040045 KU Econometrics in Finance (MA) (2024W)
Prüfungsimmanente Lehrveranstaltung
Labels
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mo 09.09.2024 09:00 bis Do 19.09.2024 12:00
- Anmeldung von Mi 25.09.2024 09:00 bis Do 26.09.2024 12:00
- Abmeldung bis Mo 14.10.2024 23:59
Details
max. 50 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
Mittwoch 15.01. 10:00-11:30 PC-Seminarraum 2, Oskar-Morgenstern-Platz 1 Untergeschoß
- Dienstag 01.10. 15:00 - 16:30 Seminarraum 12, Kolingasse 14-16, OG01
- Donnerstag 03.10. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
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Dienstag
08.10.
15:00 - 16:30
Seminarraum 12, Kolingasse 14-16, OG01
Seminarraum 15, Kolingasse 14-16, OG01 - Donnerstag 10.10. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
- Dienstag 15.10. 15:00 - 16:30 Seminarraum 12, Kolingasse 14-16, OG01
- Donnerstag 17.10. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
- Dienstag 22.10. 15:00 - 16:30 Seminarraum 12, Kolingasse 14-16, OG01
- Donnerstag 24.10. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
- Dienstag 29.10. 15:00 - 16:30 Seminarraum 12, Kolingasse 14-16, OG01
- Donnerstag 31.10. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
- Dienstag 05.11. 15:00 - 16:30 Seminarraum 12, Kolingasse 14-16, OG01
- Donnerstag 07.11. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
- Dienstag 12.11. 15:00 - 16:30 Seminarraum 12, Kolingasse 14-16, OG01
- Donnerstag 14.11. 15:00 - 16:30 Hörsaal 16 Hauptgebäude, Hochparterre, Stiege 5
- Dienstag 19.11. 15:00 - 16:30 Seminarraum 12, Kolingasse 14-16, OG01
- Donnerstag 21.11. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
- Dienstag 26.11. 15:00 - 16:30 Seminarraum 12, Kolingasse 14-16, OG01
- Donnerstag 28.11. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
- Dienstag 03.12. 15:00 - 16:30 Seminarraum 12, Kolingasse 14-16, OG01
- Donnerstag 05.12. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
- Dienstag 10.12. 15:00 - 16:30 Seminarraum 12, Kolingasse 14-16, OG01
- Donnerstag 12.12. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
- Dienstag 17.12. 15:00 - 16:30 Seminarraum 12, Kolingasse 14-16, OG01
- Dienstag 07.01. 15:00 - 16:30 Seminarraum 12, Kolingasse 14-16, OG01
- Donnerstag 09.01. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
- Dienstag 14.01. 15:00 - 16:30 Seminarraum 12, Kolingasse 14-16, OG01
- Mittwoch 15.01. 09:45 - 11:15 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Mittwoch 15.01. 11:30 - 13:00 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Donnerstag 16.01. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
- Dienstag 21.01. 15:00 - 16:30 Seminarraum 12, Kolingasse 14-16, OG01
- Donnerstag 23.01. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
- Dienstag 28.01. 15:00 - 16:30 Seminarraum 12, Kolingasse 14-16, OG01
- Donnerstag 30.01. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
The objective of this course is to introduce students to the field of financial econometrics and give them an overview of the most important topics and techniques. The emphasis will be on state-space models (e.g. stochastic volatility) and advanced Bayesian estimation methods. Empirical applications will depend on the bacgkround and interests of the students and may cover the estimation and testing of asset and derivatives pricing models and macro-financial econometric models. Therefore, a part of the course consists of practical sessions where some of the concepts will be applied to real financial data.
Art der Leistungskontrolle und erlaubte Hilfsmittel
The assessment consists of the following parts:i) closed-book midterm test, lasting about 60 minutes. The test can consist of multiple-choice questions, analytical derivations, and interpretations of empirical results.ii) Final exam. Depending on the number of course participants, the exams might be done as a written test or in oral form or as an empirical take-home project.iii) Take-home assignments: students must solve problems and submit written assignments. They can consist of multiple-choice questions, analytical derivations, coding and interpretations of empirical results. The solutions may also have to be presented in class.Important: aside from the three assignments, there will be no additional examination possibilities afterwards.
