Event
Python for Empirical Finance [WS192500014]
Lecturers
Organisation
- Institut für Finanzwirtschaft, Banken und Versicherungen
Part of
Appointments
- 17.10.2019 15:45 - 17:15 - Room: 20.30 Seminarraum -1.009 (UG)
- 24.10.2019 15:45 - 17:15 - Room: 20.30 Seminarraum -1.009 (UG)
- 31.10.2019 15:45 - 17:15 - Room: 20.30 Seminarraum -1.009 (UG)
- 07.11.2019 15:45 - 17:15 - Room: 20.30 Seminarraum -1.009 (UG)
- 14.11.2019 15:45 - 17:15 - Room: 20.30 Seminarraum -1.009 (UG)
- 21.11.2019 15:45 - 17:15 - Room: 20.30 Seminarraum -1.009 (UG)
- 28.11.2019 15:45 - 17:15 - Room: 20.30 Seminarraum -1.009 (UG)
- 05.12.2019 15:45 - 17:15 - Room: 20.30 Seminarraum -1.009 (UG)
- 12.12.2019 15:45 - 17:15 - Room: 20.30 Seminarraum -1.009 (UG)
- 19.12.2019 15:45 - 17:15 - Room: 20.30 Seminarraum -1.009 (UG)
- 09.01.2020 15:45 - 17:15 - Room: 20.30 Seminarraum -1.009 (UG)
- 16.01.2020 15:45 - 17:15 - Room: 20.30 Seminarraum -1.009 (UG)
- 23.01.2020 15:45 - 17:15 - Room: 20.30 Seminarraum -1.009 (UG)
- 30.01.2020 15:45 - 17:15 - Room: 20.30 Seminarraum -1.009 (UG)
- 06.02.2020 15:45 - 17:15 - Room: 20.30 Seminarraum -1.009 (UG)
Note
The aim of this course is to provide students with strong knowledge in Python to independently solve real-world data problems related to computational risk and asset management.
The course covers several topics from a programming perspective, among them:
Mean-Variance Portfolio Optimization
Modeling Distribution of Asset Returns with Factor Models and ARMA-GARCH
Monte-Carlo Simulation
Parameter Estimation with Maximum Likelihood and Regressions
The course introduces students to Python, one of the most popular high-level programming languages in data analytics. After an introduction to the basic concepts, students will soon begin to solve problems related to the agenda of the lecture 'Empirical Finance'. This enables them to work with financial data, perform various statistical analysis and estimate their own time series models.