Treatment of Double Default Effects within the Basel Regulatory Framework and a Theoretical and an Experimental Investigation of Higher-Order Risk Preferences [Elektronische Ressource] / Sebastian Ebert

Sebastian Ebert

Informations

• Publié par: rheinische_friedrich-wilhelms-universitat_bonn
• Publié le: 01 janvier 2011
• Nombre de lectures: 15
• Langue: English
• Poids de l'ouvrage: 1 Mo

Extrait

Treatment of Double Default Effects within the
Basel Regulatory Framework and a Theoretical
and an Experimental Investigation of
Higher-Order Risk Preferences
Inaugural-Dissertation
zur Erlangung des Grades eines Doktors
der Wirtschafts- und Gesellschaftswissenschaften
durch die
Rechts- und Staatswissenschaftliche Fakultät
der Rheinischen Friedrich-Wilhelms-Universität
Bonn
vorgelegt von
Sebastian Ebert
aus Gutach (Schwarzwaldbahn)
Bonn 2011Dekan: Prof. Dr. Klaus Sandmann
Erstreferent: Prof. Dr. Eva Lütkebohmert
Zweitreferent: Prof. Dr. h.c. Harris Schlesinger, Ph.D.
Tag der mündlichen Prüfung: 08.07.2011
Diese Dissertation ist auf dem Hochschulschriftenserver der ULB Bonn
(http://hss.ulb.uni-bonn.de/diss_online) elektronisch publiziert.
ii.meinen Eltern
vAcknowledgements
First of all I want to thank my supervisor Eva Lütkebohmert, who introduced me to con-
ducting research by starting my dissertation with a joint project. This collaboration was
always very pleasant and fruitful. Ilearned a lot fromher deep insights into bankingregu-
lation as well as from her mathematical expertise. Later on she also gave me the freedom
tostartmyownresearch agenda. Further, shealways tookhertimetodiscussmyresearch
aswellasmyplansandambitions. IalsowanttothankHarrisSchlesinger. Myresearchon
higher-order risk preferences has significantly benefited from numerous communications
with him over the recent years. I thank Klaus Sandmann for chairing my dissertation
committee and for other things (see below). I want to thank Urs Schweizer, Silke Kinzig,
and their team for passionately running the Bonn Graduate School of Economics.
I am very thankful to my coauthor and close friend Daniel Wiesen for the successful
collaboration. It has been great fun discussing ideas, conducting experiments, analyzing
data, and writing papers with him. Further, I very much have to thank Louis Eeckhoudt
for his interest in my research and the selfless support he has given to Daniel and me. His
insights on decision theory have been particularly inspiring.
I am thankful to Klaus Sandmann for providing me a great working environment at his
chair BWL 3 - Banking and Finance. Also he gave me very good advice throughout the
years. I very much enjoyed the friendly atmosphere at BWL 3. Indeed, I always had a
good feeling when coming there in the mornings. Thisis because theteam consists of very
nice and kind people. These include my office mates Marcelo Cadena, Franz Goedecke,
Christian Hilpert, Jing Li, Filip Uzelac, and, right next door, Birgit Koos and Manuel
Wittke, Anne Ruston, and the many student assistants. I very much benefited, personally
and intellectually, from discussions with the doctoral students and the young professors
at BWL 3, Alexander Szimayer and An Chen. Both of them were very friendly and open
to me right from the beginning and were willing to share some of their experiences in
academia with me.
I also want to thank my classmates from the Bonn Graduate School of Economics. We
were a great team and supported each other personally as well as in our research and
career plans. Many of them became close friends of mine. Besides, we surely had a lot of
viifun over the years. Further (financial) economists I have to thank are Stefan Ankirchner,
André Betzer, Armin Falk, Asen Kochov, Johannes Maier, Farzad Saidi, Reinhard Selten,
Klaus Utikal, and Mark van Achter.
From time to time, several people reminded me that there is a life beyond Ph.D. studies.
I am indebted to numerous close friends that made my time in Bonn so enjoyable. For
various reasons I will not mention specific names.
For me, it is important to thank some excellent teachers whose lectures I enjoyed so
much during my time as a student. These professors, in particular, inspired me and
sparked my passion for becoming a teacher and researcher myself: Ernst Eberlein, Josef
Hohnerkamp, Daniel Hug, Lorens Imhof, Jong-Sun Kim, Alois Kneip, Eva Lütkebohmert,
