Overview
The Master of Science in Mathematical Finance program is dedicated to producing technically trained professionals with an understanding of how to analyze and value complex investments, and assess the associated risks. Over the course of three semesters of study, the students receive rigorous training in mathematics, especially in the area of probability and stochastic calculus, in statistical analysis, and in computation, together with an overview of the common financial instruments and the institutional operation of markets and exchanges.
The financial landscape is constantly changing, and we design the MSMF curriculum to equip students with skills and knowledge that will provide the foundation for their future success. Our program seeks the proper balance between the mathematical and statistical theory, programming practice and financial applications.
Admission Requirements
Here is a list of the course based admissions requirements. For more information about admission, please visit our website.
- 1 semester of Linear Algebra
- 1 semester of Differential Equations
- 1 semester of calculus-based Probability and Statistics
Curriculum Requirements
MS in Mathematical Finance
| Code | Title | Credit Hours |
|---|---|---|
| Core Courses | ||
| MTH 642 | Statistical Analysis | 3 |
| MTH 643 | Statistical Analysis II with Financial Applications | 3 |
| MTH 645 | Optimization Methods | 3 |
| MTH 647 | Introduction to Mathematical Finance | 3 |
| MTH 648 | Stochastic Calculus with Application to Finance | 3 |
| MTH 649 | Computational Methods of Finance | 3 |
| FIN 650 | Financial Investment | 2 |
| FIN 651 | Quantitative Stock Portfolio Management | 2 |
| Electives | 12 | |
| Computer Science, Engineering, and Mathematics Electives (3-9 credits) | ||
| Introduction to Parallel Computing | ||
| Introduction to Artificial Intelligence | ||
| Introduction to Machine Learning with Applications | ||
| Neural Networks and Deep Learning | ||
| Partial Differential Equations I | ||
| Partial Differential Equations II | ||
| Numerical Linear Algebra | ||
| Quantitative Risk Analysis | ||
| Machine Learning in Quantitative Finance | ||
| Mathematical Probability | ||
| Finance Electives (2-6 credits) | ||
| Fundamentals of Finance | ||
| Quantitative Finance and Market Microstructure | ||
| Alternative Investments | ||
| International Finance | ||
| Corporate Finance | ||
| Financial Institutions | ||
| Financial Modeling | ||
| Other Business Electives (0-3 credits) | ||
| Financial Reporting and Control in the Healthcare Industry | ||
| Game Theory and Economic Strategy | ||
| International Financial Management | ||
| Valuation and Financial Decision Making | ||
| Fixed Income Securities | ||
| Total Credit Hours | 34 | |
MSMF with a concentration in Digital Currency
| Code | Title | Credit Hours |
|---|---|---|
| Core Courses | ||
| MTH 647 | Introduction to Mathematical Finance | 3 |
| MTH 648 | Stochastic Calculus with Application to Finance | 3 |
| MTH 642 | Statistical Analysis | 3 |
| MTH 643 | Statistical Analysis II with Financial Applications | 3 |
| MTH 645 | Optimization Methods | 3 |
| FIN 650 | Financial Investment | 2 |
| MTH 682 | Blockchain and Cryptocurrency Platforms | 3 |
| MTH 683 | Algorithmic and High-Frequency Trading | 3 |
| Electives | 11 | |
| MTH or CSC Electives (at least 6 credits must be from this list): | ||
| Topics in Mathematical Finance | ||
| Topics in Mathematical Finance | ||
| Machine Learning in Quantitative Finance | ||
| Partial Differential Equations I | ||
| Partial Differential Equations II | ||
| Numerical Linear Algebra | ||
| Mathematical Probability | ||
| Introduction to Artificial Intelligence | ||
| Introduction to Machine Learning with Applications | ||
| Neural Networks and Deep Learning | ||
| Data Security and Cryptography | ||
| FIN electives (2 to 6 credits): | ||
| International Finance | ||
| Corporate Finance | ||
| Financial Institutions | ||
| Financial Modeling | ||
| Total Credit Hours | 34 | |
Sample Plan of Study
3-semester MSMF
| Year One | ||
|---|---|---|
| Fall | Credit Hours | |
| MTH 642 | Statistical Analysis | 3 |
