Established in 1998, the financial mathematics graduate major offers studies leading to the M.S. and Ph.D. degrees in mathematics related to finance. The flexible M.S. degree may be earned as a terminal degree or en route to the Ph.D. The M.S. degree offers two tracks, one in financial engineering, and one in actuarial science. The financial mathematics M.S. degree carries the Professional Science Master's designation. We invite applications from students with a strong mathematics background — see the admissions page for how to apply, and explore below to learn more about the major.
The financial mathematics major is supported by faculty members in statistics, computer science, scientific computing, and the College of Business, College of Social Sciences and Public Policy, and College of Law. Core faculty have expertise in a wide range of related topics, and our graduates compete successfully for internships and jobs after graduation. Alumni find employment as quants in industry and government, as actuaries, and in academia.
The M.S. in financial mathematics is a two-year degree (4 semesters) with coursework from the mathematics, statistics, and scientific computing departments, and available electives from economics, finance and computer science.
There are two track options:
- Quantitative finance/financial engineering track
This track gives a general mathematical background for work in the financial industry, including available coursework in machine learning, stochastic methods and numerical analysis.
- Actuarial science track
The actuarial science track prepares students for an actuarial career, with a focus on providing background and preparation for actuarial exams. Qualified M.S. students in this track are eligible for special actuarial teaching assistantships.
Both tracks require 33 letter graded hours (11 courses) plus 2 S/U graded proseminar hours.
See below for some sample course schedules.
Quantitative Finance/Financial Engineering M.S. track
To complete the degree, students take the following courses:
- MAP 5601 Introduction to Financial Mathematics, 3 hrs, Fall
- MAP 6621 Financial Engineering I, 3 hrs, Spring
- MAP 5615 Monte Carlo Methods in Financial Mathematics, 3 hrs, Spring
- MAT 5939r Financial Mathematics Proseminar, 1 hour in Fall and Spring, for a total of 2 hours.
- ISC 5305 Scientific Programming, 3 hrs, Fall
- STA5326 Distribution theory and Inference or STA 6346 Advanced Probability and Inference I, 3 hrs, Fall
- CAP 5768 Introduction to Data Science, 3 hrs, Fall
and five or six additional approved elective courses.
Actuarial Science track
Students will take the following courses (unless adjusted when a student has already passed an actuarial exam):
- MAP 5601 Introduction to Financial Mathematics, 3 hrs, Fall
- MAP 5932 Financial Mathematics for Actuaries, 3 hrs, Fall
- MAP 5932 Short Term Actuarial Math I, 3 hrs, Spring
- MAT5932 Short-Term Actuarial Models II, 3 hrs, Fall
- MAP 5177 Long-Term Actuarial Models I, 3 hrs, Fall
- MAP 5178 Long-Term Actuarial Models II, 3 hrs, Spring
- STA 5325 Mathematical Statistics, 3 hrs, Spring
- STA 5207 Applied Regression Methods, 3 hrs, Fall
- STA 5856 Time Series and Forecasting Methods, 3 hrs, Spring
plus two additional seminars from the following list:
- Actuarial P-Seminar, 1 hrs, Fall
- Actuarial FM-Seminar, 1 hrs, Spring
- MAT 5939 Financial Mathematics Proseminar, 1 hrs, Fall
Applicants should indicate an interest in this track at the time of application.
Elective Options
These courses are pre-approved electives that have been offered in past semesters. Terms in which they are offered in the future are subject to change. Please consult the director if you want to take a course that is not listed here.
