. Manybelieve that if the market is left to its own forces, it isexpected to generate a fair price signal. Circuit breakers,however, impede this process by design via imposingeither circumscribed price movements or by interrupting(at times, rejecting) trading actions altogether. Tracingback in history, in the early days of circuit breaker adop-tion, obviously the 1987 crash, and notably later after1997 Asian Financial crisis, concerns grew over maintain-ing pricing integrity in light of augmented local andglobal market volatility. Empirical studies examining cir-cuit breakers' ability to improve price discovery mostlyfind evidence against it for equity markets. Using aprojected standard deviation series with correctedheteroskedasticity as a proxy for asset price volatility,Chen (1993) finds in Taiwan Stock Exchange that serialcorrelations of equity returns have an inverse relationshipto price limits, suggesting inefficiency in price discoverydue to delay effect. This is supported by findings of Kimand Rhee (1997) for Tokyo.
Shen and Wang (1998) study the daily return autocor-relation, price limits, and trade volume in Taiwan Stockexchange and find limits to have a stronger impact on
24 SIFAT AND MOHAMAD
autocorrelation compared to volume, suggesting pricemay be trending after a limit is hit. Ryoo and Smith(2002) test the random walk hypothesis for Korean mar-ket and find that circuit breakers inhibit stocks' randomwalk path. Again, for Taiwan, Kim and Sweeney (2002)find that informed speculators postpone their tradingstrategies till the next trading day and thus delay priceefficiency. Comparing a limit‐hit sample against a controlsample from 1994 to 1995, Chan, Kim, and Rhee (2005)find a delay in price arrival for Kuala Lumpur StockExchange. Further evidence of trading halts inducingprice inefficiency come from Kabir (1994), Kryzanowskiand Nemiroff (1998), Martens and Steenbeek (2001),and Tan and Yeo (2003).
Lee et al. (1994) observe that a lack of multilateralprice coordination during halt periods exacerbates pricedisequilibrium in post‐halt sessions. Corwin and Lipson(2000) attribute the illiquidity risk of circuit breakers todelay of price discovery. In Taiwanese market, Cho,Russell, Tiao, and Tsay (2003) observe that pre‐halt pricesaccelerate towards the limits and conclude that pricelimits encourage price distortion rather than reducingprice uncertainty. These findings have been supportedby Henke and Voronkova (2005) for Warsaw StockExchange and Bildik and Gulay (2006) in Turkey.
Wong, Liu, and Zeng (2009b) study the transactionsdata on Shanghai Stock Exchange and find evidence ofprice discovery delay at the ceiling but not the floor. Exam-ining the unique “Sidecar” scheme operated by Koreansecurities market, Lee, Park, and Jordan (Lee, Park, &Jordan, 2009) find that trading halts in general and theSidecar scheme specifically are ineffective in curbingasymmetric information imbalances and delay arrival ofequilibrium prices. The authors also suggest themarket reg-ulator abandon the Sidecar project and follow NYSE'smechanism. Polwitoon (2011) examines the covariancecomponents of interday and intraday returns to identifythe relation between trading collar width and patterns ofoverreaction in stock exchanges of Thailand and Tokyoand finds evidence of increased structural price volatilityand overreaction, suggestive of impeded price discovery.
On the positive evidence front, Chen, Chen, andValerio (2003) contend that the efficacy of circuit brea-kers in enabling price discovery depends on the reasonfor the halt and nature of imminent news dissemination.They find, for NYSE, that halts perform well when theyspread important information or are triggered due toorder imbalance but generate noise when the news moti-vating the halt is trivial. Similar findings were reported byMadura, Richie, and Tucker (2006), who found tradinghalts to facilitate improved price discovery in NASDAQ.Hausser, Kedar‐Levy, Pilo, and Shurki (2006) investigateprice adjustment velocity in post‐halt periods in Tel Aviv
Stock Exchange, comparing public announcements ofsimilar content with or without halts. The authors findthat information circulates faster when halts are in effect,leading to faster price adjustment. This was supported byEngelen and Kabir (2006), who studied price discovery inEuronext Brussels.
Using introduction of Arrowhead trading system as aregulatory event in Tokyo Stock Exchange, Kubota andTakehara (2015) find price discovery to improve for largerstocks, but the improvement was less pronounced forsmaller cap stocks. Zimmerman (2014) studies EuropeanVIs and based on 1,800 XETRA event sample size, pricediscovery is improved by nearly 36%. Zimmerman furthersuggests regulators to consider the circuit breaker's abilityto ease price discovery as a major determinant in choos-ing the preferred type and parameters of circuit breakers.In an interesting investigation for nine Asian exchangesthat employ circuit breakers, Qiao, Chiang, and Tan(2014) employ Kalman filter method to test herdingand, in the process, find that stock return dispersionsdiminish when markets are stressed and nearing circuitbreaker hits for Japan, South Korea, and Thailand.
Surprisingly, empirical works from Africa yield resultsfavourable for circuit breaker mechanisms. For instance,for the Tunisian Stock Exchange, Dabbou (2013) studiesonly downward price limits and reports desirable effects,contrary to majority findings. Ohuche and Ikoku (2014)find evidence of price limits' effectiveness in curbingvolatility in Nigerian equity markets during the globalfinancial crisis (2007–2010), with symmetric bandsoutperforming asymmetric ones. Moreover, they findequity returns volatility may predict volume volatilityand suggest using both measures to determine the opti-mal price limit.
Although not explicitly connected to price discoveryhypothesis, in an interesting approach involving event‐study methodology, Clapham and Zimmermann (2016)study scheduled midday auctions using 2009–2013 tickdata from multiple‐listed German blue‐chip stocks anddiscover a persistent lead–lag relationship not only dur-ing scheduled midday auctions but also in succeedingcontinuous trade sessions. Moreover, they report thattrade activity on alternate exchanges dramaticallydecrease when the leading market switches to a call auc-tion. Although this study does not study circuit breakermechanism per se, the intuition of the authors' findingscan be related to circuit breakers in fragmented marketswhere only one trading venue is in a VI.
