37835 - Financial Econometrics

Learning outcomes

This course is an introduction to financial econometrics. Background  knowledge of the linear regression model and the ordinary least squares technique is not strictly required but the course will discuss very briefly these topics. The objective of the course is to introduce more advanced topics, such as linear time series models, autoregressive conditional heteroskedasticity models, limited dependent variable models and panel data models. All applications will be conducted using the econometric software GRETL.

Course contents

1. Review of linear regression and ordinary least squares
2. Maximum likelihood theory.
3. Linear time series models: definition, properties, inference, forecast, unit roots.
4. Conditional heteroskedasticity models: definition, properties, inference.
5. Limited dependent variables models.
6. Panel data models.

Readings/Bibliography

M. Verbeek, A guide to modern Econometrics, Wiley 2004.

Teaching methods

For each topic we will first introduce the relevant theory, and then move as soon as possible to its empirical application. Special emphasis will be placed on the economic interpretation of the results.

Assessment methods

The final exam is written and is is composed of two distinct sections.
The first one is mainly theoretical, and it contains 5 multiple choice questions. The second one is mainly empirical, and it contains 11 questions whose answers shoud be computed using gretl and knowledge of the empirical analysis discussed during classes. Whatever the section, each correct answer yields two points; no penalty is applied to wrong answers. The final mark is the total number of point obtained in the two sections.

Teaching tools

We will discuss several empirical analysis and replicate the results of a few papers using the econometric software gretl.

Office hours

See the website of Sergio Pastorello