Name of module in english: Portfolio Management I
Exam number: 6655
Semester: From the 2nd semester
Duration of the module: One block
Form of the module (i.e. obligatory, elective etc.): Elective
Frequency of module offer: Every summer semester, usually 1st block
Prerequisites: Students must have a basic understanding of matrix algebra and statistics. The ability to program in the R language is strongly recommended.
Applicability of module for other study programmes:
Obligatory or elective in other study programmes. For further information check regulations of the study programme.
Person responsible for module: Prof. Dr. Sven Husmann
Name of the professor: Prof. Dr. Sven Husmann
Language of teaching: English
ECTS-Credits (based on the workload): 6
Workload and its composition (self-study, contact time):
Contact time: 34 h, self-study: 146 h
Contact hours (per week in semester): 3
Methods and duration of examination:
Oral exam of 10-15 minutes (25%) and a home assignment (75%), both on the same day.
Emphasis of the grade for the final grade: Please check the regulations of the study programs.
Aim of the module (expected learning outcomes and competencies to be acquired):
From a scientific perspective, the core of portfolio management is to provide evidence that one investment strategy is superior to another. Participants in this course will acquire and deepen this competence by working on various case studies, always using the programming language R. In so doing, participants will experience that the estimation of model parameters and the associated estimation risks are the central problem of portfolio management in practice. Neglecting these estimation risks can significantly distort the expected performance of the portfolio strategies applied. In addition to this methodological competence, the participants learn about all classical portfolio strategies. Thereby, we focus on numerical optimization methods. At the end of the course, participants of this course will therefore be able to optimize portfolios under conditions common in practice.
Contents of the module:
- Comparing Portfolio Strategies
- Estimation Risk
- The R Optimization Infrastructure
- The Efficient Frontier
- Maximizing the Sharpe Ratio
- Estimation of Model Parameters
- Sustainable Portfolio Management
Teaching and learning methods:
Online lectures, online exercises, case studies, self-study. You need a computer with a webcam and a microphone to participate in this course.
Literature (compulsory reading, recommended literature):
Literature will be announced in the Moodle course.
Registration in Moodle Viadrina required.