6.2.2 | Derivatives markets
The findings for the futures markets, however, have beenmixed. Kao and Ma (1992) study the time series
SIFAT AND MOHAMAD 25
properties of British Pound, Canadian Dollar, DeutscheMark, and Swiss Franc and finds that price limits delayprice adjustment and hence create artificial price depen-dence in the short term. Similar findings were reportedfor currency futures by Chen and Jeng (1996) and Chen(1998). Veld‐Merkoulova (2003) uses multiple models ofchanging volatility to confirm a price delay discoveryhypothesis instead of facilitating it. The study, however,warns against generalizing the findings against stocks orother futures due to properties intrinsic to futures (e.g.,volatility clustering). Additionally, Fernandes and Rocha(2006) study three agricultural futures listed in Braziland find price discovery to be delayed for upper limits.Reiffen and Buyuksahin (2010) study privately imposedprice limits on lean hogs, live cattle, and pork bellyfutures on the Chicago Mercantile Exchange and findthat when options markets exist, circuit breakers' effecton price discovery is nugatory.
In summarizing the findings of this subsection, it isworth remembering that exchanges and regulators typi-cally try to encourage price continuity, which is a markof a liquid market where bid spreads and differencebetween offer prices is diminutive. Thus, naturallyexchanges value price continuity while not constrictingvolatility, combination of which is expected to sponsorstrong price discovery. Walking this tightrope, however,is tricky, especially in stressful market periods. The find-ings of empirical works, by and large, indicate failure ofdifferent circuit breaker mechanisms in enhancing pricediscovery, especially for derivatives. Within the domainof delayed price discovery, interestingly, hardly anyattempt has been made in comparison of different mech-anisms in promoting price discovery. This is especiallypuzzling in light of more datasets being readily availablein recent times. Moreover, considering VI's emergenceas a strong alternative to traditional price limits andtrading halts, barring a handful of authors (e.g., Gomber,Claphorn, and Zimmermann) very little interest is seen inthis mechanism's performance.
6.3 | Circuit breakers' role in tradinginterference
Circuit breaker mechanisms arm exchanges and regula-tors with the capacity to dictate the process of pricequotation. This can be done through suspending auto-matic matching of buy and sell orders for an instrument(e.g., Bursa Malaysia, Oslo Stock Exchange, andTadawul), discontinuing regular trade and switching toan auction phase (e.g., LSE), or halting a security's orwhole market's trading altogether (as seen in markethalts in numerous countries). These actions all interferewith traders' pre‐existing trading strategies. Whether such
interference is healthy, however, is up for debate. In thissubsection, we recount empirical papers striving toanswer this question.
Vast majority of the empirical works corroborate thatcircuit breakers interfere with investors' trading strategies.Abnormally high level of volume is reported across variousstock exchanges in post‐trigger trades for equities (Chanet al., 2005; Ferris et al., 1992; Kryzanowski & Nemiroff,1998; Lauterbach&Ben‐Zion, 1993;Wang, Chong, & Chan,2014). Though similar findings exist in futures market(Martens & Steenbeek, 2001; Veld‐Merkoulova, 2003), theconsequences are less severe when investors have an optionto migrate to alternative venues or have access to options.Moreover, some studies examined the nature of interfer-ence to the trading process vis‐a‐vis liquidity supply andrisk of information asymmetry. Corwin and Lipson (2000)looked at the order flow around trading halts in NYSE.They found that order cancellations and submissions werehigh upon trade suspension and resumption. Besides, limitorders active upon trade resumption were mainly thoseplaced during the halt, and even those orders were consid-erably distant from the market price, indicating informedtraders' reluctance to provide liquidity when markets arestressed. Kryzanowski and Nemiroff (2001) found bid–askbounce to be higher during and around halts in Torontoand Montreal Stock Exchanges. In NASDAQ, Christieet al. (2002) also find spreads to be higher with elevated vol-umes during intraday halts. Kim and Sweeney (2002) foundinformed traders to alter their trading strategies when fac-ing impending price limits. Lee and Chou (2004) find upperlimits in Taiwan to be more susceptible to informationasymmetry risk compared with stocks with large priceincreases that fail to hit the trigger. Chan et al., 2005 findin Kuala Lumpur Stock Exchange that price limits delaythe arrival of informed traders, aggravate order imbalance,and fail to improve order imbalance. Kim and Yang(2008) study consecutive limit hits in Taiwan and concludethat though successive limit hits diminish volatility, infor-mation asymmetry does not improve. Similar findings werereported by Abad and Pascual (2007) from Spanish StockExchange (SSE), where rule‐based VI fails to improve infor-mation asymmetry and liquidity upon resumption andtakes up to 90 min before mean reversion occurs. Moststrikingly, during the October 1997 market‐wide halt atNYSE, Goldstein and Kavajecz (2004) found that the costof providing liquidity via limit orders was so high thattraders withdrew from the order book and migrated to thefloor market.
To sum up, circuit breakers overall fare poorly inempirical literature in connection with trading interfer-ence. Regarding this, it is worth keeping in mind that afinancial market is expected to remain a “going concern.”One of its core functions is to exist, especially on eventful
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and news‐heavy days. Not only do circuit breakers inter-fere with this first principle, they hinder investors' tradingplans. Worse yet, occasionally they render the market“non‐existent.” Papers surveyed in this section indicatethat the trade‐offs are inadequate. As investors are unableto rearrange their positions even through buying orselling at otherwise unfavourable quotes, both tradingactivities and liquidity suffer interference.
6.4 | Circuit breakers' magnetic pull
A theorized ex ante by‐product of a circuit breaker mech-anism is its potential for inviting accelerated buy or sellorders in volatile sessions by traders fearful of beingtrapped in the event of trade suspension. Thus, in volatilesessions, quote proximity to threshold value is matchedby intensified trading volume and order flow such thatthe existence of the threshold helps achieve the threshold.Miller (1989) was the first to argue that price limits maybe self‐fulfilling if investors accelerate their trading activ-ities for fear of being locked into positions near a triggerpoint. Greenwald and Stein (1991) hinted at a possible“feeding on itself” effect of price limits in a stressful mar-ket condition whereby a substantial price fall would feedon itself and precipitate market crash. Gerety andMulherin (1992) proposed the idea of a fear threshold,whereby after a price change of a certain percentage,traders would be afraid of trading barriers and have ahigher incentive to leave the market compared to a bar-rier‐free market. Testing inadvertently for magnet effect,they observe investors' desire to trade prior to daily mar-ket closings, in line with the magnet effect. Studies testingdirectly for this effect generally used relatively small datasets and reach various conclusions. In futures markets,Arak and Cook (1997), Berkman and Steenbeek (1998),and Hall and Kofman (2001) find no magnet effect,whereas Holder, Ma, and Mallett (2002) find contrari-wise. Results from stock markets also conflict: Cho et al.(2003) and Nath (2003) find support for the magnet effect,but they cannot explain theoretically why it occurs onlywhen the price approaches the upper limit. Instead ofstudying all transactions prior to the limits, Du, Liu,and Rhee (2009) and Wong, Chang, and Tu (2009a) focuson the transactions before the actual limit hits, which cre-ates a selection bias in support of the magnet effect. Ifthere were no magnet effect, a priori, the price wouldnot accelerate to reach the limit when it approaches thislimit. This observation, which implies the rejection ofthe magnet effect, cannot appear in these samples,because the price limit never arrives. Abad and Pascual(2007) find no magnet effect in the SSE where limitstrigger 5‐min trading halts, followed by continuous trad-ing with revised price limits. In the SSE, investors know
that they may trade after limit hits and trading halts,which hardly creates incentive for them to acceleratetheir trades.
