QF112: Statistics Quantitative Finance

Course Catalog Description

Introduction

This course provides QF students with an introduction to (1) statistical estimation and inference and (2) the R statistical programming system. Although QF112does not expect that students have completed any formal statistical coursework in high school, including AP Statistics, the course is pitched at a level that assumes a basic acquaintance with some of the elements of calculus and probability.


Campus Fall Spring Summer
On Campus X
Web Campus

Instructors

Professor Email Office
Thomas Lonon
Tlonon@stevens.edu Altorfer 303

More Information

Course Outcomes

In general, upon completion of the course, students will be able to use statistical methods to describe and analyze marketplace phenomena and develop solutions to commonly encountered business problems. In particular, students will be able to

  • Think logically and analytically about quantitatively-based problems in a variety of functional areas such as quantitative finance and economic forecasting.
  • Appreciate both the strengths and limitations of many commonly-used statistical methodologies, parametric as well as nonparametric.
  • Reason programmatically in the context of the R statistical programming language including simulation as well as computation.
  • Employ with confidence and skill a number of powerful, widely-used, and generally-applicable statistical methodologies.
  • Think critically and skeptically about statistical findings often encountered not only in the workplace but also in the media; that is, to understand how people lie with statistics.

Course Resources

Textbook

Textbook(s): Title: Statistics with R: A Beginner’s Guide, 2018 Author: Stinerock Publisher: Sage ISBN: 978-1473-924-901 Readings: Chapters 1-13


Grading

Grading Policies

  • Attendance/Participation - 5%
  • Homework - 30%
  • Exams - 35%
  • Final Exam- 30%
  • TOTAL - 100%

Lecture Outline

Topic Reading
Week 1 Syllabus and Overview
Week 2 Data
Week 3 Sample Statistics
Week 4 Parameter Estimation
Week 5 Confidence Intervals
Week 6 Hypothesis Tests
Week 7 Hypothesis Tests (cont.)
Week 8 Linear Regression
Week 9 Exam
Week 10 ANOVA
Week 11 Linear Regression (cont.)
Week 12 Polynomial Regression and Multivariable
Week 13 Bootstrapping and Monte Carlo
Week 14 Review & Catch-up