Mathematical Methods for Quantitative Finance (CFRM 460)

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Course Details

  • Location: Online
  • Duration: 8 - 10 weeks
  • Class Times: Days
  • Cost : $1,500

Next Start Date:

January 4, 2017

This course is part of a certificate program. You can also take it without enrolling in the program.

Get Details & Apply

About this Course


This course reviews the mathematical methods fundamental for the study of quantitative and computational finance. The areas of focus include calculus and multivariable calculus, constrained and unconstrained optimization, and linear algebra.

Topics include:

  • Functions and inverse functions
  • Limits, derivatives, partial derivatives, and chain rule
  • Integrals and multiple integrals, changing the order of differentiation and integration
  • Taylor series approximations
  • Newton’s method
  • Lagrange multiplier method

Upon completion of the course students will know the fundamental mathematical concepts needed to effectively study quantitative finance areas such as fixed income, options and derivatives, portfolio optimization and quantitative risk management.

Prerequisites: Entry-level college calculus courses that include an introduction to multivariable differential calculus, such as the UW courses Math 124, Math 125 or Math 126. Additional introductory mathematics and statistics coursework is desirable. 

Program Overview

We offer this program in two formats: a standard version spanning three quarters and an intensive version spanning one quarter. 

 

You may be able to take individual courses 

without enrolling in the certificate program; 

check the course pages for details.

Learning Formats

Online

Enjoy the flexibility of online learning, which allows you to participate in class activities, assignments and discussions on your schedule, from anywhere. Experience cutting-edge technology and a supportive, diverse learning community.

Blended

Our special blended programs combine the convenience of online study with the vibrancy of in-person classes for a unique learning experience.

Not available for this course

Classroom

Attend class in person at one of our convenient locations. You’ll engage face to face with your classmates and instructors as part of a highly interactive curriculum. An online learning management system may be required to access some course materials and assignments.

Not available for this course

Start Dates

Online

Meet your instructors

College Credit

This course may earn you 3.0 college credits - what's this?

Online

Meet your instructors

Kjell Peter Konis

Ph.D., Computational Statistics, University of Oxford
MSc in Applied and Computational Mathematics

College Credit

This course may earn you 3.0 college credits - what's this?

Online

Meet your instructors

College Credit

This course may earn you 3.0 college credits - what's this?