Computational Finance is an area of concentration in the PhD program in Computational Sciences that is administered by the Computational and Data Sciences Department of the College of Science.

The PhD program requires

• 48 credit hours of course work beyond the baccalaureate degree
• 3 credit hours of seminars or colloquia
• successful performance on a comprehensive examination
• 24 credit hours of thesis research culminating in completion and public defense of a dissertation reporting on significant research in the field of computational finance.

Because computational finance is built on the mathematical theory and methods of statistics, the student must take a number of the usual graduate-level statistics courses.

Dissertation Committee

The student then forms a dissertation committee with the advice and approval of the dissertation director and the Computational and Data Sciences graduate coordinator. The committee must consist of a minimum of four members of which at least two must be faculty members in the Department of Computational and Data Sciences. Non-GMU members may serve on the committee with the consent of the GMU faculty members on the committee.

The next major step is to refine the general area of dissertation research to a more specific topic, and then to formulate a program of study. The dissertation committee must approve the program of study.

Program of Study

• CSI core courses (12 credit hours):
  • CSI 700 Numerical Methods
  • CSI 701 Foundations of Computational Sciences
  • CSI 703 Scientific and Statistical Visualization
  • CSI 710 Scientific Databases

• Mathematics/statistics core courses (15 hours):
  • CSI 672 Statistical Inference
  • STAT 656 Regression Analysis
  • CSI 678 Time Series Analysis and Forecasting
  • CSI 771 Computational Statistics
  • CSI 776 or MATH 674 Stochastic Differential Equations

The core courses are offered once per academic year on a regular basis.

CSI 779 Topics in Computational Statistics, which is offered irregularly, often covers topics in computational finance.

It is likely that the student will have a background either in finance or in statistics. The other courses in the program of study depend on the student's background, and on the specific area of computational finance in which the student will work.

Elective courses in the program can be grouped as follows.

• Basic statistics courses:
  • STAT 544 Applied Probability
  • STAT 554 Applied Statistics

• Computational statistics courses:
  • CSI 773 Exploratory Data Analysis
  • CSI 779 Topics in Computational Statistics (may be repeated for credit)
  • CSI 979 Advanced Topics in Computational Statistics (may be repeated for credit)

• Courses in finance:
  • MBA 703 Financial Markets
  • MBA 704 Risk Management and Financial Information
  • MBA 706 Investment Analysis

  The GMU MBA courses are generally designed for a cohort of MBA students. All of the courses listed above require some level of background in general business administration. Students not in the GMU MBA program must get permission of the instructor prior to enrolling in one of these courses.

  There are also courses in finance at other local universities that are available through the Washington Area Consortium.

• Courses in mathematical statistics:
  • CSI 778 Real Analysis and Statistics
  • CSI 876 Measure and Linear Spaces
  • CSI 877 Geometric Methods in Statistics
  • CSI 972 Mathematical Statistics I
  • CSI 973 Mathematical Statistics II

• Other courses in general areas of statistical theory and methods or in specialized areas:
  • STAT 655 Analysis of Variance
  • STAT 657 Nonparametric Statistics
  • STAT 662 Multivariate Statistical Methods
  • STAT 665 Categorical Data Analysis
  • STAT 574 and STAT 674 Survey Sampling I and II
  • STAT 673 Statistical Methods for Longitudinal Data Analysis
  • CSI 842 Linear and Nonlinear Modeling in the Natural Sciences
  • CSI 847 Wavelet Theory
  • CSI 903 Advanced Topics in Scientific Visualization
  • CSI 904 Seminar in Scientific Visualization
  • CSI 976 Statistical Inference for Stochastic Processes
  • CSI 978 Statistical Analysis of Signals

In addition to the 48 hours of formal course work, three hours of colloquia and/or seminars are required. They can be selected from the following one-hour courses, which may be repeated for credit.

• CSI 898 Colloquium in Computational Statistics
• CSI 899 Colloquium in Computational Sciences
• CSI 991 Seminar in Computational Statistics
  

A maximum of 24 credit hours of previous graduate course work may be applied toward the required 48 hours.

After completion of the 48 hours of course work, the next two steps are successful performance on a comprehensive exam and presentation of a dissertation proposal to the committee.

Comprehensive Examination

Dissertation