Universität Wien

040045 KU Econometrics in Finance (MA) (2024W)

8.00 ECTS (4.00 SWS), SPL 4 - Wirtschaftswissenschaften
Prüfungsimmanente Lehrveranstaltung
Moodle

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

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
  • 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 MATLAB

Desiderable 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 Casella

For 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 algorithms

Applications:

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, 269–342

Bjork, T. (2009): “Arbitrage theory in continuous-time”, Third edition, Oxford Finance

Doucet, A. and Johansen, A. M. (2008) “A tutorial on particle filtering and smoothing: Fifteen years later", Handbook of Nonlinear Filtering, 12, 656–704

Durbin, J. and Koopman, S. J. (2012): “Time series analysis by state-space methods'', Oxford University Press

Fulop, A. and Li, J. (2013) “Efficient learning via simulation: a marginalized resample-move approach", Journal of Econometrics, 176, 146–161

Gouriéroux, C. and A. Monfort (1996): “Simulation-Based Econometric Methods", Oxford University Press

Greenberg, E. (2008): “Introduction to Bayesian Econometrics", Cambridge University Press

Hautsch, N. (2012): “Econometrics of Financial High-Frequency Data”, Springer

Herbst, E. and Schorfheide, F. (2015): “Bayesian Estimation of DSGE Models", Princeton University Press

Hull, J. C. (2012): "Options, Futures, and Other Derivatives", Global Edition

Osterlee, C. W. and Grzelak, L. A. (2019): “Mathematical Modeling and Computation in Finance", World Scientific Pub Co Inc

Robert, C. P. and Casella, G. (2009) “Introducing Monte Carlo Methods with R“, Springer

Särkkä, 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