Portfolio Management II (wird zz. nicht angeboten)

Name of module in english: Portfolio Management II

Exam number: 6708

Semester: From the third semester

Duration of the module: One block

Form of the module (i.e. obligatory, elective etc.): Elective

Frequency of module offer: Every winter semester, usually 1st block

Prerequisites: A successful participation in the module Portfolio Management I is required.

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 approximately 20 minutes and a home assignment, 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):
The course begins with a brief summary of selected content from the module "Portfolio Management I", which taught that the practical application of classical portfolio rules is associated with estimation risks, in some cases considerable ones. We showed that constraints on portfolio weights and resampling methods reduce estimation risk, often significantly improving the out-of-sample performance of portfolio strategies. In this course, based on case studies, you will learn about other fundamental approaches, all of which are characterized by the fact that they can reduce the estimation risk in portfolio management. In addition, each of these approaches addresses particular aspects of portfolio management, for example, factor models address the relationship to capital market theory, the Black-Litterman model addresses subjective expectations, and sparse portfolio optimization addresses the practical need to limit the size and the rebalancing of a portfolio. Introducing these approaches will also enable students to access the state of the art of current research. In this respect, the course is also very well suited to prepare a master thesis in the field of portfolio management.

Contents of the module:

• Estimations risk
• Robust statistics and factor models
• Shrinkage estimators
• Sparse portfolio optimization
• Multistage portfolio optimization
• Combined portfolio rules
• Bayesian portfolio optimization (Black-Litterman model)

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.

Further information:
Registration in Moodle Viadrina required.