Benny Moldovanu, and Klaus Sandmann. I also want to thank my high school teachers
Sabine and Stefan Glöckler.
Financial support from the Bonn Graduate School of Economics is gratefully acknowl-
edged. I am grateful to the Heinrich Hertz-Stiftung e.V. for supporting my research visit
at University of Technology, Sydney. I thank Erik Schögl, Klaus Sandmann, and Urs
Schweizer for supporting me in conducting this very fruitful visit.
I am indebted to Magdalena Bundil for her love in good and bad times.
Finally, Iwishtothankmyfamilyand,inparticular,myparents: RegineandHans-Jürgen
Ebert. Words cannot express how much I owe to them.
viiiContents
Introduction 1
I On Double Default Modeling in the Basel Framework 11
1 TreatmentofDouble DefaultEffectswithintheGranularityAdjust-
ment (GA) for Basel II 13
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.2 Notations and basic GA methodology . . . . . . . . . . . . . . . . . . . . 16
1.3 Some illustrative examples and discussion of the methodology . . . . . . . 20
1.4 GA for a portfolio with several guarantees . . . . . . . . . . . . . . . . . . 27
1.5 Numerical validation of the analytical GA formula . . . . . . . . . . . . . 30
1.6 Discussion and conclusion to Chapter 1 . . . . . . . . . . . . . . . . . . . 33
2 Improved Double Default Modeling for the Basel Framework - An
Endogenous Asset Drop Model without Additional Correlation 37
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.2 Review and discussion of the Internal Ratings Based (IRB) treatment of
double defaults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.3 The asset drop technique as an alternative approach . . . . . . . . . . . . 46
2.3.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
2.3.2 Generalizations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
2.4 Conclusion to Chapter 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
II On Higher-Order Risk Preferences 59
3 Moment Characterization of Higher-Order Risk Preferences 61
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
3.2 Proper risk apportionment, skewness, kurtosis, and moments . . . . . . . 64
ix3.3 Moment characterizations of prudence and temperance . . . . . . . . . . . 68
3.4 Higher-order generalizations . . . . . . . . . . . . . . . . . . . . . . . . . . 72
3.5 Conclusion to Chapter 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
4 Testing for Prudence and Skewness Seeking 77
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
4.2 Prudence and skewness seeking . . . . . . . . . . . . . . . . . . . . . . . . 81
4.2.1 Mao’s lotteries and Eeckhoudt and Schlesinger’s prudence lotteries 81
4.2.2 Prudence, moments, and skewness seeking . . . . . . . . . . . . . . 83
4.3 Research questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
4.4 Experimental design and procedure . . . . . . . . . . . . . . . . . . . . . . 86
4.4.1 Prudence test embedded in a factorial design: stage ES . . . . . . 87
4.4.2 Skewness seeking test: stage MAO . . . . . . . . . . . . . . . . . . 92
4.4.3 Procedural details . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
4.5 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
4.5.1 Preliminary analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 94
4.5.2 Within subject analysis . . . . . . . . . . . . . . . . . . . . . . . . 96
4.5.3 Influences on prudent behavior . . . . . . . . . . . . . . . . . . . . 97
4.6 Conclusion to Chapter 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
A Appendices to Chapters 1, 3, and 4 103
A.1 Appendix to Chapter 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
A.2 Appendix to Chapter 3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
A.3 Appendix to Chapter 4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
A.3.1 Proofs to Section 4.2 . . . . . . . . . . . . . . . . . . . . . . . . . . 115
A.3.2 Instructions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
B CalibrationofBinaryLotteriesinExperimentsandaResultontheir
Skewness 125
B.1 Lottery calibration in experiments . . . . . . . . . . . . . . . . . . . . . . 126
B.2 Skewness in binary lotteries . . . . . . . . . . . . . . . . . . . . . . . . . . 129
B.3 Concluding remarks to Appendix B . . . . . . . . . . . . . . . . . . . . . . 131
Bibliography 132
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