Market Microstructure: Confronting Many Viewpoints

244 Pages·2012·5.504 MB·English

Market Microstructure
ForothertitlesintheWileyFinanceseries
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Market Microstructure
Confronting Many Viewpoints
Editedby
Fre´de´ric Abergel
Jean-Philippe Bouchaud
Thierry Foucault
Charles-Albert Lehalle
Mathieu Rosenbaum
A John Wiley & Sons, Ltd., Publication
Thiseditionfirstpublishedin2012
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Marketmicrostructure:confrontingmanyviewpoints/editedbyFre´de´ricAbergel...[etal.].
p.cm.–(TheWileyfinanceseries)
Includesbibliographicalreferencesandindex.
ISBN978-1-119-95277-0(cloth)
1.Securities. 2.Securities–Prices. 3.Stockexchanges. 4.Microfinance. I.Abergel,
Fre´de´ric.
HG4521.M3192012
332.64(cid:2)2–dc23 2012002916
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Contents
Introduction xi
AbouttheEditors xiii
PARTI ECONOMICMICROSTRUCTURETHEORY
1 AlgorithmicTrading:IssuesandPreliminaryEvidence 3
ThierryFoucault
1.1 Introduction 3
1.2 Whatisalgorithmictrading? 6
1.2.1 Definitionandtypology 6
1.2.2 Scopeandprofitability 11
1.3 Marketstructureandalgorithmictrading 12
1.4 Costsandbenefitsofalgorithmictrading 16
1.4.1 Algorithmictradingreducessearchcosts 16
1.4.2 Algorithmictradinghasanambiguouseffecton
adverseselectioncosts 19
1.4.3 Algorithmictradingandpricediscovery 20
1.4.4 Welfareeffects 22
1.4.5 Algorithmictradingasasourceofrisk 24
1.5 Empiricalevidence 26
1.5.1 Algorithmictradingandmarketliquidity 27
1.5.2 Algorithmictradingandvolatility 29
1.5.3 Algorithmictradingandpricediscovery 31
1.5.4 Algorithmictradingandmarketstability 34
vi Contents
1.6 Conclusions 36
Appendix 38
Acknowledgment 39
References 39
2 OrderChoiceandInformationinLimitOrderMarkets 41
IoanidRos¸u
2.1 Introduction 41
2.2 Orderchoicewithsymmetricinformation 42
2.3 Orderchoicewithasymmetricinformation 48
2.4 Theinformationcontentoforders 54
2.5 Questionsforfutureresearch 57
References 58
PARTII HIGHFREQUENCYDATAMODELING
3 SomeRecentResultsonHighFrequencyCorrelation 63
NicolasHuthandFre´de´ricAbergel
3.1 Introduction 63
3.2 Datadescription 64
3.3 Multivariateeventtime 66
3.3.1 Univariatecase 66
3.3.2 Multivariatecase 68
3.3.3 Empiricalresults 69
3.4 Highfrequencylead/lag 74
3.4.1 TheHayashi–Yoshidacross-correlationfunction 74
3.4.2 Empiricalresults 77
3.5 Intradayseasonalityofcorrelation 80
3.5.1 Empiricalresults 82
3.6 Conclusion 85
Acknowledgment 85
References 85
4 StatisticalInferenceforVolatilityandRelatedLimit
Theorems 87
NakahiroYoshida
4.1 Introduction 87
4.2 QLAforanergodicdiffusionprocess 88
4.3 QLAforvolatilityinthefinitetime-horizon 95
4.4 Nonsynchronouscovarianceestimation 98
Contents vii
4.4.1 Consistentestimator 98
4.4.2 Functionallimittheorem 99
4.4.3 ApplicationofYUIMA 101
4.4.4 Lead–lagestimation 102
4.5 YUIMAIIforstatisticalanalysisandsimulationfor
stochasticdifferentialequations 103
4.6 Higherorderasymptoticsandfinance 105
4.6.1 Martingaleexpansion 105
4.6.2 Smallσ expansion 107
Acknowledgments 107
References 108
PARTIII MARKETIMPACT
5 ModelsfortheImpactofAllOrderBookEvents 115
Zolta´nEisler,Jean-PhilippeBouchaud,andJulienKockelkoren
5.1 Introduction 115
5.2 Ashortsummaryofmarketorderimpactmodels 116
5.3 Many-eventimpactmodels 119
5.3.1 Notationanddefinitions 119
5.3.2 Thetransientimpactmodel(TIM) 121
5.3.3 Thehistorydependentimpactmodel(HDIM) 122
5.4 Modelcalibrationandempiricaltests 125
5.4.1 Data 125
