040045 KU Econometrics in Finance (MA) (2023W)
Continuous assessment of course work
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ON-SITE
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Mo 11.09.2023 09:00 to Fr 22.09.2023 12:00
- Registration is open from Tu 26.09.2023 09:00 to We 27.09.2023 12:00
- Deregistration possible until Fr 20.10.2023 23:59
Details
max. 50 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
Tuesday
03.10.
15:00 - 16:30
Seminarraum 6, Kolingasse 14-16, EG00
Thursday
05.10.
15:00 - 16:30
Seminarraum 6, Kolingasse 14-16, EG00
Tuesday
10.10.
15:00 - 16:30
Seminarraum 15, Kolingasse 14-16, OG01
Thursday
12.10.
15:00 - 16:30
Seminarraum 15, Kolingasse 14-16, OG01
Tuesday
17.10.
15:00 - 16:30
Seminarraum 15, Kolingasse 14-16, OG01
Thursday
19.10.
15:00 - 16:30
Seminarraum 15, Kolingasse 14-16, OG01
Tuesday
24.10.
15:00 - 16:30
Seminarraum 15, Kolingasse 14-16, OG01
Tuesday
31.10.
15:00 - 16:30
Seminarraum 15, Kolingasse 14-16, OG01
Tuesday
07.11.
15:00 - 16:30
Seminarraum 15, Kolingasse 14-16, OG01
Thursday
09.11.
15:00 - 16:30
Seminarraum 15, Kolingasse 14-16, OG01
Tuesday
14.11.
15:00 - 16:30
Seminarraum 15, Kolingasse 14-16, OG01
Thursday
16.11.
15:00 - 16:30
Seminarraum 15, Kolingasse 14-16, OG01
Tuesday
21.11.
15:00 - 16:30
Seminarraum 15, Kolingasse 14-16, OG01
Thursday
23.11.
15:00 - 16:30
Seminarraum 15, Kolingasse 14-16, OG01
Tuesday
28.11.
15:00 - 16:30
Seminarraum 15, Kolingasse 14-16, OG01
Thursday
30.11.
15:00 - 16:30
Seminarraum 15, Kolingasse 14-16, OG01
Tuesday
05.12.
15:00 - 16:30
Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
Thursday
07.12.
15:00 - 16:30
Seminarraum 15, Kolingasse 14-16, OG01
Tuesday
12.12.
15:00 - 16:30
Seminarraum 15, Kolingasse 14-16, OG01
Thursday
14.12.
15:00 - 16:30
Seminarraum 15, Kolingasse 14-16, OG01
Monday
08.01.
15:00 - 16:30
Seminarraum 15, Kolingasse 14-16, OG01
Thursday
11.01.
15:00 - 16:30
Seminarraum 15, Kolingasse 14-16, OG01
Tuesday
16.01.
15:00 - 16:30
Seminarraum 15, Kolingasse 14-16, OG01
Thursday
18.01.
15:00 - 16:30
Seminarraum 15, Kolingasse 14-16, OG01
Tuesday
23.01.
15:00 - 16:30
Seminarraum 15, Kolingasse 14-16, OG01
Thursday
25.01.
15:00 - 16:30
Seminarraum 15, Kolingasse 14-16, OG01
Tuesday
30.01.
15:00 - 16:30
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Seminarraum 17, Kolingasse 14-16, OG02
Seminarraum 17, Kolingasse 14-16, OG02
Information
Aims, contents and method of the course
Assessment and permitted materials
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) Closed-book final exam, lasting about 60 minutes. Depending on the number of course participants, the exams might be done 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.
Minimum requirements and assessment criteria
Required prerequisites:- probability and econometrics (especially time-series analysis) as taught in "040111 - Introductory Econometrics"
- knowledge of R and/or MATLABDesiderable prerequisites:- maximum likelihood and GMM estimation as taught in "040033 - Econometrics II"
- 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 40%, and (iii) counts 30%.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
- knowledge of R and/or MATLABDesiderable prerequisites:- maximum likelihood and GMM estimation as taught in "040033 - Econometrics II"
- 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 40%, and (iii) counts 30%.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
Examination topics
Approximate syllabus:1. Introduction to stochastic calculus
2. Continuous-time pricing models
3. Volatility models: GARCH, realized volatility, stochastic volatility
4. State-space models and filtering methods (Kalman and particle filtering)
5. Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC)
6. Bayesian inference with particle MCMC and SMC squared algorithms
7. Empirical applications: estimation of option pricing models and DSGE models
2. Continuous-time pricing models
3. Volatility models: GARCH, realized volatility, stochastic volatility
4. State-space models and filtering methods (Kalman and particle filtering)
5. Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC)
6. Bayesian inference with particle MCMC and SMC squared algorithms
7. Empirical applications: estimation of option pricing models and DSGE models
Reading list
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
Association in the course directory
Last modified: Mo 27.11.2023 15:27
applications will 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.