02.03.2022: In the summer term 2022 the course is schedules as in person. Updates will be given shortly before lectures start.
Literature for Bayesian methodology:
- Bayesian Econometrics:
- Bayesian Statistics:
Aim of the Course:
Participants of this course study the theory and practice for modeling univariate (financial) time series. Students perform empirical projects including programming tasks in R.
The course is taught in English (on request German).
- The basics of time series modeling: autoregressive and moving average processes
- Forecasting (financial) time series
- More on time series modeling: unit root tests and diagnostic tools
- Modeling volatility dynamics: ARCH, GARCH, and TGARCH models as well as appropriate maximum likelihood estimators and their properties
- Long-run forecasting
- Explaining returns and estimating factor models
Information about the literature can be found on the slides.
Audience / Qualification
A prerequisite for the participation in the course Applied Financial Economtrics is the participation in the course Econometrics I or an equivalent course plus some basics in R.
The course Applied Financial Economtrics is a compulsory part of the study specializations Empirical Economics and Financial Markets for economics, an optional compulsory part of the study specialization Corporate Finance for business administration, and optional for all other students.
The course consists of one midterm exam (Lernzielkontrolle), the exercise presentation, and a final exam.
All details can be found in the overview (in German).
|Allgemein: Übergang der PO|
|Slides||List of Exercises|
|Lecture 6: R Code||ToDo-List||Moments|
|Lecture 7: R Code||AR1 Simulation, Shiller2014.csv|
|Tutorial 1||R Solutions||BWMData.csv, CAPM.RData|
|Tutorial 2||Working with data||ZlotyExchangeRate.csv, Siemens.txt|
|Tutorial 3||First Simulation||ConsumerPriceAnalysis.csv|
|Tutorial 5||Random Walk||ARIMA Forecast|
|ARIMA Selection Graph|
|BMW Model Check|
Lag Length Selection
|ADF Simulation Summary|
|BMW Unit Root|
|Zloty Exchange Rates|
|Consumer Price Analysis|
|ML Bernoulli||ML Bernoulli PDF|
|Joint Log Likelihood|
|AR and GARCH|
|Time series analysis|
Appointments and Rooms
|Lecture||Thu||10.00-12.00||R 009|| |
First session: 28.04.2022
|Tutorial||Mon||8:30-10:00||W 116|| |
First session: 02.05.2022