Topics in empirical market microstructure [Elektronische Ressource] : measuring the informational content of order flow / vorgelegt von Oliver Wünsche

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Topics in Empirical Market Microstructure:Measuring the Informational Content of Order FlowInaugural-Dissertationzur Erlangung des Doktorgradesder Wirtschaftswissenschaftlichen Fakult¨atder Eberhard-Karls-Universit¨at Tu¨bingenvorgelegt vonOliver Wu¨nscheaus L¨obau2010Dekanin: Prof. Dr. rer. pol. Kerstin PullErstberichterstatter: Prof. Dr. rer. pol. Joachim GrammigZweitberichterstatter: Prof. Dr.-Ing. Rainer Scho¨belTag der mu¨ndlichen Pru¨fung: 14. August 2008AcknowledgmentsAlthough there is a page to be submitted stressing out that this thesis is solely the authorsown work (except of course potential co-authors), alot of people are involved in the evolutionofthefinalpaper. Somearedirectlyinvolved throughsubjectmatterdiscussionsorinvarioussupport functions while some had a rather indirect effect. I could probably write a separatethesis naming all the people who I am grateful to. However, I rather focus to name a fewthat, I believe, had the most significant impact on this work.First and foremost, I want to thank my supervisor Joachim Grammig for all the supportand patience throughout my time in Tu¨bingen University. I learned a lot from you, Jo,far beyond the academic context. I would also like to thank my colleagues Robert Jung,KerstinKehrle, RamonaMaier, LuisHuergo, FranziskaPeter, ThomasDimpfl,PeterSchmidtas well as my co-author Erik Theissen for all their constructive feedback during numerouspresentationsandbrain-stormingsfromwhichIheavilybenefitted.

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Topics in Empirical Market Microstructure:
Measuring the Informational Content of Order Flow
Inaugural-Dissertation
zur Erlangung des Doktorgrades
der Wirtschaftswissenschaftlichen Fakult¨at
der Eberhard-Karls-Universit¨at Tu¨bingen
vorgelegt von
Oliver Wu¨nsche
aus L¨obau
2010Dekanin: Prof. Dr. rer. pol. Kerstin Pull
Erstberichterstatter: Prof. Dr. rer. pol. Joachim Grammig
Zweitberichterstatter: Prof. Dr.-Ing. Rainer Scho¨bel
Tag der mu¨ndlichen Pru¨fung: 14. August 2008Acknowledgments
Although there is a page to be submitted stressing out that this thesis is solely the authors
own work (except of course potential co-authors), alot of people are involved in the evolution
ofthefinalpaper. Somearedirectlyinvolved throughsubjectmatterdiscussionsorinvarious
support functions while some had a rather indirect effect. I could probably write a separate
thesis naming all the people who I am grateful to. However, I rather focus to name a few
that, I believe, had the most significant impact on this work.
First and foremost, I want to thank my supervisor Joachim Grammig for all the support
and patience throughout my time in Tu¨bingen University. I learned a lot from you, Jo,
far beyond the academic context. I would also like to thank my colleagues Robert Jung,
KerstinKehrle, RamonaMaier, LuisHuergo, FranziskaPeter, ThomasDimpfl,PeterSchmidt
as well as my co-author Erik Theissen for all their constructive feedback during numerous
presentationsandbrain-stormingsfromwhichIheavilybenefitted. Further,Ithankespecially
my colleague Stefan Frey who put tremendous effort in preparing the data set and setting up
a sophisticated ITinfrastructure. Without that achievement, thecalculations andanalyses in
thisthesis wouldhave probablytaken decadesuntil finalization. Althoughshewas sometimes
hard to understand due to her Swabian dialect (in particular when I was not yet familiar),
I strongly appreciate the contribution of Angelika Hutt who maintained the homepage and
helpedgreatly inall matters ofadministration. Ishouldalso notforget tothankourawesome
student assistants Anja, Julia, Uli, Natascha, Benno, Tobias, Felix and Miriam who did a
great job in providing support and brought (not rarely) entertainment to the office.
My fascination for statistics and econometrics however, started already during my under-
graduatestudies,inparticularduringmytimeasstudentassistantatthechairofeconometrics
of Reinhard Hujer in Frankfurt. I am heavily indebted to all the people who introduced me
into the mysteries of SAS, Gauss, LaTeX and the basic knowledge of empirical research,
iii
namely Marco, Dubi, Stefan, Christopher, Sandra, Stephan and last but not least my good
friend Paulo with whom I pretty simultaneously developed an enthusiasm for the world of
econometrics.
