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.
5 credits
30.0 h
Q1
Teacher(s)
Candelon Bertrand;
Language
English
Prerequisites
You should have a knowledge of basic topics in statistics, econometrics and finance such as those covered in the following courses:
Fundamental mathematical and statistical concepts (such as those covered in Mathématiques avancées et fondements d'économétrie [ LECGE1337 ])
Advanced Finance [LLSMS2100A or LLSMS2100B]
In addition, this course is reserved for students with a bachelor's degree in business engineering or students with equivalent quantitative method skills
Fundamental mathematical and statistical concepts (such as those covered in Mathématiques avancées et fondements d'économétrie [ LECGE1337 ])
Advanced Finance [LLSMS2100A or LLSMS2100B]
In addition, this course is reserved for students with a bachelor's degree in business engineering or students with equivalent quantitative method skills
Main themes
This course overviews topics in computational finance and financial econometrics (data sciences applied to finance).
The emphasis of the course will be on making the transition from an economic model of asset return behavior to an econometric model using real data.
This involves:
Both edX and DataCamp plateforms will be used to allow practical training and continuous learning on R.
The emphasis of the course will be on making the transition from an economic model of asset return behavior to an econometric model using real data.
This involves:
- exploratory data analysis;
- specification of models to explain the data;
- estimation and evaluation of models;
- testing the economic implications of the model;
- forecasting from the model.
Both edX and DataCamp plateforms will be used to allow practical training and continuous learning on R.
Aims
At the end of this learning unit, the student is able to : | |
| 1 |
Upon completion of this course, students are expected to complete the following key tasks:
3. Knowledge and reasoning; 4. Critical thinking skills. |
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
The following topics will be covered:
- Introduction to R manipulation and programming (1x3h)
- Expected utility framework and modern portfolio theory using R (3x3h)
- Refresher on basic econometrics and linear regression (1x3h)
- TS topics (including volatility modelling) (3x3h)
- GMM estimation applied to asset pricing (1x3h)
Faculty or entity
CLSM
