Causality in Finance

About

This course delves into some of the statistical methods used to estimate causality in financial contexts. The course will cover, among other methods, Difference-in-Differences, and Interrupted Time Series.
During the course, each day will be dedicated to a specific method. We will start the day by studying a specific causality problem. We will then apply the method to our problem intuitively. In a second time, we will go over the mathematics of the method more formally and dive into further details. There will be short assignments to do every day about the method covered.
The course uses R programming to perform statistical analysis.
This course is addressed to third-year bachelor students who have an interest in developing their statistical toolkit in finance and first-year master students who want to consolidate their knowledge of causality in finance.

Course leader

Mathis Mourey

Target group

- Statistics: Descriptive Statistics, Inferential Statistics (Multivariate Regression, ANOVA). Preferably had an introduction about ARMA models, but not mandatory.
- Fundamental knowledge in finance (type of financial instruments, understanding of markets, familiar with financial jargon)
- Beginner knowledge in R and RStudio

Course aim

• Understand the advantages and drawbacks of each method presented in the course.
• Apply the right statistical method to a financial causality problem.
• Use R programming to perform all methods covered in class appropriately.
• Reflect critically when interpreting the results given by the chosen statistical method.

Fee info