| MTH 647 | Introduction to Mathematical Finance | 3 |
| FIN 650 | Financial Investment | 2 |
| Elective 1 | 3 | |
| Credit Hours | 11 | |
| Spring | ||
| MTH 643 | Statistical Analysis II with Financial Applications | 3 |
| MTH 648 | Stochastic Calculus with Application to Finance | 3 |
| FIN 651 | Quantitative Stock Portfolio Management | 2 |
| Elective 1 | 3 | |
| Credit Hours | 11 | |
| Summer | ||
| Summer Internship or Project (Optional) | ||
| Credit Hours | 0 | |
| Year Two | ||
| Fall | ||
| MTH 645 | Optimization Methods | 3 |
| MTH 649 | Computational Methods of Finance | 3 |
| FIN Electives | 3-6 | |
| Additional Elective (If Needed) 1 | 3 | |
| Credit Hours | 12 | |
| Total Credit Hours | 34 | |
3-semester MSMF w/concentration in Digital Currency
| Year One | ||
|---|---|---|
| Fall | Credit Hours | |
| MTH 642 | Statistical Analysis | 3 |
| MTH 647 | Introduction to Mathematical Finance | 3 |
| FIN 650 | Financial Investment | 2 |
| MTH 682 | Blockchain and Cryptocurrency Platforms | 3 |
| Credit Hours | 11 | |
| Spring | ||
| MTH 643 | Statistical Analysis II with Financial Applications | 3 |
| MTH 648 | Stochastic Calculus with Application to Finance | 3 |
| FIN 651 | Quantitative Stock Portfolio Management | 2 |
| MTH 683 | Algorithmic and High-Frequency Trading | 3 |
| Credit Hours | 11 | |
| Summer | ||
| Summer Internship or Project (Optional) | ||
| Credit Hours | 0 | |
| Year Two | ||
| Fall | ||
| MTH 645 | Optimization Methods | 3 |
| MTH 649 | Computational Methods of Finance | 3 |
| CSC/MTH Electives | 3-6 | |
| FIN Elective 1 | 2 | |
| Credit Hours | 12 | |
| Total Credit Hours | 34 | |
Mission
The Master of Science in Mathematical Finance program is dedicated to producing technically trained professionals with an understanding of how to analyze and value complex investments, and assess the associated risks. Over the course of three semesters of study, the students receive a rigorous training in mathematics, especially in the area of probability and stochastic calculus, in statistical analysis, and in computation, together with an overview of the common financial instruments and the institutional operation of markets and exchanges.
The financial landscape is constantly changing, and we design the MSMF curriculum to equip students with skills and knowledge that will provide the foundation for their future success. Our program seeks the proper balance between the mathematical and statistical theory, programming practice and financial applications.
Goals
- Provide future finance professionals with the advanced quantitative skills required to understand, evaluate and price modern financial instruments. Such skills include both analytic techniques of mathematical finance, and computer-based simulation techniques.
- Expose participants to the key statistical methods, and specifics of applying these methods to working with financial data.
- Impart the necessary hands-on software and programming skills to solve various optimization and simulation problems arising in financial setting.
Concentration in Digital Currency
- Provide participants with the quantitative and programming tools used in the implementation and trading of cryptocurrencies and digital currency financial instruments.
Student Learning Outcomes
- Students will demonstrate advanced knowledge of risk-neutral approach to pricing financial instruments, discrete and continuous-time frameworks of modern mathematical finance, and common financial derivatives.
- Students will master the tools of statistical analysis and statistical software packages and be able to apply them to various financial datasets.
- Students will demonstrate working knowledge of software and programming tools to use optimization and simulation techniques in financial setting master the common models of portfolio analysis, as well as the quantitative approach to risk models.
Concentration in Digital Currency
- Students will master the mathematical foundations of cryptocurrency algorithms and the tools of algorithmic trading used in electronic markets for digital currency and other financial instruments.
