IE 423

Course ID:

Course Code & Number

IE 423

Course Title

Optimization Models in Finance

Level

BS

Credit Hours/ ECTS Credits

(3+0+0) 3 TEDU Credits, 5 ECTS Credits

Year of Study:

Senior

Semester:

Fall

Type of Course:

Elective

Mode of Delivery:

Face-to-face

Language of Instruction:

English

Pre-requisite / Co-requisite::

Catalog Description

Introduction to Mathematical Programming models used in computational finance. Asset-Liability Management, Arbitrage and Asset Detection with Linear Programming, Mean-variance models with Quadratic Programming, Portfolios with Combinatorial Constraints with Mixed Integer Programming; Asset-Liability Management using Risk Measures with Stochastic Programming; Multi-period Portfolio Optimization, Binomial Pricing with Dynamic Programming; Robust Profit Opportunities in Risky Portfolios, Robust Portfolio Selection with Robust Optimization. Introduction to Mathematical Programming models used in computational finance. Asset-Liability Management, Arbitrage and Asset Detection with Linear Programming, Mean-variance models with Quadratic Programming, Portfolios with Combinatorial Constraints with Mixed Integer Programming; Asset-Liability Management using Risk Measures with Stochastic Programming; Multi-period Portfolio Optimization, Binomial Pricing with Dynamic Programming; Robust Profit Opportunities in Risky Portfolios, Robust Portfolio Selection with Robust Optimization.

Course Objectives

The main objective of this course is to introduce useful optimization models with emphasis on financial applications.

Software Usage

Optimization Solvers, Data Analysis Software

Course Learning Outcomes

1. Use Linear Programming for Asset-Liability Management, Arbitrage and Asset Detection (c),
2. Solve Mean-variance models via Quadratic Programming (c),
3. Formulate Mixed lnteger Programming Portfolio models with Combinatorial Constraints (c, i), 4. Construct Stochastic Programming models ta solve Asset-Liability Management problems with appropriate Risk Measures (c, h),
5. Develop Dynamic Programming models for Multi-period Portfolio Optimization (e),
6. Price options with binomial lattice model (e, i),
7. Detect profit opportunities in risky portfo]ios, and select optimal portfolios (c, h).

Learning Activities and Teaching Methods:

Telling/Explaining Discussion/Debate Questioning Reading Demonstrating Problem Solving Hands-on Activities

Assessment Methods and Criteria:

Test / Exam Quiz Case Studies / Homework

Assessment Methods and Criteria Others:

Design Content

Recommended Reading

Required Reading

Grading

Learning Activities and Teaching Methods Others:

Course Coordinator:

Çağrı Latifoğlu

Student Workload:

Workload	Hrs
Course Readings	36
Hands-on Work	30
Exams/Quizzes	24

Course & Program Learning Outcome Matching: