Event
Python for Computational Risk and Asset Management [WS192500016]
Lecturers
Organisation
- Institut für Finanzwirtschaft, Banken und Versicherungen
Part of
- Brick Python for Computational Risk and Asset Management | Industrial Engineering and Management (M.Sc.)
- Brick Python for Computational Risk and Asset Management | Economics Engineering (M.Sc.)
- Brick Python for Computational Risk and Asset Management | Information Systems (M.Sc.)
- Brick Python for Computational Risk and Asset Management | Information Engineering and Management (M.Sc.)
- Brick Python for Computational Risk and Asset Management | Economathematics (M.Sc.)
Appointments
- 17.10.2019 15:45 - 17:15 - Room: 20.30 Seminarraum -1.017 (UG)
- 24.10.2019 15:45 - 17:15 - Room: 20.30 Seminarraum -1.017 (UG)
- 31.10.2019 15:45 - 17:15 - Room: 20.30 Seminarraum -1.017 (UG)
- 07.11.2019 15:45 - 17:15 - Room: 20.30 Seminarraum -1.017 (UG)
- 14.11.2019 15:45 - 17:15 - Room: 20.30 Seminarraum -1.017 (UG)
- 21.11.2019 15:45 - 17:15 - Room: 20.30 Seminarraum -1.017 (UG)
- 28.11.2019 15:45 - 17:15 - Room: 20.30 Seminarraum -1.017 (UG)
- 05.12.2019 15:45 - 17:15 - Room: 20.30 Seminarraum -1.017 (UG)
- 12.12.2019 15:45 - 17:15 - Room: 20.30 Seminarraum -1.017 (UG)
- 19.12.2019 15:45 - 17:15 - Room: 20.30 Seminarraum -1.017 (UG)
- 09.01.2020 15:45 - 17:15 - Room: 20.30 Seminarraum -1.017 (UG)
- 16.01.2020 15:45 - 17:15 - Room: 20.30 Seminarraum -1.017 (UG)
- 23.01.2020 15:45 - 17:15 - Room: 20.30 Seminarraum -1.017 (UG)
- 30.01.2020 15:45 - 17:15 - Room: 20.30 Seminarraum -1.017 (UG)
- 06.02.2020 15:45 - 17:15 - Room: 20.30 Seminarraum -1.017 (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 automated robo investment advisory.
The course covers several topics from a programming perspective, among them:
Quantitative Portfolio Strategies: Extensions to Mean-Variance Portfolio Optimization
Return Densities: Forecasting with Traditional and Machine Learning Approaches, Monte Carlo Simulation
Financial Economics: Rationalizing Risk Premiums via Stochastic Discount Factor
Multi-Asset Valuation: DCF Approach, No-Arbitrage and Ito Calculus
The total workload for this course is approximately 90 hours.
Prior knowledge of AIFB programming and KIT statistics classes is recommended.
The course introduces students to Python. Students will solve problems related to the agenda of the lecture 'Computational Risk and Asset Management'. This enables them to work with financial data, perform various statistical analysis and estimate their own time series models.