Winter Term 21/22

Lecture & Exercise

Financial Econometrics (Master)

  • Prof. Dr. Peter N. Posch
Winter Semester 2022/2023
Monday, 10 a.m. to 2 p.m. (s.t.)
TU Dortmund/Online


General Information:

As part of the cooperative teaching project "Applied Financial Econometrics", we enable the enrollment in the course "Financial Econometrics" at the TU Dortmund University. The course is complementary to the master's course in "Empirical Finance" at the MSM. The course can be credited within the mobility window. The crediting will be possible without any problems and can be done either with 7.5 ECTS (full course) or with 5 ECTS in case of completion of a slightly streamlined course program/exam.

The registration can be made informally by e-mail. Please note, however, that PC capacity is limited, which means you should only register if you are sure you really want to take the course.

Please send inquiries or registrations to Noah Urban. Please use your "" address for all inquiries.

More information about the course can be found below:

Content of the Module:

This lecture applies modern econometric methods to current questions from the field of finance, risk-management and commodity markets. We will both explore the theoretical dimensions of the models used as well as apply the methods to real-life datasets.

Econometric methods covered: OLS (Gauss-Markov Assumptions, Hypothesis Testing, Omitted Variables Bias, Multicollinearity, ...), Quantile Regression, Maximum Likelihood Estimation (MLE), Time Series (Autocorrelation, Random Walk, Stationarity, Autoregressive (AR) and Moving Average (MA) Processes and ARMA, Vector Autoregressive (VAR) Processes, ARCH and GARCH Models), and Forecasts etc.


Students learn the basic and advanced methods of financial econometrics. They apply the methods using datasets and thereby learn both the application of econometric methods as well as the caveats associated with real-life data, data gathering and data mining. The use of the industry specific programming language (currently Python) for econometric analysis is an essential part of this course.


None. However, knowledge in statistical and econometrical methods as well as prior knowledge in finance is strongly recommended. Due to limited PC capacities you need to apply for this course.

Methods of Assessment:

Written and graded exam covering the entire module (90 minutes) or graded presentation based on written case study’s expose. The mode of the exam will be assigned at the beginning of the course.