Financial Mathematics (MR)
Master of Financial Mathematics Degree Requirements
| Code | Title | Hours | Counts towards |
|---|---|---|---|
| Core Courses | 26 | ||
| Options and Derivatives Pricing | |||
| Fundamentals of Statistical Inference I | |||
| Capital Investment Economic Analysis | |||
| Career Development for Quants | |||
| Stochastic Calculus for Finance | |||
| Fundamentals of Statistical Inference II | |||
| Monte Carlo Methods for Financial Math | |||
| Seminar in Financial Mathematics 1 | |||
| Computational Methods in Economics and Finance | |||
| Summer Internship/Project Course | 1 | ||
| Select one summer semester requirement of the following: | |||
| Internship in Financial Mathematics | |||
| Project in Financial Mathematics | |||
| Elective Courses | 9 | ||
| See "Elective Courses" listed below | |||
| Total Hours | 36 | ||
- 1
Students need to take FIM 601 (1 credit) in their second and third semesters for a total of 2 credits
Elective Courses
| Code | Title | Hours | Counts towards |
|---|---|---|---|
| Select at least three courses listed below: | 9 | ||
Risk Management Track | |||
| FIM/MA 549 | Financial Risk Analysis | 3 | |
| ISE 519 | Database Applications in Industrial and Systems Engineering | 3 | |
| MBA 518 | Enterprise Risk Management | 3 | |
| MBA 521 | Advanced Corporate Finance | 3 | |
Data Science for Finance Track | |||
| ISE 519 | Database Applications in Industrial and Systems Engineering | 3 | |
| ST 503 | Fundamentals of Linear Models and Regression | 3 | |
| ST 516 | Experimental Statistics For Engineers II | 3 | |
| ST 540 | Applied Bayesian Analysis | 3 | |
| ST 590 | Special Topics (Applied Time Series) | 1-6 | |
| ST 562 | Data Mining with SAS Enterprise Miner | 3 | |
| ST 555 | Statistical Programming I | 3 | |
Portfolio Management Track | |||
| OR/MA 504 | Introduction to Mathematical Programming | 3 | |
| OR/ISE 505 | Linear Programming | 3 | |
| OR 506 | Algorithmic Methods in Nonlinear Programming | 3 | |
| MBA 523 | Investment Theory and Practice | 3 | |
| MBA 524 | Equity Valuation | 3 | |
| MA 531 | Dynamic Systems and Multivariable Control I | 3 | |
| ISE 519 | Database Applications in Industrial and Systems Engineering | 3 | |
Actuarial Science Track | |||
| ECG 701 | Microeconomics I | 3 | |
| ECG 702 | Microeconomics II | 3 | |
| ECG/ST 750 | Introduction to Econometric Methods | 3 | |
| ECG/ST 751 | Econometric Methods | 3 | |
| ECG/ST 752 | Time Series Econometrics | 3 | |
| ECG/ST 753 | Microeconometrics | 3 | |
| MA/ST 747 | Probability and Stochastic Processes II | 3 | |
| MBA 518 | Enterprise Risk Management | 3 | |
PhD Preparation Track | |||
| OR/ISE 505 | Linear Programming | 3 | |
| ECG/ST 751 | Econometric Methods | 3 | |
| ECG/ST 752 | Time Series Econometrics | 3 | |
| MA 523 | Linear Transformations and Matrix Theory | 3 | |
| MA 540 | Uncertainty Quantification for Physical and Biological Models | 3 | |
| MA/ST 546 | Probability and Stochastic Processes I | 3 | |
| ST 730 | 3 | ||
| ST 740 | Bayesian Inference and Analysis | 3 | |
| MA 791 | Special Topics In Real Analysis (Functional Analysis) | 1-6 | |
Other | |||
| CSC 505 | Design and Analysis Of Algorithms | 3 | |
| CSC 522 | Automated Learning and Data Analysis | 3 | |
| CSC 540 | Database Management Concepts and Systems | 3 | |
| CSC 541 | Advanced Data Structures | 3 | |
| CSC/MA 580 | Numerical Analysis I | 3 | |
| CSC/MA 583 | Introduction to Parallel Computing | 3 | |
| ISE 712 | Bayesian Decision Analysis For Engineers and Managers | 3 | |
| MBA 515 | Enterprise Resource Planning Systems | 3 | |
| MBA 526 | International Finance | 3 | |
| MA 515 | Analysis I | 3 | |
| MA 520 | Linear Algebra | 3 | |
| MA 532 | Ordinary Differential Equations I | 3 | |
| MA 534 | Introduction To Partial Differential Equations | 3 | |
| MA 544 | Computer Experiments In Mathematical Probability | 3 | |
| MA 555 | Introduction to Manifold Theory | 3 | |
| MA/BMA 573 | Mathematical Modeling of Physical and Biological Processes I | 3 | |
| MA/BMA 574 | Mathematical Modeling of Physical and Biological Processes II | 3 | |
| MA 584 | Numerical Solution of Partial Differential Equations--Finite Difference Methods | 3 | |
| MA 587 | Numerical Solution of Partial Differential Equations--Finite Element Method | 3 | |
| MA 715 | Measure Theory and Integration | 3 | |
| MA 723 | Theory of Matrices and Applications | 3 | |
| MA/ST 746 | Introduction To Stochastic Processes | 3 | |
| MA/ST 748 | Stochastic Differential Equations | 3 | |
| OR/ISE 501 | Introduction to Operations Research | 3 | |
| OR/MA 504 | Introduction to Mathematical Programming | 3 | |
| OR/E/MA 531 | Dynamic Systems and Multivariable Control I | 3 | |
| OR/MA 719 | Vector Space Methods in System Optimization | 3 | |
| OR/ISE 772 | Advanced Stochastic Simulation | 3 | |
| OR/BMA/MA/ST 773 | Stochastic Modeling | 3 | |
| ST 505 | Applied Nonparametric Statistics | 3 | |
| ST 512 | Statistical Methods For Researchers II | 3 | |
| ST 556 | Statistical Programming II | 3 | |
| ST 563 | Introduction to Statistical Learning | 3 | |
Accelerated Bachelor's/Master's Degree Requirements
The Accelerated Bachelors/Master’s (ABM) degree program allows exceptional undergraduate students at NC State an opportunity to complete the requirements for both the Bachelor’s and Master’s degrees at an accelerated pace. These undergraduate students may double count up to 12 credits and obtain a non-thesis Master’s degree in the same field within 12 months of completing the Bachelor’s degree, or obtain a thesis-based Master’s degree in the same field within 18 months of completing the Bachelor’s degree.
This degree program also provides an opportunity for the Directors of Graduate Programs (DGPs) at NC State to recruit rising juniors in their major to their graduate programs. However, permission to pursue an ABM degree program does not guarantee admission to the Graduate School. Admission is contingent on meeting eligibility requirements at the time of entering the graduate program.
Full Professors
- David Dickey
- Paul Fackler
- Sujit Ghosh
- Kazufumi Ito
- Negash Medhin
- Tao Pang
- Tom Vukina
- Mark Walker
- Richard Warr
Associate Professors
- Min Kang
- Andrew Papanicolaou
- Denis Pelletier
- Charlie Smith
Assistant Professors
- Ilze Kalnina
- Yerkin Kitapbayev
- Dominykas Norgilas
Practice/Research/Teaching Professors
- Wei Chen
- Richard Ellson
- Jeffrey High
- Ram Valluru
Emeritus Faculty
- Richard Bernhard
- Peter Bloomfield
- Jeffrey Scroggs
- John Seater
- Jim Wilson