FE515 Introduction to R

Course Catalog Description


This course is designed for graduate students.This course aims at helping students from Financial Engineering and Financial Analytics with core courses study. The content will cover the following topics:
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


Professor Email Office
Cheng Lu

More Information

Course Resources


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 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)
Numerical integration
Required: Lecture Notes 12
Week 14 Rmarkdown and Basic LaTeX
Review Midterm and Assignment 3-4