Syllabus

Title
1633 K5 - Quantitative Optimization Methods in Finance
Instructors
Dr. Sühan Altay
Contact details
  • Type
    PI
  • Weekly hours
    2
  • Language of instruction
    Englisch
Registration
09/16/20 to 09/27/20
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Dates
Day Date Time Room
Thursday 10/15/20 09:30 AM - 12:30 PM Online-Einheit
Thursday 10/22/20 10:00 AM - 01:00 PM Online-Einheit
Thursday 10/29/20 09:30 AM - 12:30 PM Online-Einheit
Thursday 11/05/20 09:30 AM - 12:30 PM Online-Einheit
Thursday 11/12/20 09:30 AM - 12:30 PM Online-Einheit
Thursday 11/19/20 09:30 AM - 12:30 PM Online-Einheit
Thursday 11/26/20 09:30 AM - 12:30 PM Online-Einheit
Thursday 12/03/20 09:30 AM - 12:30 PM Online-Einheit

Procedure for the course when limited activity on campus

In case of limited activity on campus, the course will take place via Distance Mode. 

There will be virtual synchronous course units and/or lecture casting under the' distance teaching' mode.  Synchronous course units will mostly be devoted to working on examples as well as for Q&A.

Students are expected to be active online Q&A sessions as well as on the forum page of the course.

Online attendance is compulsory. The assessment is based on an online midterm (40%), homework assignments (group or individual work) (20%), and a final project (group or individual work) (40%).

 

 

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

Full attendance is mandatory. This means that students should attend at least 80% of all lectures ( at most one session  can be missed).

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.

Assessment

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

  • 90.00-100.00 - Excellent (1)
  • 80.00-89.99   - Good (2)
  • 70.00-79.99   - Satisfactory (3)
  • 60.00-69.99   -  Sufficient (4)
  • 00.00-59.99   -  Insufficient (5)

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

Prerequisites for participation and waiting lists

Fulfillment of the specific requirements for admission to courses and examinations defined in the curriculum.

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: 2020-11-27



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