FE515 Introduction to R
Course Catalog Description
Introduction
Data gathering, cleaning, and visualization
Fundamental topics in statistics (Moments, linear regression and etc.)
Fundamental topics in financial engineering (the Black-Scholes model etc.)
Numerical methods in Finance (root-finding methods, optimization, interpolation, and numerical integration)
Campus | Fall | Spring | Summer |
---|---|---|---|
On Campus | X | X | |
Web Campus | X | X |
Instructors
Professor | Office | |
---|---|---|
Beichen Zhang
|
bzhang61@stevens.edu |
More Information
Course Resources
Textbook
Lecture Notes and Code
The art of R programming: a tour of statistical software design. Norman Matlo, First Edition,2011. ISBN-10: 1593273843, ISBN-13: 978-1593273842
An Introduction to Analysis of Financial Data with R. Ruey Tsay, First Edition, 2012. ISBN-10:0470890819, ISBN-13: 978-0470890813
Introduction to the Practice of Statistics. David S. Moore, George P. McCabe, Bruce A. Craig, Eighth Edition, 2014. ISBN-13: 978-1464158933, ISBN-10: 1464158932
Additional References
CRAN: http://www.wikibooks.org
R-help Info: https://stat.ethz.ch/mailman/listinfo/r-help
R-help Archive: http://r.789695.n4.nabble.com
Quick R: http://www.statmethods.net
Grading
Grading Policies
Attendance: Attendance is not mandatory and will not be counted in the final grade.
Assignments: All the assignments should be submitted with a report in a PDF file, which contains the code and results (with an explanation if needed), and the correct label. You can generate the PDF file using Word or Latex or Rmarkdown or other software. R file or RMD file is optional. Grading is mainly based on the report.
Exams: Both Midterm and Final Exams are taken home. The dates are planned to be the same as the tentative course schedule, any changes will be reflected in the Announcement. The format will be similar to assignments but due in one week.
- 30% Homework
- 30% Midterm
- 40% Final
Late Policy: Assignments received 1-6 days late will have 20% of the total points deducted. Assignments received more than one week late will receive 0 points. Please send an email before the due date if you need an extension. The extension is usually 2 or 3 days after the due date and should be less than one week.
Lecture Outline
Topic | Reading | |
---|---|---|
Week 1 | R basics(1) Data structures & Loops |
Required: Lecture Notes L1 Optional: The Art of R Prog. Ch.1 |
Week 2 | R basics(2) Self-defined functions ”apply” functions |
Required: Lecture Notes L2 Optional: The Art of R Prog. Ch.5 |
Week 3 | R basics(3) Date and Time objects |
Required: Lecture Notes L3 Optional: The Art of R Prog. Ch.12 |
Week 4 | R Basic(4) Packages: quantmod |
Required: Lecture Notes L4 |
Week 5 | Stats in R (1) Generating Random Variables Discrete distribution & Sampling |
Required: Lecture Notes L5 Optional: The Art of R Prog. Ch.8 |
Week 6 | Stats in R (2) Returns & Moments |
Required: Lecture Notes L6 |
Week 7 | Stats in R (3) Linear regression Stepwise selection |
Required: Lecture Notes L7 |
Week 8 | Review Assignment 1-2 | |
Week 9 | Numerical method in R (1) Bisection method & Newton's Method Gradient Descent | Required: Lecture Notes L8 |
Week 10 | Numerical method in R (2) BS Model & Implied Volatility |
Required: Lecture Notes L9 |
Week 11 | Numerical method in R (3) Monte-Carlo Simulation |
Required: Lecture Notes 10 |
Week 12 | Numerical method in R (4) Optimization in R |
Required: Lecture Notes 11 Optional: Optimization in R.pdf |
Week 13 | Numerical method in R (5) Interpolation Numerical integration |
Required: Lecture Notes 12 |
Week 14 | Rmarkdown and Basic LaTeX Review Midterm and Assignment 3-4 |