Finally, I want to thank my family who always supported me throughout the entire years
of education. Without you, not a single page of the following text would have been written.
Oliver Wu¨nsche
Zu¨rich, 06. February 2010Contents
Acknowledgments i
Contents iii
List of Figures v
List of Tables vi
1 Introduction 1
2 Time and the Price Impact of a Trade 6
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 Market Structure and Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3 Empirical Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3.1 The Dufour/Engle Approach . . . . . . . . . . . . . . . . . . . . . . . 14
2.3.2 A Structural Approach. . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.4 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.5 Interpretation and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.6 Conclusion and Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
A.1 Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
A.2 Additional Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3 Time Varying Arrival Rate Dynamics 49
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
3.2 Modeling Trade Arrival Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.2.1 Reviewing the EKOP Model . . . . . . . . . . . . . . . . . . . . . . . 52
iiiCONTENTS iv
3.2.2 Critical Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.3 The EKOP Model with Time Varying Trading Intensities . . . . . . . . . . . 55
3.4 Time Varying Arrival Rates on XETRA . . . . . . . . . . . . . . . . . . . . . 59
3.4.1 Intra-day Behavior of Different Types of Traders . . . . . . . . . . . . 59
3.4.2 Arrival Rate Dynamics. . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.5 Cross-Sectional Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
B.1 Derivation of Stable Likelihood . . . . . . . . . . . . . . . . . . . . . . . . . . 68
B.2 Intra-day Pattern of Arrival Rates . . . . . . . . . . . . . . . . . . . . . . . . 70
4 Using Mixed Poisson Distributions 72
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.2 Heterogeneity Within the Trading Group and Mixed Poisson Distributions . . 75
4.2.1 The Bivariate Poisson Inverse Gaussian Model . . . . . . . . . . . . . 76
4.2.2 The Bivariate Negbin Model . . . . . . . . . . . . . . . . . . . . . . . 77
4.3 Simulation of PIN Under Different Distributions . . . . . . . . . . . . . . . . 78
4.4 Empirical Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
C.1 Derivation of Stable Bivariate Negbin Likelihood . . . . . . . . . . . . . . . . 88
C.2 Parameter Bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
5 Summary and Outlook 91
Bibliography 94List of Figures
2.4.1 Intra-day patterns for the estimated standardized adverse selection components. . . . . . . 23
2.4.2 Time between trades versus adverse selection component. . . . . . . . . . . . . . . . . . 25
2.4.3 Time between trades versus standardized adverse selection component. . . . . . . . . . . 26
2.4.4 Time between trades versus standardized adverse selection component for individual stocks. . 27
2.4.5 Intra-day pattern of trade durations. . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.4.6 Results for the NYSE traded matched sample. . . . . . . . . . . . . . . . . . . . . . . 30
2.5.1 Average duration shock for different trade categories. . . . . . . . . . . . . . . . . . . . 31
A.2.1Intra-day patterns for the estimated standardized spread components. . . . . . . . . . . . 46
A.2.2Frequencies of different order types. . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.2.1 Tree representation of the EKOP model. . . . . . . . . . . . . . . . . . . . . . . . . 53
3.3.1 Analytical vs. simulated expected order imbalance. . . . . . . . . . . . . . . . . . . . . 57
3.4.1 Intra-day Pattern for the PIN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
B.2.1Intra-day Pattern for the arrival rate of the uninformed traders. . . . . . . . . . . . . . 70
B.2.2Intra-day Pattern for the arrival rate of the informed traders. . . . . . . . . . . . . . . 71
4.3.1 PIN bias when the data generating process is BPIG\BNB\Poisson. . . . . . . . . . . . . 81
4.4.1 Original vs. simulated data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
4.4.2 BNB-EKOP Estimates vs. Poisson-EKOP Estimates. . . . . . . . . . . . . . . . . . . 86
C.2.1Parameter bias when μ or α are varied. . . . . . . . . . . . . . . . . . . . . . . . . . 90
vList of Tables
2.2.1 Characteristics of the stocks in the sample (Xetra/DAX stocks). . . . . . . . . . . . . . 13
2.4.1 Estimation results of the extended MRR model with ACD shocks. . . . . . . . . . . . . . 20
2.4.2 Adverse selection in percent of the spread. . . . . . . . . . . . . . . . . . . . . . . . . 22
2.4.3 Correlations of the estimated standardized spread components . . . . . . . . . . . . . . 24
2.4.4 Matched sample of NYSE traded stocks . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.5.1 Numerical Example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
A.1.1Estimation results for a matched sample of NYSE traded stocks. . . . . . . . . . . . . . 35
A.1.2Estimation results of the DE quote revision equation. . . . . . . . . . . . . . . . . . . 36
A.2.1Estimation results for MRR for different periods of the day. . . . . . . . . . . . . . . . 38
A.2.2Implied spread and adverse selection share of MRR for different periods of the day. . . . . 39
A.2.3Standardized spread measures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
A.2.4Estimation results for MRR taking into account different trade types. . . . . . . . . . . . 47
3.2.1 Ljung-Box statistics for the order imbalance and the number of balanced trades. . . . . . . 56
3.4.1 Estimation results of the arrival rate dynamics. . . . . . . . . . . . . . . . . . . . . . 61
3.5.1 Estimation results for pooled regressions. . . . . . . . . . . . . . . . . . . . . . . . . 66
4.3.1 True model BNB\BPIG - estimated model Poisson. . . . . . . . . . . . . . . . . . . . 80
4.3.2 True model Poisson - estimated model BNB\BPIG. . . . . . . . . . . . . . . . . . . . 82
viChapter 1
Introduction
When Garman (1976) coined the term ”market microstructure”, a new research direction
in the broad field of finance and capital market research was born. In recent years, there
was a surge in financial market microstructure research. Due to the new dimensions of
computer technology and availability of data, especially, empirical studies are sprouting up.