As for behavioural investors, Arak and Cook (1997)point out that trend‐hunters' momentum tracking activi-ties are responsible for the magnet effect. As the pricerushes towards the legal limit, such traders are fearfulof getting trapped out of the trend. Thus, the buy/sellpressure condenses, and the upper/lower limit is reachedsooner and hence the acceleration towards magnet effect.
In Malaysian market, Chan et al. (2005) find evidenceof magnet effect in Kuala Lumpur Stock Exchange usingtransactions data from January 1995 to December 1996. Aweak form of magnet effect was documented by Sifat andMohamad (2018) for the same exchange. Using a logitmodel, Hsieh, Kim, and Yang (2009) find evidence ofthe magnet effect of price limits, with important regula-tory implications. They find that the price volatility dropssignificantly following consecutive limit hits, supportingthe volatility hypothesis. They also find that the longerthe duration of consecutive limit hits, the more the vola-tility is reduced. This indicates that price limits counteroverreaction only when prices hit the limit consecutively.In Australian Stock Exchange (ASX), Frino, Lecce, andSegara (2011) find that trading volume and volatility areabnormally high immediately after a price limit isreached. Other recent studies finding magnet effectinclude Tooma (2011) in Egypt, Lin (2013) in Taiwan,Wong, Kong, and Li (2016) in China, and Aktas (2016)in Turkey, while Dabbou (2013), Wan et al. (2015) findthe opposite in Tunisian and Chinese markets. Wangand Hsin (2013), meanwhile, find mild and asymmetricmagnet effect using intraday data from Taiwan. Mostrecently, Curran and Mollica (2018) employ logitapproach on an intraday TAQ dataset and find strong evi-dence of magnet effect in Shanghai and Hong Kong mar-kets. Focusing on a market liberalization angle, theauthors link stronger incidence of magnet effect to globalinjection of speculative capital via Hong Kong. They alsoidentify firms experiencing high foreign capital inflow asthe likeliest to incur magnet effect.
All in all, the question of whether circuit breakersinduce a magnet effect yields mixed results in empiricalpapers. In the early days of circuit breaker research, mag-net effect hypothesis appears to have garnered less atten-tion compared with ex post hypotheses. Greateravailability of higher frequency datasets, improved com-puting powers, and easier access to intraday trade andquote data has enabled a revival in interest in magneteffect studies in recent years, particularly from East Asianvenues. Despite the proliferation of empirical papers,clarity remains elusive, as empirical results still areconflicting.
SIFAT AND MOHAMAD 27
6.5 | Less explored phenomena: Spilloverand coordination
In contrast to the voluminous empirical works on volatil-ity spillover effects of circuit breakers across trading daysand even into other securities, very little attention hasbeen paid to potential spillover of volatility, liquidity, orinterference across venues. Fabozzi and Ma (1988) werethe first to empirically demonstrate bidirectional spilloverof price volatility from the stocks halted on the NYSE toover‐the‐counter market. Later, in the context of spot ver-sus futures price discovery, Morris (1990) hinted at possi-bility of deterioration in market quality triggered byhigher demand for liquidity and corresponding wild pricefluctuations if there is a discrepancy between circuitbreaker activation across venues. The seminal work bySubrahmanyam (1994) can be credited as being the firstto formally suggest that in stressful periods, impendingcircuit breaker trigger in a leading (generally more liquid,halted or halt‐imminent) market is likely to spur greatervolatility in lagging (less liquid, continuous trading alive)venue(s) due to higher demand for liquidity. As such, hewas one of the first to advocate cross‐exchange circuitbreaker coordination. In recent times, Chakrabarti(2011) presents evidence of volatility spillover because ofuncoordinated circuit breaker mechanisms. The authors,however, remark that continuous trading in secondaryexchanges, whereas primary exchange' halt (NYSE intheir case) is active can be informationally more efficientdespite the high bid–ask spreads in the alternativevenues. Conversely, examining Deutsche Boerse datafrom 2009, Gomber, Haferkorn, Lutat, and Zimmermann(2013) find no evidence of intermarket volume migrationand, by extension, volatility spillover. This finding shouldbe treated with caution, however, especially because mostEuropean markets in recent times exhibit far greater frag-mentation and wide varieties of active circuit breakerscompared to Gomber et al.'s data period. More recently,re‐examining the same hypotheses, Clapham, Gomber,and Panz (2017) too find no evidence of volatilityspillover upon analysing 2,337 VIs in Deutsche Boersefrom 2011 to 2015.
7 | CHALLENGES IN CIRCUITBREAKER RESEARCH
7.1 | Distinguishing between alternativeexplanations
From a study design standpoint, reliable estimation of thenet effect of circuit breakers on market quality can bedifficult. The crux of the issue is distinguishing betweennumerous plausible alternative reasons for price change.
For example, diminished transitory volatility afterenacting a narrow price limit band is not necessarily avindication. That decrease could be due to seasonality,reduced noise trade due to overall market sentiment,new technology allowing greater access to liquidity, ormyriad different reasons that are almost impossible todetermine. This difficulty requires making bold assump-tions about the counterfactual, that is, how volatilitycould have been in absence of the circuit breaker. Con-versely, the counterfactual dilemma of how trade wouldbe in a non‐circuit breaker market suffers equally fromthe necessity of bold assumptions, though it is an areamarkedly considerably less explored and perhaps moreinteresting. Macroeconomic factors such as inflation andcapital flow policy can be possible explanations for a longtime series regression performed to measure the effective-ness of the hypothetical price limit. The period of sam-pling is of equal importance as well. At any rate, thishypothetical exercise considers total volatility and doesnot extricate its transitory element. Bypassing this predic-ament by decomposing volatility into fundamental andtransitory presupposes, the two are mutually exclusive.Keeping aside the technically daunting task of precisedecomposition, academics have convincingly argued thetwo can be interrelated. For instance, in a high funda-mental volatility situation, dealers may retreat partiallyor fully from the market in fear of overexposure to inven-tory risk.
