Veranstaltung
Computational Risk and Asset Management [WS192500015]
Dozent/en
Einrichtung
- Institut für Finanzwirtschaft, Banken und Versicherungen
Bestandteil von
- Teilleistung Computational Risk and Asset Management | Wirtschaftsingenieurwesen (M.Sc.)
- Teilleistung Computational Risk and Asset Management | Technische Volkswirtschaftslehre (M.Sc.)
- Teilleistung Computational Risk and Asset Management | Wirtschaftsinformatik (M.Sc.)
- Teilleistung Computational Risk and Asset Management | Informationswirtschaft (M.Sc.)
- Teilleistung Computational Risk and Asset Management | Wirtschaftsmathematik (M.Sc.)
Veranstaltungstermine
- 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)
Anmerkung
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.