Approved courses offered by the Department of Mathematics
- MAA 5616 Measure and Integration I, fall
- Area: Analysis
- MAA 5617 Measure and Integration II, spring
- Area: Analysis
- MAA 6416 Topics in Stochastic Calculus, occasional
- Area: Analysis
- MAP 5207 Optimization, spring
- Area: Analysis
- MAD 5403 Foundations of Computational Mathematics I, fall
- Area: Computational mathematics
- MAD 5404 Foundations of Computational Mathematics II, spring
- Area: Computational mathematics
- MAD 5738 Numerical PDE, fall
- Area: Computational mathematics
- MAD 5420 Numerical Optimization, fall
- Area: Computational mathematics
- MAD 5932 Numerical Linear Algebra, fall and spring
- Area: Computational mathematics
- MAP 5345 Elementary PDE I, fall
- Area: Differential equations
- MAP 5346 Elementary PDE II, spring
- Area: Differential equations
- MAP 5932 Stochastic Differential Equations, occasional
- Area: Differential equations
- MAP 6XXX Financial Engineering II, fall
- Area: Financial mathematics
- MAP 6939r Research Seminar in Financial Mathematics, fall and spring
- Area: Seminar
Approved courses offered by collaborating departments
- CAP 5619 Deep and Reinforcement Learning Fundamentals, spring
- Area: Computer science
- CAP 5638 Pattern Recognition, fall and spring
- Area: Computer science
- CAP 5771 Data Mining, spring
- Area: Computer science
- CIS 5930 Introduction to Data Science, fall
- Area: Computer science
- ISC 5318 High Performance Computing, fall
- Area: Scientific Computing
- ECO 5204 Macroeconomic Theory I, fall
- Area: Economics
- ECO 5281 Financial Economics I
- Area: Economics
- ECO 5282 Financial Economics II
- Area: Economics
- ECO 5715 International Finance, fall
- Area: Economics
- ECO 5416 Econometrics I, various
- Area: Economics
- ECO 5423 Econometrics II, various, but not also STA 5167
- Area: Economics
- ECO 5408 Computational Economics, fall
- Area: Economics
- ECO 5428 Time Series Analysis, spring,
- Area: Economics
- FIN 5515 Investment Management and Analysis, spring
- Area: Finance
- FIN 5537 Financial Derivatives and Risk Management, fall
- Area: Finance
- STA 5167 Statistics in Applications II, spring
- Area: Statistics
- STA 5635 Applied Machine Learning, fall or spring
- Area: Statistics
- STA 5721 High Dimensional Statistics, spring
- Area: Statistics
- STA 5066 Data Management and Analysis with SAS I, fall
- Area: Statistics
- STA 5067 Data Management and Analysis with SAS II, spring
- Area: Statistics
- STA 5207 Applied Regression Methods, fall
- Area: Statistics
- STA 5327 Statistical Inference, spring
- Area: Statistics
- STA 5807 Topics in Stochastic Processes, fall
- Area: Statistics
- STA 5856 Time Series and Forecasting Methods, spring
- Area: Statistics
Sample Schedules
Quantitative Finance/Financial Engineering track
First Fall
- MAP5601 Introduction to Financial Mathematics
- ISC 5305 Scientific Programming
- STA5326 Distribution Theory and Inference or STA 6346 Advanced Probability and Inference I
- MAT5939 Financial Math Proseminar
First Spring
- MAP6621 Financial Engineering I
- Elective
- CAP 5771 Data Mining (elective)
- MAT5939 Financial Math Proseminar
Summer
- Optional elective or internship
Second Fall
- CAP 5768 Introduction to Data Science
- Elective
- Elective
Second Spring
- MAP5615 Monte Carlo in Financial Math
- Elective
- Elective
Actuarial Science track
First Fall
- STA5326 Distribution theory and Inference
- MAP5601 Introduction to Financial Mathematics
- MAP 5932 Financial Mathematics for Actuaries
- MAT 5939 Financial Math Proseminar (elective)
- Actuarial P-Seminar (elective)
First Spring
- STA 5325 Mathematical Statistics
- Elective
- MAP 5932 Short Term Actuarial Math I
- MAT 5939 Financial Math Proseminar (elective)
- Actuarial FM-Seminar (elective)
Summer
- Optional elective or internship
Second Fall
- STA 5207 Applied Regression Methods
- MAP 5177 Long-Term Actuarial Models I
- MAP 5932 Short Term Actuarial Math II
Second Spring
- STA 5856 Time Series and Forecasting Methods
- MAP 5178 Long-Term Actuarial Models II
- Elective
A Ph.D. in financial mathematics culminates in a dissertation of original research written under the direction of a major professor or co-directors and defended in an oral examination.
