Go to content

Applied Financial Econometrics

Current Issues

02.03.2022: In the summer term 2022 the course is schedules as in person. Updates will be given shortly before lectures start.


10.06.2016:

Literature for Bayesian methodology:



Course Contents

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). 

Course Outline 

  1. The basics of time series modeling: autoregressive and moving average processes
  2. Forecasting (financial) time series
  3. More on time series modeling: unit root tests and diagnostic tools
  4. Modeling volatility dynamics: ARCH, GARCH, and TGARCH models as well as appropriate maximum likelihood estimators and their properties
  5. Long-run forecasting
  6. Explaining returns and estimating factor models 

Literature

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. 

Grading System

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).


Downloads

Allgemein: Übergang der PO
Lecture Tutorial Other Stuff
Slides List of Exercises

Ergodicity and Stationarity Picture

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 4 AR3-Process LagLenthSelection.RData
Tutorial 5 Random Walk ARIMA Forecast

BMW Forecast
ARIMA Selection Graph
BMW Model Check
BMW Improvement

Lag Length Selection

Simulated Data

ADF Simulation Summary
BMW Unit Root
Zloty Exchange Rates
Consumer Price Analysis
ML Bernoulli ML Bernoulli PDF
Joint Log Likelihood
ARCH(1)
AR and GARCH
Time series analysis
Exponential Family
Bayesian AR(p)

Appointments and Rooms

Schedule

Lecture Thu 10.00-12.00 R 009

Rolf Tschernig

First session: 28.04.2022

 

Tutorial Mon 8:30-10:00 W 116

Rolf Tschernig

First session: 02.05.2022

 



  1. Faculty of Business, Economics and Management Information Systems
  2. Department of Economics

Chair of Econometrics