As it is typical of classical event studies, the earliestdesigners of empirical studies' findings suffered fromquestionable inferences drawn on transitory volatility asan effect of circuit breaker placements. Moreover, statisti-cal power of the studies was limited due to limited size ofsample. This problem has been partially overcome in the2000s due to availability of larger data sets spanninggreater time window, enabling a study design wherebyvolatility factors previous unaccounted will tend to zero.This work‐around using large data samples too can bequestioned on several grounds. First, what constitutes alarge enough sample size, given limited theoreticalunderpinning or precedence, is debatable. In circuitbreaker context, a large sample size could be (a) a collec-tion of multiple rule mechanisms (e.g., mixture of haltsand limits), (b) all securities within a jurisdiction affectedby the same rule, (c) all time periods affected by the samerule, or (d) all cross‐listed securities across exchanges andjurisdictions affected by any/all rules. Even with this sam-ple size, event study remains a strong candidate for studydesign involving multiple circuit breaker regimens at dif-ferent periods at different markets so that a before–afteranalysis will be less susceptible to omitted volatility fac-tors. Existing empirical work has left many of these issuesunaddressed, especially with regard to cross‐exchange
28 SIFAT AND MOHAMAD
tests and market‐wide circuit breakers. The latter is moreexcusable on account of its rarity in a given jurisdictionand remains unlikely to be remedied soon and can bene-fit from agent‐based synthetic modelling instead.
The asymmetric nature of different species of circuitbreaker triggers, which—by design—is highly dependenton mechanism preferred by the exchange and/or regula-tor, poses challenges in reaching a unified understandingto help design an all‐purpose solution to the circuitbreaker conundrum. For instance, market‐wide tradinghalts are rarely triggered and thus can be considered ascandidates for specialized methodological approaches tounderstand them better, for example, applications ofextreme value theory and black swan event studies.Conversely, price limits are considerably less rare, asevidenced in East Asian exchanges, whereas VIs aremuch more frequent occurrences, as seen in Europeanvenues (Clapham et al., 2017).
Despite improved precision of before–after volatilitycomparisons with each extra observation, it is susceptibleto contamination from extraneous factors: for example,variables tied to the business cycle or macroeconomictrends. Hence, the rarity factor means the informativecontent will remain minutely incremental, especially ifthe factors change very slowly over protracted periods.Sampling more frequently to generate higher samplecount through a specific interval compounds the problembecause despite enabling a more accurate estimation ofamalgamated effects of circuit breakers on volatility,demarcating these effects is not so straightforward. How-ever, examining some effects of circuit breaker on themarket will be easier if prices frequently trigger it. Thisexplains why many of the post‐2000 studies focused oncontrasting market condition around the terminus andproduce more powerful statistical results compared withthe 1990s literature due to more trigger hits. Many ofthe post‐2000 studies modelled a substitute expositioncorrelated to triggering of the circuit breaker and anotherindependent of it: Typically, through examining volatilitybefore and after comparably sized intraday price varia-tions before the circuit breaker regime. Examples includeTooma (2011) and Cho et al. (2003).
7.2 | Experimental studies
Clear majority of the studies surveyed neglected to isolatethe net effect of circuit breakers on volatility from theeffects of many other factors. Some attempted to circum-vent this counterfactual problem by employing controlperiods of pseudo‐events, which, a priori, resemble thosesurrounding price limits (Kim & Rhee, 1997; Tooma,2011). However, these events are likelier to be associatedwith significant information and uncertainty compared
with control sample. Thus, if conclusions are to be drawnfrom comparing events, pseudo hit scenarios are less sig-nificant. A way to overcome this may be doing experi-mental studies. The experimental approach provides anopportunity to make direct comparisons of alternativetrading arrangements with or without price limits undercontrolled environments. They should provide a superiorangle for investigating behaviour of traders in presence(and/or absence) of circuit breakers under the same con-trolled environment. The problem with this approach,however, is that experimental markets provide anoversimplified caricature of real markets that are basedon theoretical frameworks they are designed to test. Fur-thermore, invariably the subjects of these studies are notprofessional/informed traders. Therefore, opponentsmay have some ground to discredit the results, just as reg-ulators baulk at these academic findings with a liberalgrain of sceptic salt. Nonetheless, experimental studiesremain one of the best alternatives for overcoming pres-ent limitations.
7.3 | Firm characteristics andmanipulation
Empirical works linking circuit breaker topics to theunderlying firm characteristics are surprisingly scarce.Exceptions include Kim and Limpaphayom (2000), wholooked into attributes of firms that frequently hit pricelimits. They report that less actively traded, volatile, andlow market‐cap firms are most prone to limit hits. Focus-ing on liquidity, Chen, Kim, and Rui (2005b) found thatilliquid stocks stand to enjoy greater liquidity whenafforded a wider price limit.
Kim and Park (2010) observe that although marketregulators are reluctant to admit market manipulationas a raison d'etre for price limits, it is treated as a receivedwisdom among exchange officials and market actors.They propose a model illustrating the opportunity costof price inefficiency (by employing circuit breakers):deterrence of manipulation, predicting that when fear ofmanipulation is high, price limits will be imposed byexchanges. This is supplemented by an analysis of 43markets vis‐à‐vis the corresponding countries' level ofcorruption and law enforcement quality. Both served asa proxy for market manipulation. Not surprisingly, themodel predicts higher probability of exchanges in corruptand low‐quality law enforcement countries to adopt pricelimits. This is interesting considering McDonald andMichayluk's (2003) suggestion that price limits them-selves can be a means for price manipulation, citing evi-dence of suspicious activities near the circuit breakertrigger points in France.