5.4.2 Thecaseoflargeticks 126
5.4.3 Thecaseofsmallticks 127
5.5 Conclusion 131
Appendix 133
Acknowledgments 134
References 134
6 LimitOrderFlow,MarketImpact,andOptimalOrder
Sizes:EvidencefromNASDAQTotalView-ITCHData 137
NikolausHautschandRuihongHuang
6.1 Introduction 137
6.2 Marketenvironmentanddata 139
6.3 Majororderflowandorderbookcharacteristics 141
6.4 Aneconometricmodelforthemarketimpactof
limitorders 145
viii Contents
6.4.1 AcointegratedVARmodelforthelimit
orderbook 145
6.4.2 Estimatingmarketimpact 149
6.5 MarketimpactatNASDAQ 153
6.6 Optimalordersize 157
6.7 Conclusions 160
Acknowledgment 161
References 161
PARTIV OPTIMALTRADING
Introduction:TradingandMarketMicro-structure 165
Charles-AlbertLehalle
References 169
7 CollectivePortfolioOptimizationinBrokerageData:
TheRoleofTransactionCostStructure 171
DamienChalletandDavidMortondeLachapelle
7.1 Introduction 171
7.2 Descriptionofthedata 172
7.3 Results 173
7.4 Theinfluenceoftransactioncostsontradingbehavior
fromoptimalmean-varianceportfolios 176
7.5 Discussionandoutlook 184
Acknowledgments 185
References 185
8 OptimalExecutionofPortfolioTransactionswith
Short-TermAlpha 187
AdrianaM.CriscuoloandHenriWaelbroeck
8.1 Introduction 187
8.2 Short-termalphadecayandhiddenorderarbitrage
theory 190
8.3 Totalcostdefinitionandconstraints 193
8.3.1 Equationswithouttheriskterm 193
8.3.2 Equationsincludingriskwithoutthealphaterm 195
8.4 Totalcostoptimization 196
8.4.1 Resultsforλ = 0andthearbitraryalphaterm 196
8.4.2 Risk-adjustedoptimization 202
Contents ix
8.5 Conclusions 206
8.5.1 Mainresultsintheabsenceofshort-termalpha 206
8.5.2 Mainresultswithshort-termalpha 207
8.5.3 Institutionaltradingpractices 208
Proviso 210
References 210
CombinedReferences 213
Index 227
Introduction
The accumulation of high frequency market data in recent years has
revealedmanysurprisingresults.Theseresultsareinterestingbothfrom
theoreticalandpracticalstandpoints.Themechanismofpriceformation
is at the very heart of economics; it is also of paramount importance
to understand the origin of the well-known anomalous ‘stylized facts’
in financial price series (heavy tails, volatility clustering, etc.). These
issuesareofobviousimportanceforpracticalpurposes(organisationof
markets,executioncosts,priceimpact,etc.).Thisactivityisalsocrucial
to help the regulators, concerned with the organisation of liquidity in
electronicmarketsandtheissuesraisedby‘highfrequencytrading’.
Correspondingly, this problem has been vigorously investigated by
at least five different communities (economics, financial mathematics,
econometrics, computer science and econo-physics), scattered in aca-
demicinstitutions,banksandhedgefunds,withatpresentlimitedover-
lap and sometimes lack of visibility. On the other hand, due to the
giganticamountofavailabledata,precisequantitativetheoriescannow
beaccuratelytested.
Atthetimewherethisconferenceseriesstartedin2010,theinterest
formarketmicrostructurehadfinallyreachedastagewheretheinterest
forthetheoreticalbreakthroughsofthepioneersinthefieldhadbecome
comparabletoitspracticalimportanceformarketpractitioners.Thanks
to the development of high frequency trading, market microstructure
is now, not only a subject of theoretical modelling and simulation but,
moreinterestinglymaybe,arealpracticalfieldwhereabettermodelcan
makeabigdifference.
The organisers of the conference thought that it would be extremely
fruitful to confront the ideas that have blossomed in those different