Mindestanforderungen und Beurteilungsmaßstab
Required prerequisites:- basic probability and econometrics (especially time-series analysis)
- maximum likelihood and GMM estimation as taught in "040033 - Econometrics II"
- knowledge of R and/or MATLABDesiderable prerequisites:- basic concepts in asset pricing and financial derivatives pricing
- basic Monte Carlo methods: see chapters 2-3 from the book "Introducing Monte Carlo Methods with R" (2009), by Robert and CasellaFor the final grade: (i) counts 30%, (ii) counts 45%, and (iii) counts 25%.To pass the course, a minimum level of 45% has to be reached.Rating:
[85%; 100%]: 1.0
[70%; 85%): 2.0
[55%; 70%): 3.0
[45%; 55%): 4.0
[0; 45%): 5.0
- maximum likelihood and GMM estimation as taught in "040033 - Econometrics II"
- knowledge of R and/or MATLABDesiderable prerequisites:- basic concepts in asset pricing and financial derivatives pricing
- basic Monte Carlo methods: see chapters 2-3 from the book "Introducing Monte Carlo Methods with R" (2009), by Robert and CasellaFor the final grade: (i) counts 30%, (ii) counts 45%, and (iii) counts 25%.To pass the course, a minimum level of 45% has to be reached.Rating:
[85%; 100%]: 1.0
[70%; 85%): 2.0
[55%; 70%): 3.0
[45%; 55%): 4.0
[0; 45%): 5.0
Prüfungsstoff
State-space models and Bayesian estimation:1. Principles of Bayesian inference
2. Monte Carlo methods: importance sampling, MCMC
3. State-space models and filtering methods: Kalman/particle filter
4. Simulated MLE, particle MCMC
5. Sequential Monte Carlo (SMC) samplers
6. SMC squared algorithmsApplications:7. Volatility models: GARCH, realized volatility, stochastic volatility
8. Option pricing models and DSGE models (depending on time and students' background/interests)
2. Monte Carlo methods: importance sampling, MCMC
3. State-space models and filtering methods: Kalman/particle filter
4. Simulated MLE, particle MCMC
5. Sequential Monte Carlo (SMC) samplers
6. SMC squared algorithmsApplications:7. Volatility models: GARCH, realized volatility, stochastic volatility
8. Option pricing models and DSGE models (depending on time and students' background/interests)
Literatur
There is no unique textbook for this course. A mixture of book chapters and research papers will be relevant for the development of the material covered. A preliminary list is the following:Andrieu, C., Doucet, A. and Holenstein, R. (2010) “Particle Markov Chain Monte Carlo", Journal of the Royal Statistical Society, Series B, 72, 269–342Bjork, T. (2009): “Arbitrage theory in continuous-time”, Third edition, Oxford FinanceDoucet, A. and Johansen, A. M. (2008) “A tutorial on particle filtering and smoothing: Fifteen years later", Handbook of Nonlinear Filtering, 12, 656–704Durbin, J. and Koopman, S. J. (2012): “Time series analysis by state-space methods'', Oxford University PressFulop, A. and Li, J. (2013) “Efficient learning via simulation: a marginalized resample-move approach", Journal of Econometrics, 176, 146–161Gouriéroux, C. and A. Monfort (1996): “Simulation-Based Econometric Methods", Oxford University PressGreenberg, E. (2008): “Introduction to Bayesian Econometrics", Cambridge University PressHautsch, N. (2012): “Econometrics of Financial High-Frequency Data”, SpringerHerbst, E. and Schorfheide, F. (2015): “Bayesian Estimation of DSGE Models", Princeton University PressHull, J. C. (2012): "Options, Futures, and Other Derivatives", Global EditionOsterlee, C. W. and Grzelak, L. A. (2019): “Mathematical Modeling and Computation in Finance", World Scientific Pub Co IncRobert, C. P. and Casella, G. (2009) “Introducing Monte Carlo Methods with R“, SpringerSärkkä, S. and Svensson, L. (2023): “Bayesian Filtering and Smoothing", Second Edition. Cambridge University Press
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Di 07.01.2025 17:45