SE-419 Financial Econometrics (Autumn 2025)

Upon completion of this course the student should be able to:

• demonstrate knowledge of the econometric methods used in finance
• compare and contrast the merits as well as the limits of econometric approaches to financial data
• locate, retrieve, pre-process and visualize relevant financial data using R
• implement an analytical approach to learning from financial data
• structure and solve financial problems by combining relevant knowledge from the areas of finance, statistics and mathematics
• estimate financial models, test hypotheses and generate responsible forecasts using R
• communicate the results of the analysis to specialists as well as to non-specialist audiences
• track innovations in finance and follow new developments in the programming language R

This course starts with a primer on formulating statistical models in R. By means of examples, the R packages relevant for the course are introduced.

The course provides a rigorous introduction to core econometric techniques used in empirical economic and financial research. It begins with the classical Ordinary Least Squares (OLS) framework for estimating linear relationships and evaluating model assumptions. The focus then shifts to modelling time-dependent data using AutoRegressive Moving Average (ARMA) models, emphasizing stationarity, lag structure, and forecasting. Finally, the course covers ARCH and GARCH models for capturing time-varying volatility, a key feature of financial time series. Throughout the course, students will apply these techniques using real-world data and statistical software, gaining practical skills in model estimation, diagnostic testing, and forecasting.

The course consists of lectures and group work sessions. Estimated workload is about 200 hours.

Theoretical arguments presented in the lectures are supplemented by student projects. A project typically involves the application of statistical procedures (open source statistical program R) to real-life financial data sets. Each project is designed to give the student an alternative access to a theoretical issue and to train specific data analytical skills. Through this applied focus, we increase the students’ awareness of the potential as well as the limitations of state-of-the art econometric tools for finance.