Practical Financial Optimization

About

Week One

Day 1: Introduction to GAMS using mean variance/ mean standard deviation optimization.

Day 2: Continued introduction to GAMS. Adding practical constraints such as fixed costs, size constraints and gearing to the mean variance model. Analysing the results in Excel.

Day 3: Scenario generation and optimization. Case: index tracking and regret minimization.

Day 4: Scenario optimization continued. Case: Value at Risk and Conditional Value at Risk.

Day 5: Exchange Traded Funds and Feature Selection. Introduction to the final project on a real life asset allocation optimization and benchmarking case.

Week Two

Day 1: The final project will be further discussed. We will work together on developing a back-testing framework for use in the final project.

Day 2-5: Work on the final project in class. Ad-hoc lectures are given based on the development of the project work.

Target group

Master

Course aim

Skills:
Participants will be trained in quantitative evaluation of risk-return trade-offs, and learn how to model, solve, and document large, practical problems.

The course also gives an introduction to the programming language GAMS (General Algebraic Modelling Systems), which will be used extensively in all the cases and examples.

Participants who have followed the course will be able to formulate and solve optimization problems in GAMS in particular within the following areas:

Measuring and managing return and risk trade offs
Adding practical constraints to financial optimization problems
Immunization and dedication of a bond portfolio
Modelling Value at Risk and Conditional Value at Risk
Back-testing results of ex-ante optimization

Competencies:

The course gives an introduction to the domain of practical financial risk and portfolio management. Participants will work with problem areas that can be attacked using optimization models.

Fee info