Advanced Financial and Macro Econometrics (F)
Course content
The course introduces selected topics from research in
multivariate time series econometrics with applications to finance
and macroeconomics. For each topic, the econometric theories are
discussed and illustrated by empirical applications.
Topics include theory and application of:
- Co-integration in vector autoregressive (VAR) models with application to e.g. term-structure models with non-stationary driving trends and portfolio strategies based on pairs-trading.
- Multivariate models with autoregressive conditional heteroscedasticity (ARCH) with applications to portfolio selection and risk assessments.
- Static and dynamic models for asses pricing. This includes the capital asset pricing model (CAPM), the asset pricing theory (APT) model, as well as extensions allowing time-varying conditional betas.
- Bootstrap based testing in the financial and macro-econometric contexts above.
MSc programme
in Economics – elective course
The PhD Programme in Economics at the Department of Economics:
- The course is an elective course with research module. In order to register for the research module and to be able to write the research assignment, the PhD students must contact the study administration AND the lecturer.
- The course is a part of the admission requirements for the 5+3 PhD Programme. Please consult the 5+3 PhD admission requirements.
The course is open to:
- Exchange and Guest students from abroad
- Credit students from Danish Universities
- Open University students
After completing the course the student is expected to be able to:
Knowledge:
- Account for the theory for co-integrated VAR models, including the role of deterministic terms in the model, interpretation of the driving stochastic trends, and hypothesis testing and identification in the model.
- Account for the application of the co-integrated VAR model to macroeconomics and finance and the interpretation of the results.
- Account for the theory for multivariate ARCH models, including necessary restrictions for positive definiteness of the time varying covariance, and discuss advantages and drawbacks of different model formulations.
- Account for the application of multivariate ARCH models within the area of portfolio selection and risk assessment.
- Account for the theory for factor models and applications within asset pricing. This includes a detailed discussion of the underlying assumptions, and the restrictions implied by the assumption of no-arbitrage.
- Account for bootstrap-based inference.
Skills:
- Construct co-integrated VAR models and test assumptions for valid inference.
- Perform inference withint the co-integrated VAR model, including determination of the co-integration rank, hypotheses testing on the structure of the model, and identification the co-integration relationships.
- Construct and estimate multivariate ARCH models based on a suitable parametrization.
- Apply the time varying conditional covariance matrix for portfolio optimization and risk assessments.
- Use factor models for empirical asset pricing, and test restrictions implied by no-arbitrage.
- Implement simple bootstrap algorithms.
- Critically evaluate research papers containing econometric time series analyses.
- Identify and analyze the characteristic properties of economic time series data
Competences:
- Apply the acquired knowledge and skills independently in later employment in either public or private institutions.
- Master and implement relevant statistical models and solutions in new and complex contexts.
Lectures and exercise classes.
The course is based on selected journal articles and lecture notes.
Supplementary reading:
Francq, C. and J. M. Zakoian, GARCH Models: Structure, Statistical Inference and Financial Application, 2nd edition, Wiley, 2019.
Taylor, S.J., Asset Price Dynamics, Volatility and Prediction, Princeton University Press, 2005.
Tsay, R., Analysis of Financial Time Series, Wiley, 2005.
It is strongly recommended to have followed the course
Econometrics II at the Study of Economics, University of
Copenhagen, or equivalent prior taking ”Advanced Financial and
Macro Econometrics”.
Knowledge of theory in financial econometrics equivalent to that
achieved in "Financial Econometrics A" at the Study of
Economics, University of Copenhagen, or equivalent is
recommended.
Schedule:
2 hours lectures one to two times a week from week 6 to 20.
2 hours exercise classes from week 6 or 7 to 20.
- The students receive oral collective feedback from quizzes on the content of the lectures.
- Each student receive written feedback on the mandatory assignments from the teaching assistants
- The teaching assistant gives oral collective feedback on the written assignment.
For enrolled students: Rules etc at Master(UK) and Master(DK)
When registered you will be signed up for exam.
- Full-degree students – sign up at Selfservice on KUnet
- Exchange and guest students from abroad – sign up through Mobility Online and Selfservice- read more through this website.
- Credit students from Danish universities - sign up through this website.
- Open University students - sign up through this website.
The dates for the exams are found here Exams – Faculty of Social Sciences - University of Copenhagen (ku.dk)
Please note that it is your own responsibility to check for overlapping exam dates.
- ECTS
- 7,5 ECTS
- Type of assessment
-
Home assignment, 12 hours
- Type of assessment details
- Individual home assignment. Max 10 standard pages.
It is not allowed to collaborate on the assignment with anyone. - Examination prerequisites
-
To qualify for the exam the student must no later than the given deadlines during the course:
- Hand in and have approved 3 out of 3 mandatory assignments.
- The assignments must be handed in individually.
- Aid
- All aids allowed
for the home assignment.
Use of AI tools is permitted. You must explain how you have used the tools. When text is solely or mainly generated by an AI tool, the tool used must be quoted as a source.
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
for the written exam.
An oral re-examination may be with external assessment. - Exam period
-
Exam information:
The examination date can be found in the exam schedule here
More information is available in Digital Exam from the middle of the semester.
More information about examination, rules, aids etc. at Master (UK) and Master (DK).
- Re-exam
-
20 minutes oral examination with 20 minutes preparation time.
Written aids allowed during the preparation time.
No aids allowed during the examination.
Reexam information:
The reexamination date/period can be found in the reexam schedule here
More information in Digital Exam in August.
More information at Master UK) and Master DK).
Criteria for exam assessment
Students are assessed on the extent to which they master the learning outcome for the course.
In order to obtain the top grade “12”, the student must with no or only a few minor weaknesses be able to demonstrate an excellent performance displaying a high level of command of all aspects of the relevant material and can make use of the knowledge, skills and competencies listed in the learning outcomes.
In order to obtain the passing grade “02”, the student must in a satisfactory way be able to demonstrate a minimal acceptable level of the knowledge, skills and competencies listed in the learning outcomes.
Single subject courses (day)
- Category
- Hours
- Lectures
- 42
- Class Instruction
- 28
- Preparation
- 124
- Exam
- 12
- English
- 206
Kursusinformation
- Language
- English
- Course number
- AØKK08398U
- ECTS
- 7,5 ECTS
- Programme level
- Full Degree Master
Ph.D.
- Duration
-
1 semester
- Placement
- Spring
- Price
-
Information about admission and tuition fee: Master and Exchange Programme, credit students and guest students (Open University)
- Studyboard
- Department of Economics, Study Council
Contracting department
- Department of Economics
Contracting faculty
- Faculty of Social Sciences
Course Coordinators
- Heino Bohn Nielsen (18-7673777c7d3c707d767c3c7c77737a81737c4e73717d7c3c79833c7279)
- Anders Rahbek (13-6a776d6e7b7c377b6a716b6e74496e6c787737747e376d74)
Teacher
See "Course Coordinators".
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Kursusinformation for indskrevne studerende