Advanced Financial Empirical Research (AFER)

Course description

The course equips students with models and research methods used in empirical finance. The course aims to enhance students’ capability in understanding and assessing prior empirical research in finance, applying the models and methods they have learnt on new problems, and carrying out their own empirical analysis.
Methods to be introduced in the course include panel data models, limited dependent variable regressions, multifactor pricing models and volatility and correlation modeling. The course will focus on the use of these methods for topics including empirical asset pricing, corporate default and credit rating, household financial decisions, and financial market policy effects. This course consists of lectures and several teacher-guided computer-based exercises. Being highly practical, this course prepares students for writing master thesis in finance.

Intended Learning Outcomes
The goal of the course is to deepen students' understanding of models and research methods used in empirical finance research.

Knowledge and understanding
After completing the courses, students shall demonstrate:
1. A deep understanding of the selected empirical issues in finance,
2. A thorough understanding of the important models and research methods in empirical finance.

Skills and abilities
Students shall have the ability to independently
3. Apply the models and methods they have learnt on new problems and to carry out their own empirical analyses,
4. Use scientific methods to analyse quantitative empirical material.

Judgement and approach
Students shall be able to independently
5. Search for relevant information from literature within finance,
6. Evaluate literature within finance.

Please Note !

There will be no new admissions to this course.

Course title:
Advanced Financial Empirical Research
Semester: Autumn term 2019
Study period: 3 4
Rate of studies: 50%
Level: Graduate level (second cycle)
Credits: 7.5
Language of instruction: English


Course coordinator:
Oskar Sjölander
Head of course:
Lu Liu