12 Months, 36 Credits
The MS in Computational Finance curriculum offers integration of finance, mathematics, and computing. The required mathematics courses have substantial financial content and the experiential computational finance course that students take during the summer make use of skills learned in the mathematics, analytics and finance courses taken up to that point.
The program features a unique summer project that includes analysis of real-world data in a business case provided by Saunders corporate partners. This provides students with challenging tasks that help to develops computational skills and prepares students for employment. The program ends with a required non-credit comprehensive exam based on the courses completed by the student.
The multidisciplinary nature of the program and the involvement of as many as four RIT colleges are its strengths. Starting in 2017, this program is now a 12-month, full-time program beginning exclusively in the fall and ending with a summer semester. Part-time options are not currently available.
Curriculum for current and past students
Current students and alumni – please visit the Office of the Registrar for a History of Course Catalogs to view and download official degree requirements pertaining to the academic year you began your degree.
Computational finance, MS degree, typical course sequence
|Course||Sem. Cr. Hrs.|
Accounting for Decision Makers
A graduate-level introduction to the use of accounting information by decision makers. The focus of the course is on two subject areas: (1) financial reporting concepts/issues and the use of general-purpose financial statements by internal and external decision makers and (2) the development and use of special-purpose financial information intended to assist managers in planning and controlling an organization's activities. Generally accepted accounting principles and issues related to International Financial Reporting Standards are considered while studying the first subject area and ethical issues impacting accounting are considered throughout.
Survey of Finance
This course introduces students to the field of finance and prepares them to undertake a study of advanced topics in other courses. Students learn about financial markets, regulation, and the fundamentals of corporate finance in areas such as investment and financing decisions. A brief overview of financial reporting allowing students to understand firm performance is also provided.
Students learn about various equity markets, trading, and valuation. The focus of this course is on valuing equities using widely used methods and in forming and analyzing equity portfolios. Students also learn portfolio optimization methods.
Students learn about various debt markets, trading, and valuation. The focus of this course is on valuing debt instruments using widely used methods and in forming and analyzing debt portfolios.
Students learn about derivatives contracts, their pricing, and uses. The course will cover advanced financial engineering topics such as the engineering of fixed-income contracts, volatility positions, credit default swaps, and structured products.
Mathematics of Finance I
This is the first course in a sequence that examines mathematical and statistical models in finance. By taking a mathematical viewpoint the course provides students with a comprehensive understanding of the assumptions and limitations of the quantitative models used in finance. Topics include probability rules and distributions, the binomial and Black-Scholes models of derivative pricing, interest and present value, and ARCH and GARCH time series techniques. The course is mathematical in nature and assumes a background in calculus (including Taylor series), linear algebra and basic probability. Other mathematical concepts and numerical methods are introduced as needed.
Mathematics of Finance II
This is the second course in a sequence that examines mathematical and statistical models in finance. By taking a mathematical viewpoint the course provides students with a comprehensive understanding of the assumptions and limitations of the quantitative models used in finance. Topics include delta hedging, introduction to Ito calculus, interest rate models and Monte Carlo simulations. The course is mathematical in nature and assumes a background in calculus (including Taylor series), linear algebra and basic probability. Other mathematical concepts and numerical methods are introduced as needed.
Computational Finance Exam Preparatory
Computational Finance students take a field exam at the end of their program. This course provides basic help to students taking this exam. (all required finance courses in the Computational finance program)
Computational Finance Experience
Students apply their mathematical, data analytic, and integrative finance skills in a complex project involving real or simulated data. Under the supervision of an advisor, students work in teams to perform a stipulated task/project and write a comprehensive report at the end of the experience. Subject to approval by the Program Director, an individual student internship/coop followed by an in-depth report may obtain equivalent credit.
|Total Semester Credit Hours||36|
|STAT-747||Principles of Statistical Data Mining|