Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
3 credits
15.0 h
Q1
This learning unit is not being organized during year 2019-2020.
Teacher(s)
Segers Johan;
Language
English
Prerequisites
Basic univariate and multivariate statistics. Working knowledge of the R language for statistical computing.
Main themes
The course focuses on copulas and their use in modelling dependence between random variables. Both theoretical and practical aspects will be covered.
Aims
At the end of this learning unit, the student is able to : | |
| 1 |
By the end of the course, the student will have a working knowledge on copula models and their use in modelling dependence between random variables. He will be able to select, calibrate, and validate a copula model and use the fitted model to answer questions related to multivariate data: calculation of risk measures, prediction, decision making. |
The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
Content
1 Fundamentals : Cumulative distribution functions, Sklar’s theorem, various copula properties, densities and conditional distributions, Measures of association
2 Models: Archimedean copulas, extreme-value copulas, elliptical copulas
3 Inference: Nonparametric inference via the empirical copula, parametric inference via measures of association and likelihood-based parametric inference
The course provides a mixture of theory, parametric models, and implementation in R.
2 Models: Archimedean copulas, extreme-value copulas, elliptical copulas
3 Inference: Nonparametric inference via the empirical copula, parametric inference via measures of association and likelihood-based parametric inference
The course provides a mixture of theory, parametric models, and implementation in R.
Teaching methods
During the lectures, the teacher motivates and introduces the main concepts. The students then work independently or in groups to solve the questions in the text. In the meantime, the teacher interacts with the students personally helping them advance at their own pace.
Evaluation methods
Intermediate tests (8/20)
There will be two tests, the first one on chapter 1 and the second one on chapter 2. The tests will take place during the lectures. The tests are compulsary. Each test will count for 4 points of the final grade, so 8 points out of 20 (40%) in total. The questions will concern the exercises of type ‘M’ (calculations related to particular models) in the lecture notes. The tests are closed-book.
Project and oral exam (12/20)
The material of chapter 3 will be examined via a project assignment. Students will be required to analyze aspects of dependence in a dataset using techniques covered in the course. They describe the analysis and its results in a short text. Specific instructions will be communicated at the start of chapter 3. This text and any supplementary files are to be submitted via MoodleUCL by the start of the exam period.
The oral examination will center around the project submitted by the student.
There will be two tests, the first one on chapter 1 and the second one on chapter 2. The tests will take place during the lectures. The tests are compulsary. Each test will count for 4 points of the final grade, so 8 points out of 20 (40%) in total. The questions will concern the exercises of type ‘M’ (calculations related to particular models) in the lecture notes. The tests are closed-book.
Project and oral exam (12/20)
The material of chapter 3 will be examined via a project assignment. Students will be required to analyze aspects of dependence in a dataset using techniques covered in the course. They describe the analysis and its results in a short text. Specific instructions will be communicated at the start of chapter 3. This text and any supplementary files are to be submitted via MoodleUCL by the start of the exam period.
The oral examination will center around the project submitted by the student.
Other information
The course is bi-annual and will not be given in 2019-2020.
Online resources
The course text is available on the MoodleUCL course page.
Bibliography
Teaching material
- Syllabus "LSTAT2410 - Copulas: models and inference" (J. Segers)
Teaching materials
- syllabus on moodle
Faculty or entity
LSBA
