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M.S. in Applied Mathematics with a Specialization in Quantitative Risk Management

The aim for the Quantitative Risk Management specialization is to train students for careers in the quantitative financial or insurance industries, or for a Ph.D. program geared towards mathematical aspects of these disciplines. Students will acquire knowledge in the fundamentals of financial risk management as well as advanced pricing and hedging methodologies relevant to modern financial markets. Student will be able to take a variety of courses in risk management, mathematical finance, artificial intelligence, stochastic analysis, statistics, and computational finance. 

Students must take Math 540 or Math 475, or show evidence of having taken a course in Probability equivalent to Math 475.

Required to satisfy the following two course options:

  • Math 542 Stochastic Processes OR Math 543 Stochastic Analysis Math 588 Quantitative Risk Management

Required to take at least one of the following courses

  • Math 582 Mathematical Finance II
  • Math 565 Monte Carlo Methods in Finance
  • Math 587 Theory and Practice of Modeling Risk and Credit Derivatives

Remaining Elective courses to be chosen in consultation with the academic advisor, or from the following list or any unused core/required courses listed above:

  • Math 543 Stochastic Analysis
  • Math 544 Stochastic Dynamics OR Math 545 Stochastic Partial Differential Equations
  • Math 546 Introduction to Time Series OR Math 566 Multivariate Analysis
  • Math 563 Mathematical Statistics or Math 564 Applied Statistics
  • Math 569 Statistical Learning
  • Math 574 Bayesian Computational Statistics
  • Math 578 Computational Mathematics II
  • Math 586 Theory and Practice of Fixed Income
  • Math 581 Finite Element Method OR Math 589 Numerical Methods for Partial Differential Equations OR Math 590 Meshfree Methods

PLANS OF STUDY

Here we present a few plans of study for the various options in the MS program. Note that Math 593, a required course, is not listed below under the plans of study as it is a zero credit course offered every semester.

A Sample Course Sequence for the Specialization in `Computational Statistics for Data Science’ with Project:

Fall - Year 1

Math 577
Math 540 or Math 475
Math 542



3 credit hours
3 credit hours
3 credit hours

Spring - Year 1

Math 588
Math 582
Math 591



3 credit hours
3 credit hours
1-3 credit hours

Fall - Year 2

Core Math 500/553/563
Elective
Math 591



3 credit hours
3 credit hours
1-3 credit hours

Spring - Year 2

Elective
Math 591



3 credit hours
1-3 credit hours
Total  32 credit hours