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Computational Optimization in Finance (R-Module)

Exam number: 6769

Semester: from 2nd semester

Duration of the module: Second block

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

Frequency of module offer: Every two or three semesters

Prerequisites: Previous or simultaneous participation in the track modules “Introduction to Portfolio Management with R” or “Decision Support under Uncertainty” or “Methods of IOM”, good knowledge and deep interest in the programming language R, mathematical modeling and quantitative methods.

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, Prof. Dr. Achim Koberstein

Name of the professor: Prof. Dr. Sven Husmann, Prof. Dr. Achim Koberstein

Language of teaching: English

ECTS-Credits (based on the workload): 6

Workload and its composition (self-study, contact time):
Self-study: 120 h, contact time: 60 h

Contact hours (per week in semester): 4

Methods and duration of examination:
Successful completion of an approx. 10-page seminar paper and implementation of a programming project in the R language as well as an intermediate and a final presentation at the end of the 2nd block (20-30 minutes each).

Emphasis of the grade for the final grade: Please check regulations of the study programme

Aim of the module (expected learning outcomes and competencies to be acquired):
Participants learn how model-based optimization approaches can be implemented in the area of financial portfolio management.

Contents of the module:
Recently, great progress has been made in taking into account uncertain data explicitly in model-based decision support systems to produce more flexible and robust planning solutions. In this seminar, we want to discuss how these approaches can be applied to the field of financial portfolio optimization. In particular, we will investigate approaches from mixed-integer and stochastic programming. In addition, we want to familiarize ourselves with modelling the language ROI for mathematical programming models and learn how to implement illustrative applications in portfolio optimization ourselves. Specific projects will be announced in the Moodle course.

Teaching and learning methods:
Online-Seminar

Literature (compulsory reading, recommended literature):
Literature will be announced in the Moodle course.

Further information:
Registration in Moodle Viadrina required.