FE516 MATLAB for Finance
Course Catalog Description
Introduction
Campus | Fall | Spring | Summer |
---|---|---|---|
On Campus | |||
Web Campus |
Instructors
Professor | Office | |
---|---|---|
Sebastian Tudor
|
studor@stevens.edu | Altorfer 301 |
More Information
Course Description
The purpose of the this course is to introduce the basics of Matlab programming and some relevant toolboxes for finance. This short course is intended for students with little or no experience with the software covering Matlab’s basic operations and features. In addition, the course works through several applications, to give the students the necessary knowledge on developing their own projects. Topics covered include functions, arrays, Matlab plotting, simulation of stochastic processes in finance, numerical and symbolic solvers. Assignments are designed to build an appreciation for randomness, simulation, and the role of approximation. Also, in-class workshops are designed for students to gain better insights and develop their skills. Several useful and powerful Matlab toolboxes are studied with relevant examples: Curve fitting Toolbox, Optimization Toolbox, Statistics Toolbox, Database Toolbox, numerical solvers (solving equations, integration, differentiation, ODEs, etc.), Symbolic Math Toolbox, Simulink. The final part of the class involves financial applications (Monte Carlo Simulation, Brownian Motion Simulation and Calibration, Black–Scholes option pricing, etc.) Other topics could be added at students’ request.
Course Outcomes
After taking this course, the students will be able to:
1. Import/export data
2. Create and manipulate variables
3. Working data in and out of databases
4. Analyze and visualize data
5. Implement algorithms, simulate stochastic processes
6.Use symbolic and numerical solvers
Course Resources
Textbook
Attaway, Stormy. Matlab: a practical introduction to programming and problem solving (2011)
Additional References
Matlab Primer, Matlab help
Grading
Grading Policies
- 40% HW
- 20% classwork
- 40% Final Project
Lecture Outline
Topic | Reading | |
---|---|---|
Week 1 | Syllabus,requisites,plan of the class,Basic commands,Arrays,Logical operators | |
Week 2 | Program flow (for, while, if, switch),Data types: strings, matrices,cells,2D and 3D plotting,Scripts and functions | In-class exercises |
Week 3 | File manipulations (I/O): read, write | In-class exercises |
Week 4 | Workshop: surface plot, matrix manipulations, solving a linear system of equations, iterative search for the minimum of a convex function | HW 1 is due |
Week 5 | Interpolation, extrapolation, and linear regression,Least squares problem | |
Week 6 | Curve cutting toolbox | examples and exercises |
Week 7 | Optimization toolbox | examples and exercises |
Week 8 | Symbolic Math toolbox and numerical solvers (for ODE, PDE,etc.) | HW 2 is due |
Week 9 | Statistics Toolbox | examples and exercises |
Week 10 | Workshop: Data analysis and visualization,Database toolbox,Financial Toolbox. | HW 3 is due |
Week 11 | Brownian Motion: properties, simulation and calibration,Monte Carlo determination of mean and variance for a Geometric BM. | |
Week 12 | Simulation of stochastic processes and financial models | |
Week 13 | Workshop,Final project Q&A | HW 4 is due |
Week 14 | Final project presentations and discussion |