SIFAT AND MOHAMAD 29
7.4 | High frequency trading and volumesynchronized probability of informedtrading
High‐frequency trading, a phenomenon absent whencircuit breakers were conceptualized, introduces furthercomplexity into circuit breaker discourse. The effectshigh‐frequency trading on market quality are still underinvestigation, with conflicting early reports of pros andcons. Easley, López de Prado, and O'Hara (2014) intro-duce the idea of volume synchronized probability ofinformed trading (VPIN) to measure short‐term toxicity‐induced volatility and suggest it as an early warningsystem for regulators before triggering trading halts orother actions to prevent crashes. VPIN's efficacy is yet tobe established, however. In fact, several studies reporthigh likelihood of false positives for VPINs, limited pre-ventive capacity, difficulty in calculation in fragmentedor single trading platforms (Chakrabarty, Pascual, &Shkilko, 2015), questionable statistical power (O'Hara,2015), and unfavourable findings from several empiricalattempts (Cheung, Chou, & Lei, 2015; Phuensane & Wil-liams, 2016; Pöppe, Moos, & Schiereck, 2016). In a similarvein, Bethel, Leinweber, Ruebel, and Wu (2011) suggest ayellow‐light signal approach in real time to allow regula-tors to decelerate the trading pace rather than suspendtrading. Examining a sample of 45 Spanish stocks, Abad,Massot, and Pascual (2017) investigate the level of liquid-ity in high‐VPIN scenarios via cumulative distributionfunction. They conceptualize VPIN‐related toxicityperiods by focusing on 99% CDF VPIN threshold. Theauthors conclude that VPIN has limited ability inpredicting truly toxic events.
Other concerns surrounding VPIN include the risk ofreinforcing magnet effect on top of volatility and worsen-ing liquidity. For instance, in a Subrahmanyam‐esquemarket, VPIN approaching a limit means an impendinghalt. Thus, some market actors trade prematurely, whichintensifies order imbalance—pushing VPIN towards thelimit. Additionally, liquid investors may recoil from trad-ing activities because the ex‐ante impact of limits/haltsamplify the spread of small orders, further aggravatingshort term volatility.
7.5 | Lack of data
Another challenge of circuit breaker studies for manyacademics is the paucity of data. The extremely expensivenature of higher frequency and granular historical datacan impede historical back‐testing to test efficacy of pricelimits when they were first implemented—especially foremerging and frontier markets, where archival practicesare often suboptimal. The problem rings truer for
specialized studies requiring access to the market micro-structure variables. The availability quandary has recededsomewhat in recent times, though that is mostly forrecent data. Granular data sets such as limit order booksand transaction data are becoming more available—par-ticularly for advanced markets. In frontier and emergingmarkets, noncooperation of regulatory authorities and/or exchanges can pose a problem too. Lastly, the prolifer-ation of high‐speed algorithmic trading provides anotherfertile ground for empirical research into circuit brea-kers—both for human traders and algorithms. This pros-pect too suffers from data availability challenges due topotential conflict of interest as it is contingent upon coop-eration of a willing participant (i.e., a High FrequencyTrading firm or broker).
8 | FUTURE RESEARCH AVENUES
8.1 | Extension of conventional approach
The crux of the limitations of the empirical studies thusfar can be attributed to methodological constraints ema-nating from counterfactual dilemma and difficulty indecomposing volatility into fundamental and transitory.Besides, because lack of data and theoretical frameworkstill plague the field, most researchers are limited toanalysis of effectiveness of one type of circuit breaker inone market alone. As such, vast majority of studies focuson single mechanism within a single market. Worthyexception are Lim and Brooks (2009), who compare theefficiencies of Chinese, Korean, and Taiwanese marketsusing bicorrelation test statistics. Also, Wong et al.(2016) test to see if the magnet effect of circuit breakersin Chinese market interacts with price limits. Xu, Zhang,and Liu (2014) compare interday and intraday circuitbreakers' effect on short‐term market reaction. Moreover,Zimmermann (2014) analyses circuit breakers on theGerman market and uses scheduled intraday auction ascontrol group. In a rare comparative study, Claphamet al. (2017) examine relative efficiencies of circuit brea-kers across multiple European venues in curbing excessvolatility and preserving liquidity. With a core focus onVI, the authors document superior performance of VIscheme in warding off volatility, which does, however,incur a trade‐off of suppressed liquidity. Based on theirfindings, the authors advocate tighter price brackets andshorter interruption windows. Meanwhile, in the UnitedStates, Goldstein (2015) examines volatility transmissionacross American markets in response to NYSE Rule80A. Gomber, Clapham, Haferkorn, Panz, and Jentsch(2017) attempt to test if post‐halt volatility improves inhome and satellite markets and find that though volatilitysubsides, it comes at a cost of delayed price discovery and
30 SIFAT AND MOHAMAD
higher spreads in the satellite market. Nonetheless, stud-ies examining multiple mechanisms within the samemarket or across markets are sparse, which would haveshed more insight into comparative efficacies of differentcircuit breakers.
Very few attempts have been made to connect circuitbreakers to other subdisciplines of financial economics,baring examples of Chou (1997), and Wei and Chiang(2004) who examine return and risk in stressful marketenvironments, Chou, Li, Lin, and Wang (2006) who studyrisk of portfolios subject to price limits and nonsynchro-nous trading, and Chou, Chou, Ko, and Chao (2013)who study micro and macroeconomic factors affectedduration of price limits.
Harel and Harpaz's (2006) Bayesian model attemptedto examine price discovery process under circuit breakerand non‐circuit breaker scenarios. Draus and Van Achter(2012) also design a model to test circuit breakers' role inpreventing a bank‐run‐esque situation and promotion ofsocial welfare. Brugler and Linton (2014) experiment withplacebo scenarios, subjecting the stocks to artificial limitsto compare different price limits' successes. However,Coursey and Dyl's (1990) experimental model that testedthe same market with price limits, trading halts, andwithout circuit breakers remains untested andunreplicated till now This thinly traded path should leadto interesting insights provided the study accounts formultiple new mechanisms in play today, such as volatilitycall auctions, dynamic and static limits, greater heteroge-neity of traders such including high‐frequencyalgorithms.
Most empirical studies since 1999 heeded to Harris's(1997) suggestion that a classical event study promisesthe best design for understanding before–after effects ofcircuit breakers. As such, empirical efforts to have beenlargely monodimensional since then. Notable exceptionsinclude efforts to study the economic traits of firms thatfrequently hit the price limit (Chou et al., 2013; Kim &Limpaphayom, 2000), investor sentiments in stressfulmarket scenarios such as price limits (Ackert et al.,2001; Guo, Chang, & Hung, 2017), order aggression(Wang et al., 2017), price limit performances of liquidand illiquid stocks (Chen, Kim, & Rui, 2005b; Guoet al., 2017), information competitiveness under pricelimits (Hsieh et al., 2009), manipulation‐based explana-tions for circuit breakers (Deb, Kalev, & Marisetty, 2013;Kim & Park, 2010; McDonald & Michayluk, 2003), pricelimits' effect IPOs (Thomadakis, Gounopoulos, Nounis,& Merikas, 2016), and informational advantages ofwealthy versus retail investors in stressful marketsituations ala circuit breaker scenarios (Li, Geng,Subrahmanyam, & Yu, 2017). Ryoo and Smith (2002)examine whether price limits disturb the random walk
path of stock prices in Korea. Abad et al., 2017 explorethe possibility of using VPIN as triggers for single‐stockcircuit breakers, based on efficient advance indicationhypothesis of Easley et al. (2014).
High frequency (millisecond) data were used toApergis (2014) in future markets and Aktas (2016) instock market. Gwilym and Ebrahim (2013) examinewhether position limits can deter manipulations infutures markets. Others experimented with difference indifference design (Brogaard & Roshak, 2015) that exploitsstaggered introduction of limits to address previous con-cerns of omitted volatility variables. Draus and VanAchter (2012) design a model to test efficacy of circuitbreakers in preventing market runs (similar to bank runs)and its ability to promote social welfare. Though themodel finds that a tightly set circuit breaker is able toachieve its objectives, it is conditional upon strict, nearlyoptimal macroeconomic climate. In the absence of anideal economic condition, the circuit breaker can reducesocial welfare by impeding socially desirable trades.
From statistical and methodological perspectives,Guo, Liang, and Fang (2010) test if price limits affectautocorrelation of stock return series via Monte Carloexperiment and suggest that some seminal works infinancial economics, such as empirical works based onLo and MacKinlay's (1988) variance ratio could be biasedif the stock price time series includes prices subject toprice limits. Nobanee and Al‐Hilu (2013) employ anextreme value approach to inspect circuit breakers'impact on volatility in Thai market. Errais and Bahri(2016) examine whether standard deviation is a goodmeasure of volatility in markets with price limits usingAfrican samples.
Zhang, Ping, Zhu, Li, and Xiong (2016) use an artifi-cial stock market model to measure the effect of circuitbreakers, whereas Xiong, Nan, Yang, and Yongjie (2015)employ an agent‐based model to do the same. Lin(2015) proposes a theoretical framework for capturing tailbehaviour of volatility under price limits and demon-strates its effectiveness in measuring dynamic spillovereffects. Deb, Kalev, and Marisetty (2016) propose a pro-pensity score matching technique to capture volatilitytransmission, information asymmetry, and decomposi-tion of transitory volatility from overall volatility. Thesame technique was used by Guo et al. (2017) to studycircuit breakers' impact on liquidity of related stocks inTaiwan across industries. Wan et al. (2015) examine thestatistical properties and pre‐hit dynamics of triggeredstocks in Chinese markets. Operating from a behaviouralfinance paradigm, Lin and Chou (2011) study price limitsfrom a Prospect Theory perspective and show that pricelimits are more effective when traders are loss‐averseand less rational. Other innovative approaches include
SIFAT AND MOHAMAD 31
test for the ideal futures margin with price limits for sin-gle‐stock futures using Longin and Censoring methods(Chen, Chou, Fung, & Tse, 2017). They find 13.5% to beideal for Taiwanese futures to avoid default risk.
8.2 | Alternative and preventivemechanisms
Circuit breakers seem to hit the headlines followingmajor crashes, after which a flurry of academic studiessprout. Unsurprisingly, studies examining the root causeof 2010 flash crash and its aftermath have surfaced since2011. Efforts are ongoing to suggest remedial measures toprevent its recurrence. One such suggestion is the coordi-nation of circuit breakers across venues. For example,NASDAQ and NYSE underwent a joint 10‐min pauseexercise in 2010 following a 10% dynamic price movewithin 5 min. Though the SEC chair Mary Schapiro sug-gested that stymying excess volatility would help regaininvestors' and companies' confidence and thereby attractnew trading interest, coordination of circuit breakers inthe U.S. market remains contentious due to mountingfragmentation of trading and growing role (and politicalclout) of high‐frequency trading agents.2 Besides, effortsto harmonize circuit breakers across many platformshas provided unclear evidence so far regarding its feasi-bility and economic advantages. Increasing tick sizeshas been suggested as an alternative to circuit breakers.However, this is highly likely to result in greater bid–ask spread, leading to higher trading costs and dimin-ished investment, therefore, less economic activity. Forexample, the rollback of the tick‐size reduction employedin European markets in 2009 (BATS and Chi‐X inclusive)is reported to have threatened a cumulative loss of €60bnover 10 years. Moreover, given the high‐frequency tradingenvironment of today, surveillance of “rogue” algorithmthat may threaten flash crash has been suggested. Thoughit is unlikely that many stakeholders will assent to full‐scale divulsion of their trading strategies by sharing thealgorithm, a reasonable test of robustness and resilienceof those algorithm with an audit trail should not beunwieldy. Establishing a maximum spread bandwidthcan be an effective regulation also, though it is likelyhigh‐frequency trading houses would argue against itbecause they stand to profit by providing liquidity instressful market situations. The effect of exchange harmo-nization on liquidity and market stability is still a verynew area of research. With the scant evidence at hand,it is difficult to argue that coordinated circuit breakerswill outperform individual circuit breakers. Another
2Market fragmentation in the United States and Europe have grown dueto REG NMS (2005) and MiFID I and II respectively.
alternative can be introducing randomness in the haltand auction process, inspired by Subrahmanyam's(1997) suggestion for stymying the magnet effect. Com-bined with a higher resting period between trading ses-sions, it is possible that decelerating the trading pacecould level the playing field between the computers andordinary traders. Notwithstanding high‐frequency firms'opposition to such manoeuvre, it will be interesting tosee model‐based studies examining scenarios of deceler-ated trading versus the status quo.
Although most exchanges experimented with somemeasures of fine‐tuning or tweaking the predominant cir-cuit breakers such as price limits, trading halts and theirsubvariants, innovative approaches are not uncommoneither. For example, KRX (Korea Exchange) introduceda “sidecar” scheme in 2001 for KOSPI futures contract,which upon moving ±5% for over 1‐min triggers a sus-pension for 5 min. Speed bumps have been tried in U.S.futures markets which slow down the trading activitywithout fully shutting trade down. Speed bumps operatea more constricted price channel compared with tradi-tional circuit breakers and last much less longer, forexample, opening price limit for index futures at CME(Chicago Mercantile Exchange) and NYFE (New YorkFutures Exchange) and interim lower price limit for indexfutures at Kansas City Board of Trade. The Canadian reg-ulator IIROC experimented with freeze parameters, pres-ently active in TSX Alpha, which is very similar to pricelimits. In this mechanism, unusually high or low pricesin the opening auction cause a delayed opening, and ifentered during continuous trades, order is rejected. Moreinnovative approaches include Short Sales restrictions(e.g., SEC in USA's short sale circuit breaker in 2010)and UMA (Unusual Market Activity) queries (as in BursaMalaysia) upon detection of irregular price or volume inan asset. If a UMA alert is triggered, the issuing firmhas to publicly disseminate information to clarify matters.A Market Alert, on the other hand, acts as an announce-ment to traders regarding recent developments in a listedfirm and urges traders to tread cautiously in affectedsecurity(ies). All in all, the changes have been gradualand incremental. For future researchers, the topic ofinnovation in circuit breakers, however, remains a largelyuncharted territory.