Ph.D. students are not admitted to any particular professor's research group in advance, but rather arrive in the program under the initial guidance of the director. Students spend their first year in coursework getting to know the faculty, and then find a major professor during the second year. The major professor then takes over as primary academic advisor.
Before submitting a dissertation, a student first advances to candidacy for the Ph.D., as described below. For full time students this normally takes five or six semesters. See also the Timely Progress for Ph.D. Students page.
Complete requirements for the Ph.D. appear on the Mathematics Graduate Bulletin page. This includes attending the regular Financial Math research seminar MAP6939r in each semester after completion of the first two years of courses.
Advancement to Ph.D. Candidacy
Advancement to Ph.D. candidacy requires the following steps:
- Completing Ph.D. degree course requirements
- Passing written qualifying exams
- Obtaining a major professor or co-directors
- Passing the Ph.D. candidacy exam (Advanced Topics Exam - ATE)
When the supervisory committee determines that the student has passed the Ph.D. candidacy exam, the student is formally advanced to candidacy and begins working on the dissertation.
Typical course schedule for the first two years
qual = PhD qualifier course
First Fall
- MAP5601 Introduction to Financial Mathematics (qual)
- STA 6346 Advanced Probability and Inference I
- MAD5403 Foundations of Computational Math, I (qual)
- MAT5939 Financial Math Proseminar
First Spring
- MAP6621 Financial Engineering I (qual)
- MAD 5932 Numerical Linear Algebra or elective
- MAD5404 Foundations of Computational Math, II (qual)
- MAT5939 Financial Math Proseminar
Summer
- Optional elective or internship
Second Fall
- MAA5616 Measure and Integration I
- MAP6xxx Financial Engineering II, or elective
- MAD 5420 Numerical Optimization or elective
Second Spring
- MAP5932 Stochastic Analysis
- MAP5615 Monte Carlo methods in Financial Mathematics
- MAA 5617 Measure and Integraton II, or elective
The financial mathematics major leads to a graduate degree in mathematics and requires an undergraduate background in mathematics courses equivalent to those included in a mathematics major. Applicants should have experience with proof-based mathematics courses at the advanced undergraduate level, and with using computers to solve mathematical problems.
Also, please consult the FSU Math Graduate Program page for further information about graduate study at FSU.
Visit the FSU Graduate School website for information on applying.
Application Deadlines
All M.S. and Ph.D. students are admitted for the fall term only. The fall term normally begins in the last week of August.
December 15 is the deadline for Ph.D. degree applications to receive full consideration for fall admission and financial aid.
April 30 is the deadline for M.S. degree applications to receive full consideration for fall admission, and may in certain cases be considered for limited financial support if available. Applications completed after April 30 but before June 30 may sometimes be considered if space is available.
Consult the director for questions about the degree majors; specific questions about the actuarial science track of the M.S. degree may be sent to the coordinator of actuarial science. Questions about financial support may be directed to the associate chair for graduate studies.
Admissions Checklist: Required Background
- Please include a CV with your application, including the years of study at all institutions of higher education that you attended, along with any significant jobs you held after earning your first Bachelor's degree.
- The following specific math courses are required prerequisites:
- Calculus I, II, and III (multivariate calculus)
- Differential equations
- Linear algebra
- Probability and statistics
- For Ph.D. applicants: Real Analysis or Advanced Calculus
An upper division undergraduate course in proof-based Real Analysis or Advanced Calculus provides the needed background for further coursework in financial mathematics. Representative texts for this material include:
- W. Rudin, Principles of Mathematical Analysis, 3rd edition; or
- S. Abbott, Understanding Analysis, Springer.
- You should have experience with proof-based mathematics courses at the upper-division level.
