University of Washington - Master of Science in Computational Finance and Risk Management

Masters Degree
Published

January 16, 2024

MS Overview

Key takeaways

  • only 1 year program, fall semester provided
  • tuition fee $45,000
  • many interesting electives

Program

Fall

Deadline: February

Fees

$45,570

Prerequisites/requirements

Minimum admissions requirements

  • Calculus through partial differentiation, matrix algebra, and one-dimensional optimization
  • Probability and statistics at the level of an upper level undergraduate course or entry level graduate course
  • A programming language such as Java, C++, Python, or in a math or a statistics programming language such as MATLAB, R/S-PLUS

GRE/GMAT

optional

For reporting GRE scores, use 4854 (University of Washington) under Score Report Recipients. The Department code is not necessary. For reporting GMAT scores, use GMAT code 459-75-53.

Items for application

  • Academic transcripts from undergraduate or previous graduate education.
  • Statement of purpose, a current resume, and recommendation letters (two for online applicants; three for campus applicants).
  • Completed application forms and payment of $85 non-refundable application fee
  • Other items as identified by your application (e.g. proof of English proficiency)

Curriculum

Optional undergraduate-level preparatory courses

  • Mathematical methods for quantitative finance
  • Probability and statistics for computational finance
  • Introduction to financial markets
  • Introduction to computational finance and financial econometrics
  • R programming for quantitative finance

Mandatory MS-CFRM courses

  • Investment science
  • Financial data science
  • Asset allocation and portfolio management
  • Options and other derivatives
  • Monte Carlo methods in finance
  • Ethics in the finance profession

Required course option

  • Financial data access & analysis with SQL, VBA, Excel
  • Optimization methods in finance

Elective MS-CFRM courses

  • Special studies in computational finance
  • Financial software development and integration with C++
  • Machine learning for finance
  • Introduction to trading systems
  • Advanced trading systems
  • Advanced C++ for finance
  • FinTech, blockchain, and cryptocurrencies
  • Fixed income analytics
  • Portfolio performance analysis & benchmarking
  • Endowment and institutional investment management
  • Risk in financial institutions
  • Quantitative risk management
  • Credit risk management
  • Stochastic calculus for quantitative finance
  • Energy markets analytics and derivatives
  • Financial time series forecasting methods

Special CFRM electives

Independent research or study

Internship or CPT

Master’s thesis

Elective AMATH courses

AMATH 582 and 583 are acceptable elective courses in the CFRM MS program

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