8.3 | Studies from regulatory angle
Pros and cons of circuit breakers, after several decades ofpractice and research, are relatively well understoodtoday. Yet the debate on whether the practice is war-ranted endures, abutted partially by contradictory andoften unclear findings from academia. Drawing conclu-sions likewise from regulatory investigations is difficult
32 SIFAT AND MOHAMAD
as very few such findings are publicized. Nonetheless,from the regulatory standpoint, it is understandable thatperennial public scrutiny puts them under pressure todo something. In other words, regulators cannot—practi-cally—be seen doing nothing. This results in an inherentrisk of regulatory overreaction in proximity of extremelyvolatile episodes. Long‐term consequences of such short‐term transformations are worth investigating. Further-more, quantification of additional costs associated withimplementing such constrictive measures and the resul-tant economic costs on the country and potentiallydiminished market quality promise to shed furtherinsight into the circuit breaker discourse.
8.4 | High‐frequency and algorithmictrading
Like circuit breakers, high‐frequency trading (HFT) andalgorithmic trading (AT) are divisive issues. The intersec-tion of the three is a source of even greater debate andchallenges. The proponents underline HFT and AT's rolein facilitating liquidity, deflating bid–ask spreads, and fos-tering pricing efficiency. The detractors, however, arguethat the improved efficiency has a steep trade‐off. Forstarters, from a technical point of view, the capacity bot-tlenecks of existing legacy systems to independently inter-act with each other engenders a high probability ofprecipitating extreme, undesirable events, preventingwhich falls directly in the domain of circuit breakers'efficacy. Ex post, tail events triggered by HFT typicallygenerate voluminous datasets, analysis of which is CPU‐
intensive, whereas its interpretation requires highlyskilled individuals trained in multiple disciplines—adearth of which exists in the regulatory domain. Besides,the sheer speed at which HFT precipitates extreme eventschallenges existing regulatory infrastructure. Consideringthese challenges, how regulatory brakes like circuit brea-kers need to catch up with the technology remains to beseen. Some proposals advanced by experts in this regardinclude the following:
a. Full/partial dissolution of extant order‐priority rulesin tranquil/distressed periods,
b. Introducing time‐price priority to dissuade theunproductive “millisecond latency” hardware race(Baron, Brogaard, & Kirilenko, 2014),
c. Applying a pro rata approach whereby all orders at agiven price receive proportionate (partial) execution,
d. Alternate priority scheme and Crumbling Quotesapproach ala IEX Exchange,
e. Introducing periodic auctions; midday auction inparticular—benefits of which were argued by Amihud,Mendelson, and Lauterbach (1997) a long time ago,
f. Command and Control schemes (e.g., mandatoryresting periods), and
g. Steep cancellation fees and transaction taxes.
Most suggestions mentioned here attract concernspertaining to competition and opportunities to dynami-cally hedge, both of which rely on synchronized tradingon the same exchange and rival exchange(s). Options f)and g) are likely to dampen an exchange's ability toattract orders while simultaneously irking free marketproponents. Option d), meanwhile, is particularly inter-esting to us given the context of forestalling scenarioswhere circuit breakers would be relevant. For example,in IEX traders enjoy the provision of having their orderedprotected from execution dynamically according tochanging trade velocity, which partially/fully insulatesthe limit orders should the quotes begin to change rap-idly, order flow dries up dramatically, or equivalentadverse scenarios. In practical terms, however, such trad-ing ecosystem innovations failed to attract trading activityto IEX, when compared with more established venues.Whether other exchanges follow down this path remainsto be seen. Additionally, the proliferation of private darkpools contributes to challenges in preventing volatile epi-sodes. Besides, what some decry as market fragmentation,others call congenial competition. Whatever the debate,that wide distribution of trades across so many venuesmakes flash crashes easier is hardly contested. Lastly,the mechanics and the events triggering the flash crashare well‐understood by the HFT exponents. Yet thereremain many loopholes and questionable practices, con-flicts of interest, and business models of the exchangesand traders that need to be streamlined.
8.5 | Blockchain
As regulators struggle in making judgement callspertaining to circuit breakers and volatile episodes,exchanges face a challenge regarding high frequencytrade transactions that exceed regulatory capacity to mon-itor it. Regarding this, third party solutions have beenproposed based on blockchain that can record, verify,and validate transactions. Project Medici, for example, isplanning to open an exchange called Medici based onblockchain with the goal of democratizing securities trad-ing by reducing the loopholes in the system and loweringcosts associated with going public transacting, and settle-ment. Others innovative suggestions include use of Nashequilibrium for securities market equilibrium via algo-rithms to better detect cartel‐like behaviour, which canalso be tweaked to prevent volatile episodes or crashes.Such measures can supplement or supplant existing cir-cuit breaker mechanisms. Although we are tempted to
SIFAT AND MOHAMAD 33
engage in a discussion of potential for circuit breakers inthe increasingly popular cryptocurrency markets, giventhe lack of central bankers, market makers, and, gener-ally speaking, any overarching authority, such a discoursewould be futile at this point. Equally daunting is theimmense technical difficulties of implementing a circuitbreaker on an open chain network, which the Blockchainproponents aim for.
