Event
Computational Risk and Asset Management [WS192500015]
Lecturers
Organisation
- Institut für Finanzwirtschaft, Banken und Versicherungen
Part of
- Brick Computational Risk and Asset Management | Industrial Engineering and Management (M.Sc.)
- Brick Computational Risk and Asset Management | Economics Engineering (M.Sc.)
- Brick Computational Risk and Asset Management | Information Systems (M.Sc.)
- Brick Computational Risk and Asset Management | Information Engineering and Management (M.Sc.)
- Brick Computational Risk and Asset Management | Economathematics (M.Sc.)
Appointments
- 17.10.2019 09:45 - 11:15 - Room: 20.30 Seminarraum -1.008 (UG)
- 24.10.2019 09:45 - 11:15 - Room: 20.30 Seminarraum -1.008 (UG)
- 31.10.2019 09:45 - 11:15 - Room: 20.30 Seminarraum -1.008 (UG)
- 07.11.2019 09:45 - 11:15 - Room: 20.30 Seminarraum -1.008 (UG)
- 14.11.2019 09:45 - 11:15 - Room: 20.30 Seminarraum -1.008 (UG)
- 21.11.2019 09:45 - 11:15 - Room: 20.30 Seminarraum -1.008 (UG)
- 28.11.2019 09:45 - 11:15 - Room: 20.30 Seminarraum -1.008 (UG)
- 05.12.2019 09:45 - 11:15 - Room: 20.30 Seminarraum -1.008 (UG)
- 12.12.2019 09:45 - 11:15 - Room: 20.30 Seminarraum -1.008 (UG)
- 19.12.2019 09:45 - 11:15 - Room: 20.30 Seminarraum -1.008 (UG)
- 09.01.2020 09:45 - 11:15 - Room: 20.30 Seminarraum -1.008 (UG)
- 16.01.2020 09:45 - 11:15 - Room: 20.30 Seminarraum -1.008 (UG)
- 23.01.2020 09:45 - 11:15 - Room: 20.30 Seminarraum -1.008 (UG)
- 30.01.2020 09:45 - 11:15 - Room: 20.30 Seminarraum -1.008 (UG)
- 06.02.2020 09:45 - 11:15 - Room: 20.30 Seminarraum -1.008 (UG)
Note
The aim of this course is to master real-world challenges of computational risk and asset management and provide students with a skill set to incorporate different portfolio objectives into the investment process. It enables students to solve such challenges independently in Python.
The course covers several topics, 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 180 hours.
Students will build up on the statistics and finance knowledge from their Bachelors program to learn about to automatize modern quant portfolio strategies. Students learn about advanced topics which are relevant for a realistic, real-world asset and risk management process.