- You should have some experience programming in a high level computer language like C++, Fortran, Python, and/or Julia before the start of the first semester.
Students are encouraged to take programming as part of their undergraduate coursework. Admitted students unsure of their background are encouraged to consult resources for C++ programming below for guidance and self-study, if needed.
Admissions Checklist: Recommended Background
- The following is recommended background for preparation for the M.S. coursework:
- Real Analysis or advanced calculus.
See above note.
- Numerical Analysis.
Some prior exposure to numerical methods of mathematics is recommended to prepare for MAD 5403-04 Foundations of Computational Mathematics in the first year of the program.
- Finance and Economics.
Some prior coursework in economics and/or finance is recommended.
- Real Analysis or advanced calculus.
- For admission to the Ph.D. degree, it is recommended that students have taken, in addition to the courses listed above:
- A two-semester sequence in Advanced Calculus; and
- Several other upper division mathematics courses, to prepare for the required graduate analysis course in the first year.
Frequently asked questions for Financial Mathematics
1. How much does it cost to study at FSU? Do you give any financial aid?
Visit the Financial Aid page to learn about financial aid and the FSU tuition page for cost of registration at FSU. Currently, Ph.D. students making timely progress toward the degree are financially supported with a Departmental Teaching Assistantship or University Fellowship that includes a stipend and most tuition. M.S. students are normally self-funded, but in some years the Department has a few tuition waivers available.
2. Should I apply for the M.S. or Ph.D.?
The M.S. degree is a two-year coursework degree aimed at professional preparation for the quant world. Students considering the Ph.D. degree need a stronger mathematical background and a greater degree of independence. The Ph.D. usually takes five or six years to complete, and Ph.D. students normally earn the M.S. degree after the first two years.
3. If I am admitted for the M.S. degree only, can I switch to the Ph.D.?
If you were admitted as an M.S. student, but wish to pursue the Ph.D., you may elect the first year Ph.D. qualifier courses, and if successful apply after one year to transfer to the Ph.D. degree. Transfers to the Ph.D. degree are competitive and not guaranteed. See the director for more information.
Internships
Students are encouraged to explore internship opportunities in financial organizations to further improve their understanding of the work of quants in financial markets. Students apply for these opportunities directly; help with doing this is provided in the first year Proseminar.
Some examples of organizations at which our students have been summer interns in the past are listed below.
- Southern Company, Atlanta
- Bloomberg, NY
- CitiMortgage, St Louis
- Yahoo!, Sunnyvale
- Bank of America, NY
- Bell Trading, Chicago
- Federal Home Loan Bank, Atlanta
- Florida State Board of Administration
Professional Organizations
- Bachelier Finance Society
- International Association for Quantitative Finance
- The American Mathematical Society
- The Society for Industrial and Applied Mathematics
Job Links
C++ Resources
Documentation
- C++ tutorial
- Cplusplus.com — the C++ Resources Network
Books — Introductory
- Lecture Notes for COP 3014 — Programming I (a course offered by the CS Department), by Bob Myers
- "C++ Primer (4th Edition)" by Stanley B. Lippman
- "Programming: Principles and Practice Using C++" by Bjarne Stroustrup
- "Introducing C++ for Scientists, Engineers, and Mathematicians" by D.M. Capper, Springer-Verlag
Books — Advanced
- "The C++ Programming Language: Special Edition" by Bjarne Stroustrup
- "Numerical Recipes in C++: The Art of Scientific Computing" by William H. Press, Saul A. Teukolsky, William T. Vetterling, and Brian P. Flannery
- "Scientific and Engineering C++: An Introduction with Advanced Techniques and Examples" by John J. Barton and Lee R. Nackman
- "Financial Instrument Pricing Using C++", by Daniel J. Duffy, Wiley
- "C++ Design Patterns and Derivatives Pricing", by M.S. Joshi, Cambridge
Compilers (free)
- GCC (recommended - all operating systems) (available in the Math Lab)
- Eclipse/CDT