NMAK14022U Statistics for non-linear time series models (AAM)
MSc Programme in Mathematics
MSc Programme in Statistics
MSc Programme in Mathematics-Economics
Statistics for non-linear time series models
Time series are stochastic processes sampled at discrete instants
of time; they are observed in nature, economy and society. Time
series are available in abundance in the form of returns of
financial assets (as high and low frequency data) ,as time series
related to the Internet such as file sizes, length of transmission
duration of files, time series of medical oberservations on
patients, etc.
What these time serie have in common is that they are typically of
non-linear
structure, i.e. the present observation does not depend in a linear
way on the past oberservations. The course aims at providing
statistical tools for non-linear time series in particular on
volatility models. The GARCH, exponential GARCH, stochastic
volatility models belong to this class. They are major models in
econometrics and financial time series analysis. We will study
parameter
estimation, the selection of models and prediction with application
to quantitative risk measures. During the course, the theory will
be applied to real-life data in order to show the strength and the
limitations of the methods.
Knowledge:
At the end of the course the student will be familiar with modern
estimation techniques, model selection methods and prediction
technology for non-linear time series models, in particular
for financial time series models.
Skills:
The student will be able to fit real-life data to parametric
non-linear processes, in particular common financial time series
models such as GARCH and stochastic volatility models. He/she will
be able to apply standard software (R) to achieve the goals.
Competences:
The student will learn about the advantages and limitations of
non-linear
time series models. He/she will be able to read textbooks and
monographs on the topic in the fields of econometrice, time series
analysis, statistics and actuarial science.
Lecture notes will be provided.
- Category
- Hours
- Exam
- 66
- Lectures
- 35
- Theory exercises
- 105
- Total
- 206
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- Credit
- 7,5 ECTS
- Type of assessment
- Continuous assessmentContinuous assessment consisting of a final oral examination (30 minutes without preparation time and without aids) and two take home exams.
The oral examination counts for 70% of the grade. The remaining 30% correspond to a Mid Term (15%) and a Final Term Test (15%). In these take home tests, the student will solve some theoretical problems and get statistical expertise on simulated and real-life data, mostly from insurance and finance applications. The student must receive more than 50% of the marks for each of the Mid Term and the Final Term Tests. Otherwise the grade for the course is -3. - Aid
- Without aids
The oral final exam is without aids. All aids are allowed for the two tests.
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners at the oral exam, one internal examiner at the take home exams.
- Re-exam
- Oral examination (30 minutes) with internal censor without preparation time and notes. The student is admitted to the re-examination if he/she has received more than 50% of the marks in both the Mid Term and the Final Term Tests. If this has not been achieved at the time of the first examination the student may resubmit the two tests before the re-examination.
Criteria for exam assesment
The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.
Course information
- Language
- English
- Course code
- NMAK14022U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 4
- Schedule
- A (Tues 8-12 + Thurs 8-17)
- Course capacity
- No limit
- Continuing and further education
- Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Mathematical Sciences
Course responsibles
- Thomas Valentin Mikosch (7-6f6b6d7175656a426f63766a306d7730666d)
Lecturers
Olivier Winterberger