AØKA08230U Financial Econometrics A (F)

Volume 2019/2020
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.

Content

The course gives an introduction to the properties and stylized facts of univariate asset returns and their variability with emphasis on modeling of (conditional) volatility. We consider three different modeling approaches to volatility: (1) GARCH-type models, (2) stochastic volatility models (SV), and (3) realized volatility (RV).

 

The stochastic properties of the processes are analyzed and discussed in detail using mathematical statistical methods. A key tool for the analysis of GARCH-type and SV models is the so-called drift criterion for Markov chains.

 

Estimation of volatility and volatility models will be based primarily on (quasi) maximum likelihood. This includes applications of the EM-algorithm as well as the Kalman filter.

 

The theoretical properties of the estimators are analyzed and sufficient conditions for asymptotic normality are stated and verified.

 

All estimation is carried out in OxMetrics 7.0 and the students are expected to do some amount of coding using the Ox programming language.

 

The goodness of fit of the models are discussed based on analysis of the model residuals.

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 (under suitable conditions) that the likelihood-based estimators are asymptotically normal.

  • Implement the estimation of volatility models using the Ox language.

  • 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.

  • Idealy read leading and novel journal articles within financial econometrics.

Literature

The course will be based on 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).
Lectures and exercise classes.
During the semester mandatory assignments must be handed in and not later than the given deadline.
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 "Se skema" (See schedule) 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/​ku1920/​uk/​module.htm
-Select Department: “2200-Økonomisk Institut” (and wait for respond)
-Select Module:: “2200-E19; [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 accept. 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.
  • Category
  • Hours
  • Class Exercises
  • 28
  • Exam
  • 3
  • Lectures
  • 42
  • Preparation
  • 133
  • Total
  • 206
Credit
7,5 ECTS
Type of assessment
Written examination, 3 hours under invigilation
The exam assignment is given in English and must be answered in English.
____
Exam registration requirements

3 out of 3 mandatory assignments must be approved to be able to sit the exam.

____

Aid
Without aids
Marking scale
7-point grading scale
Censorship form
No external censorship
for the written exam. The exam may be chosen for external censorship by random check.
____
Exam period

The exam takes place in the exam venues of the university:

14 January 2020

 

Exam information:

The exact time and room will be available in the Digital Exam from the middle of the semester.

 

In special cases, the exam date can be changed to another day and time within the exam period.

 

For enrolled students more information about examination, rules etc. is available at the intranet for Master students (UK), Master students (DK) and Bachelor students (DK).

____

Re-exam

The written reexam take place in the exam venues of the university:

17 February 2020

 

NOTE: If only few students register for the written re-exam, the re-exam might change to a 20 minutes oral examination with 20 minutes preparation time. All written aids allowed during the preparation time. No aids allowed during the examination. If changed to an oral re-exam, the exam date, time and place might change as well. The Examination's Office then inform the students by KU e-mail.

 

Reexam info:

Info is available in Digital Exam early February.

More info at Master(UK), Master(DK) and Bachelor(DK).

Criteria for exam assesment

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.