2023/2024 KAN-CCMVV2413U Python for the Financial Economist
English Title | |
Python for the Financial Economist |
Course information |
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Language | English |
Course ECTS | 7.5 ECTS |
Type | Elective |
Level | Full Degree Master |
Duration | One Semester |
Start time of the course | Autumn |
Timetable | Course schedule will be posted at calendar.cbs.dk |
Max. participants | 104 |
Study board |
Study Board for cand.merc. and GMA (CM)
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Course coordinator | |
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Main academic disciplines | |
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Teaching methods | |
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Last updated on 15-02-2023 |
Relevant links |
Learning objectives | ||||||||||||||||||||||||||||
Use Python and the methods presented in the course and exercises to solve problems similar or slightly different from the problems solved in the exercises of the course. | ||||||||||||||||||||||||||||
Course prerequisites | ||||||||||||||||||||||||||||
The course is oriented towards master-students
with solid quantitative skills and the following background:
1. Master course in portfolio theory 2. Master course in bond and option analysis 3. Undergraduate course in statistics 4. Mathematics course covering optimization and basic matrix algebra. Some experience with scientific computing in coding languages such as Python, R, VBA, Matlab or similar would be an advantage. It will be assumed that students have knowledge about basic concepts (e.g. "for loops" and "if statements") from scientific computing. |
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Examination | ||||||||||||||||||||||||||||
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Course content, structure and pedagogical approach | ||||||||||||||||||||||||||||
The aim of this course is to enable students to implement financial models using realistic data. Several topics in financial economics will be covered and the students will at the end of the course be able to implement financial models from scratch in Python. This will be highly relevant when writing academic papers and/or working in the financial industry.
The teaching format will be different than the traditional teaching format at CBS and requires a high degree of self-motivation and self-management from the students.
The course is very exercise based. The students will learn Python by solving a range of different problems in financial economics. This includes implementation of different models from academic research papers. Examples of potential topics include
In the beginning of the course there will be a general introduction to Python and exercises that familiarise the students with relevant Python functionalities used in the course. |
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Description of the teaching methods | ||||||||||||||||||||||||||||
The course is very exercises based and the main
workload consists of solving exercises using Python.
The objectives of the lectures are two-fold. First, lectures are used to present solutions to the solved exercises which may be pre-recorded videos. This will enable students to assess their ability to solve the exercises. Second, some lectures are held as Q&A sessions that will cover certain topics in more details and where the students can get answers to specific questions. |
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Feedback during the teaching period | ||||||||||||||||||||||||||||
The lectures involve discussions with students
about the solution of pre-assigned problems as well as Q&A
sessions in which students are able to ask and discuss individual
questions and problems.
In addition, students may book individual meetings if needed during weekly office hours which will be held online. |
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Student workload | ||||||||||||||||||||||||||||
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Further Information | ||||||||||||||||||||||||||||
Term papers are not allowed due to the special nature of the course. |
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Expected literature | ||||||||||||||||||||||||||||
No required literature. Notes and exercises introduce relevant material.
Relevant literature:
Yves Hilpisch, Python for Finance Attilio Meucci, Risk and Asset Allocation Claus Munk, Fixed Income Modelling
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