ESF:MPF_APFE Applied Financial Econometrics - Course Information

MPF_APFE Applied Financial Econometrics

Extent and Intensity

0/2/0. 5 credit(s). Type of Completion: k (colloquium).
In-person direct teaching

Teacher(s)

doc. Ing. Tomáš Plíhal, Ph.D. (seminar tutor)

Guaranteed by

doc. Ing. Tomáš Plíhal, Ph.D.
Department of Finance – Faculty of Economics and Administration
Contact Person: Iva Havlíčková
Supplier department: Department of Finance – Faculty of Economics and Administration

Prerequisites

BPE_ZAEK Introduction to Econometrics || BPE_INEC Introduction to Econometrics
Students are expected to be familiar with basic concepts of Corporate finance, Statistics and Econometrics

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

there are 7 fields of study the course is directly associated with, display

Course objectives

This course provides a structured environment for students to apply econometric methods to empirical problems in finance. It is designed to support the development of students’ diploma theses or comparable empirical projects by offering ongoing guidance, peer feedback, and clearly defined checkpoints. Students will work independently during weekly seminars, with the instructor providing individualized support and occasional group clarifications as common issues arise. The course assumes prior knowledge of statistics and econometrics, and it is recommended that students have completed the thesis assignment course before enrollment.

Learning outcomes

After completing the course, the student should be able to:
- Apply econometric techniques to real-world financial datasets.
- Develop and refine a research question in financial econometrics.
- Manage a data-driven empirical project.

Syllabus

• The following key elements of empirical research in financial econometrics will be addressed through individual work and guided consultations: topic selection, literature review, data acquisition, and model estimation.
• - Initial project formulation: research question, motivation, and scope
• - Review of relevant empirical literature and methodological choices
• - Financial data sources: market data, firm-level data, and macro-financial indicators
• - Data preparation and preprocessing: cleaning, merging, and transformation
• - Specification of econometric models suitable for financial applications
• - Estimation techniques and preliminary results
• - Diagnostic testing and robustness checks
• - Interpretation of results in financial and economic context
• - Finalization of results and writing of empirical research report or thesis chapter

Literature

recommended literature
• LEVENDIS, John D. Time Series Econometrics: Learning through replication. Springer, 2018. ISBN 3-319-98281-8. info
• BERNARDI, M and Catania L AMP. The model confidence set package for R. International Journal of Computational Economics and Econometrics. 2018, vol. 8, No 2, p. 144-158. Available from: https://dx.doi.org/10.1504/IJCEE.2018.091037. info
• CAMPBELL, J W and A W MacKinlay A C LO. The econometrics of financial markets. Princeton University press, 1997. ISBN 0-691-04301-9. info

not specified
• JAMES, Gareth R.; Daniela WITTEN; Trevor HASTIE and Robert TIBSHIRANI. An introduction to statistical learning : with applications in R. Second edition. New York: Springer, 2021, xv, 607. ISBN 9781071614174. info
• LINTON, O. Financial econometrics: Models and Methods. Cambridge University Press, 2019. ISBN 978-1-316-63033-4. info

Teaching methods

Seminar-based teaching focused on individual student work on an empirical project under the supervision of the instructor.
- Regular in-class consultations, individual feedback, and shared discussions when common issues arise.
- Progress monitored through periodic submissions (checkpoints) and instructor comments.

Assessment methods

Assessment is based on continuous progress and active participation. Requirements for passing the course include:
– Minimum 80% attendance at seminar sessions.
– Timely submission of progress reports at designated checkpoints.
– Final presentation summarizing the results and progress made during the semester.
- The course is graded on a pass/fail basis (colloquium).

Náhradní absolvování

In case of a study stay abroad, it is possible to complete the course in an alternative form. Please contact the instructor to arrange an individual study plan.

Language of instruction

English

Study support

https://is.muni.cz/auth/go/2thnyb

Further Comments

Study Materials
The course is taught annually.
The course is taught every week.

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