Also new trading platforms and trading mechanisms have evolved and gain more and more
influence. Fully electronic limit order books play a very important role in today’s stock
exchange design. They differ from a traditional specialist market in terms of transparency,
anonymity and the wide variety of order types from a traditional specialist market. While
the classical capital market theory deals with equilibrium prices and equilibrium quantities,
market microstructure rather tries to shed light on the path to equilibrium. How can agents
benefit from not only watching the outcome of the trading process (e.g daily closing prices)
but the trading process itself (e.g whenand how much is traded on a transaction level)? How
fast are prices reacting to news events? What is the probability that a market event was
triggered by private information? How large is the impact of private information compared
to pure noise trading on prices? What is the best market design to facilitate a profitable
trading platform? All those important questions demand a more detailed look through the
microscope at the trading process itself.
Referring to Madhavan (2000), one could say:
”Market microstructure is the area of finance that studies the process by which
investors’ latent demands are ultimately translated into prices and volumes.”
1CHAPTER 1. INTRODUCTION 2
Whentalkingaboutempirical marketmicrostructureweusuallytalkaboutlargedatasets
representing the fast-paced trading process. Compared to traditional daily or weekly data,
high-frequency data poses an enormous challenge for the researcher. Generally spoken, the
notion that the more information, i.e. the more data, the better the results has been found
to be wrong in several respects. A famous example is measuring volatility more accurately
by using intra-day data of price changes. Even though it was shown that using a finer time
grid for the data could substantially enhance short-term forecasts there was a drawback.
Naturally, seeing the aforementioned improvement we would suggest to make the time grid
even finer and eventually take every available data point. The negative phenomenon related
to this issueis well knownas microstructure noise. Ifmicrostructure noise isleft unaccounted
for, increasing the frequency beyond a certain point can lead to serious flaws concerning the
estimated parameters of interest (compare A¨ıt-Sahalia, Mykland, and Zhang (2005)). On
the other hand, if agents act rationally, prices of financial assets should adjust very quickly
to their true values. Hence, it is desirable to use data on its highest frequency, so-called
tick-by-tick data to learn something about price discovery.
Broadly speaking, one could say that from a theoretical microstructure perspective, each
marketevent isinformative. Fromastatistical pointofviewanirregularlyspacedtick-by-tick
data series is a marked point process. The time stamps of the events are the points and the
realizations are the marks. Traditionally, time intervals were equally spaced and thus, did
not convey additional information. When modeling high-frequency time series not only are
the realizations of the variables of specific interest but their timing as well. Obviously, if the
timing of market events is not purely random, it is desirable to find an adequate modeling
approach describing the ”timing process”. Engle and Russell (1998) showed that the waiting
time between market events is predictable and proposed to model the waiting times as an
autocorrelated conditional duration (ACD) process. Since then, a plethora of econometric
models has been proposed to account for the irregularly spaced time occurrence of market
events. For example, Ghysels andJasiak (1998) findthat volatility has animpact onthe time
between transactions andthat the persistence inGARCH modelsdropswhentrade durations
are taken into account. Dufour and Engle (2000) analyze the price impact of trades taking
into account the trade duration.
The vast amount of literature on the topic comprises, among others, the book of Harris