261-5111-00L  Asset Management: Advanced Investments (University of Zurich)

SemesterSpring Semester 2020
LecturersUniversity lecturers
Periodicityyearly recurring course
Language of instructionEnglish
CommentNo enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: MFOEC207

Mind the enrolment deadlines at UZH:


261-5111-00 VAsset Management: Advanced Investments (University of Zurich)
**Course at University of Zurich**
2 hrsUniversity lecturers

Catalogue data

AbstractComprehension and application of advanced portfolio theory
ObjectiveComprehension and application of advanced portfolio theory
ContentThe theoretical part of the lecture consists of the topics listed below.

- Standard Markowitz Model and Extensions MV Optimization, MV with Liabilities and CAPM.
- The Crux with MV
Resampling, regression, Black-Litterman, Bayesian, shrinkage, constrained and robust optimization.
- Downside and Coherent Risk Measures
Definition of risk measures, MV optimization under VaR and ES constraints.
- Risk Budgeting
Equal risk contribution, most diversified portfolio and other concentration indices
- Regime Switching and Asset Allocation
An introduction to regime switching models and its intuition.
- Strategic Asset Allocation
Introducing a continuous-time framework, solving the HJB equation and the classical Merton problem.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits3 credits
Typegraded semester performance
Language of examinationEnglish
RepetitionRepetition only possible after re-enrolling for the course unit.
Additional information on mode of examinationRegistration modalities, date and venue of this performance assessment are specified solely by UZH.

Learning materials

No public learning materials available.
Only public learning materials are listed.


No information on groups available.


There are no additional restrictions for the registration.

Offered in

Data Science MasterInterdisciplinary ElectivesWInformation