Financial Econometrics A (F)

Course content

The teaching in this course may be changed to be taught either fully or partly online due to COVID-19. For further information, see the course room on Absalon. The time, place and type of assessment used for the exam may also be changed due to COVID-19, and any further information will be announced under the panel “Exam”.

The course gives an introduction to the econometric analysis of asset returns, with emphasis on econometric modeling of time-varying volatility. Various applications are considered, including risk management and derivatives pricing.

We consider three different econometric approaches to time-varying volatility: (1) GARCH models, (2) Stochastic Volatility models (SV), and (3) Realized Volatility (RV).

The stochastic properties of the processes are analyzed in detail, using new statistical theory such as applying the so-called drift criterion. Estimation of time-varying volatility will primarily be likelihood-based, including non-linear optimization and filtering methods. Econometric analysis is given of the estimators, and sufficient conditions for asymptotic normality are derived.

All modeling is illustrated empirically using standard software packages as OxMetrics 8.0. In addition, an introduction is given to simple programming as needed for implementation of e.g. risk-measures.

Education

MSc programme in Economics – elective course

Bacheloruddannelsen i økonomi – valgfag på 3. år

The Danish BSc programme in Economics - elective at the 3rd year

The course is part of the Financial line at the MSc programme in Economics,   symbolized by ‘F’.

MSc programme in mathematics-economics

Course code AØKA08230U replaces AØKA08216U. There is no changes in the course at all.

Learning outcome

After completing the course the student is expected to be able to:

Knowledge

• Account for properties of stochastic processes used for volatility modelling. This includes strict stationarity, mixing, and geometric ergodicity.

• Account for properties of maximum likelihood estimators in volatility modelling.

• Account for properties of Realized Volatility (RV) processes, including continuous-time processes.

• Account for applications of volatility models, including Value-at-Risk (VaR), option pricing, and forecasting.

Skills

• Analyze stochastic properties (e.g. weak mixing and finite moments) time series proceses, such as AR and ARCH. This includes verifying a drift criterion.

• Show that the likelihood-based estimators are asymptotically normal, and clarify under what conditions such a property holds.

• Implement the estimation of volatility models.

• Implement the estimation of volatility in relation to for example VaR analysis, forecasting, and option pricing.

• Analyze the properties of continuous time processes and show how to estimate their quadratic variation consistently.

• Discuss the suitability of a given (G)ARCH, SV, or continuous time process given well-known stylized facts about financial returns.

Competencies

• Apply the acquired knowledge and skills in new contexts. For example the student should be able to analyze richer classes of models (such as multidimensional) and carry out estimation of these. Another example is to apply the acquired knowledge when considering linear regression models with financial time series data.

Lectures, active dialog, hands-on empirical applications, derivations on blackboard, exercise classes, mandatory assignments with theoretical and empirical content, coding and implementation of models and estimation approacthes.

The course will be based on

• R.S. Pedersen and A. Rahbek (2020), “Lecture notes on Econometric Analysis of Time-Varying Volatility”, University of Copenhagen.

• S. J. Taylor, Asset Price Dynamics, Volatility and Prediction, Princeton University Press, 2007 or 2005 edition (ISBN: 9781400839254), as well as lecture notes handed out during term.

Various journal articles.

The knowldege obtained from Econometrics II before or at the same time the Financial Econometrics A is taken or an equivalent course on introductory time series analysis.

In particular, the student should be familiar with:
1. Linear time series models, such as AR and ARMA.
2. Likelihood-based estimation of linear time series models, including the basic properties of the estimators.
3. Basic misspecification tests in time series models (tests for no-autocorrelation, no-ARCH, and normality).

Schedule:
2 hours lectures 1 to 2 times a week from week 36 to 50 (except week 42).
2 hours exercise classes a week from week 36/37 to 50 (except week 42).

Schema:
The overall schema for the BA 3rd year and Master can be seen at KUnet:
MSc in Economics => "Courses and teaching" => "Planning and overview" => "Your timetable"
BA i Økonomi/KA i Økonomi => "Kurser og undervisning" => "Planlægning og overblik" => "Dit skema"

Timetable and venue:
To see the time and location of lectures and exercise classes please press the link/links under "Timetable"/​"Se skema" at the right side of this page. E means Autumn. The lectures is shown in each link.

You can find the similar information partly in English at
https:/​/​skema.ku.dk/​ku2021/​uk/​module.htm
-Select Department: “2200-Økonomisk Institut” (and wait for respond)
-Select Module:: “2200-E20; [Name of course]””
-Select Report Type: “List – Weekdays”
-Select Period: “Efterår/Autumn – Weeks 31-5”
Press: “ View Timetable”

Please be aware regarding exercise classes:
- The schedule of the exercise classes is only a pre-planned schedule and can be changed until just before the teaching begins without the participants´ acceptance. If this happens it will be informed at the intranet or can be seen in the app myUCPH and at the above link
- That the study administration allocates the students to the exercise classes according to the principles stated in the KUnet.
- If too many students have wished a specific class, students will be registered randomly at another class.
- It is not possible to change class after the second registration period has expired.
- If there is not enough registered students or available teachers, the exercise classes may be jointed.
- The student is not allowed to participate in an exercise class not registered, because the room has only seats for the amount of registered student.
- The teacher of the exercise class cannot correct assignments from other students than the registered students in the exercise class except with group work across the classes.
- That all exercise classes will be taught in English.

Oral
Individual
Collective

Feedback is obtained throughout the semester by:

• the lecturer answering questions in class,
• the lecturer giving oral feedback on written questions from groups,
• the teaching assistant giving oral feedback on written exercises in exercise classes.
ECTS
7,5 ECTS
Type of assessment
Written assignment, 3 hours
The exam assignment is given in English and must be answered in English.
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Due to the Corona crisis, the Board of Study has decided to change the regular exams and the re-sit exams in the autumn semester to written online take-home exams. Both exams are still individual.

During the exam, it is not allowed to communicate with others about the exam assignment nor the solution at all. It is also prohibited to distribute data regarding the solutions to anyone. If this or alike actions happens, it will be regarded as cheating and plagiarism.

- https:/​/​kunet.ku.dk/​newsroom/​study-messages/​Pages/​Information-on-rescheduled-PBV-winter-exams-.aspx
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Aid
All aids allowed
Marking scale
Censorship form
No external censorship
for the written exam. The exam may be chosen for external censorship by random check.
____
Criteria for exam assessment

Students are assessed on the extent to which they master the learning outcome for the course.

To receive the top grade, 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 pass the exam in this course the student is required to demonstrate understanding of the material covered in the course. This may include the ability to analyze the stochastic properties of a time serie processes and describe how a given model should be estimated.

Single subject courses (day)

• Category
• Hours
• Lectures
• 42
• Class Instruction
• 28
• Preparation
• 133
• Exam
• 3
• English
• 206