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

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• Publié par: eberhard_karls_universitat_tubingen
• Publié le: 01 janvier 2010
• Nombre de lectures: 39
• Langue: English
• Poids de l'ouvrage: 1 Mo

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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. . . . . . . . .