The M.S. in Mathematical and Computational Finance (MSMCF) at NJIT provides students with the mathematical and computational tools and with the understanding of financial instruments and markets needed to obtain positions as quantitative analysts in financial institutions including Wall Street investment firms.
The MSMCF is offered by the Department of Mathematical Sciences (DMS), in conjunction with the School of Management. DMS has established national prominence in fields of mathematical science including Fluid Dynamics and Mathematical Biology. In 2008, DMS faculty received research funding of over $2.3 million from agencies including the National Science Foundation, the National Institutes of Health, the Howard Hughes Institute of Medical Research, the Office of Naval Research, and the Department of Energy. A national study of faculty productivity conducted in conjunction with the National Research Council study of doctoral programs has ranked NJIT mathematics among the Top 10 nationally in the Top Research Universities Faculty Scholarly Productivity Index for 2007, by Academic Analytics, as reported on the web site of The Chronicle of Higher Education. The 2007 index compiles overall institutional rankings of 375 universities that offer the Ph.D. degree.
Finance courses for the program are taught by faculty from NJIT’s School of Management. Many of these faculty members have extensive experience in the financial industry. School of Management faculty will utilize industry contacts to provide every student with the opportunity to work on real world applications in the capstone Financial Engineering Project course.
The Master of Science in Mathematical and Computational Finance provides students with the theoretical knowledge as well as the practical methods and skills needed to begin or enhance careers as quantitative analysts in the financial industry. Because of the evolving nature of financial markets and institutions, practitioners in this field must be ready to learn new ideas and methods across a broad range of disciplines including mathematics, statistics, computational science, finance, and economics. The program aims to provide the multidisciplinary foundations preparing quantitative analysts for this life-long development of skills and understanding. Students should have a mathematical background equivalent to that of a typical undergraduate major in the engineering, physical, or mathematical sciences.
To receive unconditional admittance, applicants should have a bachelor’s degree reflecting substantial experience in quantitative analysis with tools that include differential equations, linear algebra, numerical computing and statistical analysis. Prerequisites: undergraduate course in finance (FIN 315 or equivalent), C/C++ programming skills, two semesters of calculus-based courses in probability or statistics, undergraduate calculus and multivariate calculus (MATH 111, MATH 112 and MATH 213 or equivalent), undergraduate differential equations (MATH 222 or equivalent), undergraduate linear algebra (MATH 337 or equivalent), exposure to partial differential equations as models, such as is typical in undergraduate courses in electromagnetism, heat transfer, fluid dynamics, elasticity and quantum mechanics. Students lacking some of these requirements may be accepted conditionally and required to take appropriate bridge courses.
- Degree Awarded: Master of Science in Mathematical and Computational Finance
- Credits required: 33 (11 three-credit hour courses)
- Program Objective: To prepare students in quantitative modeling of financial markets/instruments and analysis of those models to obtain information of practical value in the financial industry.
- Fin 641 - Derivative Markets
- Math 611 - Numerical Methods for Computation
- Math 605 - Stochastic Calculus
- Math 646 - Time Series Analysis
- Math 604 - Mathematical Finance
- Math 606 - Term Structure Models
- Math 666 - Simulation for Finance
- Math 608 - Partial Differential Equations for Finance
- Math 607 - Credit Risk Modeling
- Math 609 - Project in Math & Computational Finance