FE516 MATLAB for Finance

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Course Catalog Description

Introduction

Students will get hands-on experience with Matlab, and they will gain fundamental knowledge in developing applications involving financial data. The course is designed so that upon completion the students will be able to use Matlab for their assignments and research involving programming, particularly in future finance courses (e.g., FE 621, FE 630, FE 635, FE 535). Knowing Matlab is a very useful skill for a quantitative analyst working in the financial industry.
Campus Fall Spring Summer
On Campus X X
Web Campus

Instructors

Professor Email 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:

(i) Import/export data

(ii) Create and manipulate variables

(iii) Working data in and out of databases

(iv) Analyze and visualize data

(v) Implement algorithms, simulate stochastic processes

(vi) 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

HW 40%, Class work 20%, Final Project 40%.


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