Syllabus

Title
1471 Course V - Quantitative Optimization Methods in Finance
Instructors
Dr. Sühan Altay
Contact details
  • Type
    PI
  • Weekly hours
    2
  • Language of instruction
    Englisch
Registration
09/14/21 to 10/05/21
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Dates
Day Date Time Room
Thursday 10/14/21 10:00 AM - 01:00 PM D4.0.019
Thursday 10/21/21 10:00 AM - 01:00 PM D4.0.019
Thursday 10/28/21 10:00 AM - 01:00 PM TC.3.06
Thursday 11/04/21 10:00 AM - 01:00 PM TC.3.06
Thursday 11/11/21 10:00 AM - 01:00 PM Online-Einheit
Thursday 11/18/21 10:00 AM - 01:00 PM TC.3.06
Thursday 11/25/21 10:00 AM - 01:00 PM Online-Einheit
Thursday 12/02/21 10:00 AM - 01:00 PM Online-Einheit

Contents

Optimization methods have a significant role in quantitative financial modeling. Many computational problems in finance can be solved by optimization techniques. This course will introduce the basics of optimization methods to solve many finance-related problems ranging from asset allocation to risk management, from option pricing to interest rate modeling. The main goal of this course is to become familiar with the basic optimization techniques and to apply them into various finance-related problems.

Learning outcomes

After completing this course, the student will have the ability to

  • understand the basics of optimization methods used in financial problems;
  • apply optimization methods to concrete problems in the financial industry;
  • learn how to solve optimization problems with the help of software, e.g., MATLAB, Excel Solver, Lindo or R.

Attendance requirements

There is mandatory on-site or online attendance. This means that students should attend (online or on-site) at least 80% of all lectures ( at most one session  can be missed).  Students are expected to be active in the class (or on online Q&A sessions) .  Moreover, students will take part in a group work while working on the homework assignments and final project.

Teaching/learning method(s)

This course is mainly taught using a combination of (i) lectures elaborating relevant topics and (ii) examples (cases) illustrating and deepening various aspects of a specific topic. Real-world examples will allow students to apply theoretical knowledge to practical problems. Homework assignments and the final project will help students to consolidate and expand their knowledge and to understand the subject matter by developing solutions to applied problems. Furthermore, for the implementation and solution of the complex optimization problems, several programming languages will be presented and practiced.

The course will be held in presence for a part of the participants. At the same time, the course is streamed for all students who cannot be on campus. In case there are more than 17?  (number of Covid-19 seats available) students who are eligible and willing to come to the class, we plan to apply certain rotation rules.

Under the synchronous hybrid mode, there will be in-class teaching together with synchronous streaming.   Additionally, we plan to offer online Q&A sessions (through MS Teams) for the international students who are not able to come to Vienna. Moreover, we encourage the active use of the Forum page at Learn.

Assessment

The assessment is based on an online synchronous midterm (40%), homework  assignments (group work) (20%) and a final project ( group work) (40%). The following grading scale applies:

  • 90.00-100.00 - Excellent (1)
  • 80.00-89.00   - Good (2)
  • 70.00-79.00   - Satisfactory (3)
  • 60.00-69.00   -  Sufficient (4)
  • 00.00-59.00   -  Insufficient (5)

Prerequisites for participation and waiting lists

Positive completion of Course I and Course II

Registration via LPIS

Readings

1 Author: Alexander J. McNeil, Rüdiger Frey, Paul Embrechts
Title: Quantitative Risk Management

Publisher: Princeton University Press
Year: 2005
2 Author: Gerard Cornuejols and Reha Tutuncu
Title: Optimization Methods in Finance

Publisher: Cambridge University Press
Year: 2007

Recommended previous knowledge and skills

Sound knowledge in finance is necessary. Strong technical background (in  mathematics and statistics) is an advantage.

Availability of lecturer(s)

saltay@wu.ac.at

Last edited: 2021-10-21



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