9 | CONCLUSION
Circuit breakers, whether as price limits, trading halts, orboth, have become very common all over the financialmarkets around the world. So are circuit breakers reallyeffective? Its efficacy in stemming volatility, easing pricediscovery process, and improving market quality remainsa hot debate among industry practitioners, regulators,and academics. After nearly three decades of theoreticalstudies and empirical works, the short answer to thequestion is “we still don't know for sure.” Some of thebenefits of circuit breakers, as documented by studiesreviewed in this paper, are (a) ability to calm down anoverreactive or stressful market, (b) encourages reassess-ment of fundamentals during market turbulence, (c) cantemper bad (transitory) volatility foiling large non‐funda-mental price movements due to panic, bluffers, specula-tion, or manipulation,3 (d) reduction of transactionalrisk and enhancing risk‐sharing by encouraging liquidityproviders to participate, and (e) acting as partial substi-tute for margins in futures markets. Contrarily, studieshave found circuit breakers to (a) hinder price efficiencyby blocking the means to rational reaction to new infor-mation, (b) splattering volatility over a large time insteadof concentrating on a single day or session, (c) delayingdiscovery of equilibrium price, (d) obstructing optimaltrading plans of investors and thus reducing social wel-fare, (e) precipitation of order imbalance, (f) acting as aself‐fulfilling prophet, pulling prices to themselves byvery existence, (g) and failure to resolve informationasymmetry, and sometimes exacerbating it. However,many studies do find that price disequilibrium and orderimbalance correct themselves eventually after traderesumes. All in all, evidences so far are impressiveenough to question the justification of price limits. Andyet they have become nearly ubiquitous as we demon-strate in Table 1. The rate of adoption is very fast too.Most of the studies in the early 2000s dedicated a small
3The term bluffing (and not speculation) has been used here because wesubscribe to Harris's (1997) definition of speculator being an informedtrader and a bluffer being a trader who gambles. Though this may runcontrary to orthodox financial semantics, we consider it a superiortaxonomy.
10–12 row table to mention the number of exchangesadopting circuit breakers. A decade and a half later, wehave documented over 150 exchanges, bulk of whomemploy one form of circuit breaker or another. Morestrikingly, we have not found a single exchange thatadopted a circuit breaker and later abandoned it.
In the process of surveying for this paper, we havenoted a striking similarity between the post‐1987 crisisand post‐2010 crisis understanding of circuit breakers.In both cases, much hullabaloo ensued in favour ofgreater regulation of equity and derivatives markets. Inthe 1990s, a slew of theoretical studies followed to under-stand if new measures would be beneficial, with a fewempirical studies yielding ambiguous results. Paucity ofdata and a wide array of possible alternative explanationsfor sources of volatility meant that empirical studieslacked statistical power. In the next phase—newmillennium—academia appeared to lose interest in thedebate and very few theoretical studies were attempted,coinciding with lack of global crashes. It is interesting tonote, however, that the dot‐com bubble and crash didnot inspire any circuit breaker studies. During2000–2010, with growing availability of high‐frequencydata, soon a slew of empirical studies followed, mostlyon Asian markets. Once again, the results werenonconclusive, though—on average—tilted against regu-lators. Meanwhile, around the same time, widespreadadoption of circuit breakers took place (Figure 2). In thefinal phase, 2010–today, empirical studies became morefeasible due to greater availability of long time seriesgranular datasets, access to order books, limit‐up–limit‐down databases, and cooperation of exchanges. A note-worthy advancement in this stage has been consideringinsightful, new, alternative explanations for regulatoryintervention and nuanced look at the circuit breakerregimes. Synthetic, laboratory market studies too wereconducted, though some of its assumptions arequestioned by researchers and industry practitioners.And yet the number of exchanges adopting circuit brea-kers kept growing, which is not surprising consideringthe post‐crisis market climate. Therefore, now, the upshotis humanity's understanding of circuit breaker mecha-nisms remains still limited and compared to the 1990sthe progress has not been significant. In light of failureto address core methodological constraints plaguingempirical studies and relative disinterest in theoreticaldevelopment, regulators defy or downplay academic find-ings and continue its practice, albeit with periodictweaking. From a regulatory perspective, the circuitbreaker issue is a foregone conclusion, although mostare yet to provide empirical justification other than hope-ful claims and windy rhetoric. It is not disputed thattrading halts may ease the margin collection process for
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Circuit Breaker Adoption
New Adoption Proportion of Markets with CBs (%)
FIGURE 2 Rate of circuit breaker
adoption since the Brady Commission
Report. This image demonstrates the rate
at which circuit breakers have been
adopted by various exchanges since their
inception in the late 1980s until 2017. The
information is procured from a variety of
sources including publicly domain
information disseminated by exchanges,
fact books, analyst reports, exchange
federation data, and correspondence with
exchange personnel
34 SIFAT AND MOHAMAD
brokers (though others argue the benefits primarily infutures market cannot be generalized to equity) and thathalts allow an opportunity to traders to reflect in turbu-lent times as well as protecting them from an informa-tionally disorganized market when order flows surpassthe market's processing capacity. Innovations in process-ing capacity and trading algorithms has meant that manyof these benefits are less relevant today. Besides, the ben-efits should be considered against the risk of neglectingthe needs of hedgers, arbitrageurs, and investors partici-pating on utilitarian motives, without whom the marketswould be empty. Instruments impeding their trading abil-ity are unlikely to be ultimately advantageous. Besides,speculation is necessary for market survival as it incentiv-izes competition for liquidity. Irrational as uninformedtraders may be, denying them chance to participate hurtsthe informed traders in the long run as they miss out onprofitable trading opportunities by exploiting the unin-formed traders' erroneous strategies. Moreover,protecting the interest of limit order traders should beconsidered as well. They perform a thankless job of pro-viding liquidity and deserve to be compensated for it.Finally, the closing price of a security is a beacon forthe economy. Intervention that distorts or impedes theprocess to discovering a rational price for a securityimposes economic costs on the society. Large interdayor intraday price swings are not necessarily bad or irratio-nal. The cause of the price change matters more than itsmanifestation. Thus, demonizing volatility indiscrimi-nately only serves to slow the price discovery and byextension result in subpar resource allocation in theeconomy.
Despite all the short‐comings of circuit breakerschemes, innovative measures are on the way. After yearsof minor fine‐tuning and incremental improvements,conceptually complex are and potentially superior mech-anisms are garnering more attention. A one‐size‐fits allmechanism may not be far away. However, until such a
solution arrives, circuit breakers are the best ad hoc mea-sures available to stymie the erosion of market confi-dence. Hence, we conclude in words of Leinweber(2017): “Critics call them “Band‐aids“, but for now,band‐aids work.”
ACKNOWLEDGEMENTS
We thank the Ministry of Higher Education of Malaysiaand Research Management Centre of InternationalIslamic University Malaysia for providing the researchgrant (FRGS 15‐232‐0473).
ORCID
Imtiaz Mohammad Sifat http://orcid.org/0000-0001-7088-7995Azhar Mohamad http://orcid.org/0000-0002-1075-598X
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How to cite this article: Sifat IM, Mohamad A.Circuit breakers as market stability levers: A surveyof research, praxis, and challenges. Int J Fin Econ.2018;1–40. https://doi.org/10.1002/ijfe.1709