Computational Finance tracks are available at UIC, jointly through the Department of Mathematics, Statistics, and Computer Science (MSCS) and the Department of Finance. These tracks are available at the master's level in either the Master of Science Degree in the Mathematics and Information Science for Industry (MISI) Program or in the Master of Science Degree in Mathematics with a major concentration in the Applied Mathematics area. The differences are found in the table below. The Computational Finance track is also available at the Mathematics Ph.D. level, with doctoral work in Computational Finance arranged under the supervision of faculty in both MSCS and Finance.

Current Information Link: http://www.math.uic.edu/~hanson/Finance/CompFinance.html

Application Information Contact: Ms. Kari Dueball, Assistant Director of Graduate Studies, MSCS, e-mail: kdueball@uic.edu, phone: 312/996-3041.

Remarks:

^*The recommended track and elective courses should be used to fulfill the required additional courses.

MISI Program links: http://www.math.uic.edu/misi
http://www.math.uic.edu/programs/gradstudies/Degrees_requirements_misi.htm

Applied Math concentration links: http://www.math.uic.edu/~hanson/AppliedMath.html
http://www.math.uic.edu/programs/gradstudies/Degrees_requirements_am.htm

Ph.D. Degree in Mathematics with Concentration in Applied Mathematics:

For the Ph.D. Degree, students must earn a high pass on the MSCS Master's Exam or equivalent, pass two doctoral preliminary exams (one should be the Mathematical Science Preliminary for the Computational Finance track), fulfill the Ph.D. thesis requirement, and earn 96 semester hours of graduate credit. The selection of recommended track and elective courses at the 500 level are the same as those for the Master's Degree with a concentration in Applied Mathematics using the Computational Finance track, but the student probably would be taking a much larger larger of these courses as well as other relevant courses in MSCS and Finance. Doctoral work can be arranged under the supervision of faculty in both MSCS and Finance. For more details, please link to:
http://www.math.uic.edu/programs/gradstudies/Degrees_requirements_phd.htm

Participating Faculty:

• Gilbert W. Bassett, Jr., Departments of Finance (Econometrics, Statistics, Financial Markets, Energy and the Environment, Decision, Risk, and Voting, Statistics and Sports).
• Floyd B. Hanson, Department MSCS (Portfolio Optimization Subject to Jump Events, Computational Finance and Financial Engineering, Computational Stochastic Control, Stochastic Modeling).
• Charles Knessl, Department MSCS (Asymptotic Methods, Singular Perturbation, Stochastic Modeling, Queueing Theory).
• Stanley R. Pliska, Department of Finance (Risk Sensitive Portfolio Management, Dynamic Asset Allocation with Imperfect Information, Interest Rate Derivatives and Term Structure Models).
• Charles Tier, Department MSCS (Analysis of Stochastic Models, Queuing Theory, Financial Mathematics, Numerical Analysis).
• Stephen S. T. Yau, Department MSCS (Control, Information Theory, Financial Mathematics).

• Math 586 Computational Finance: models of financial derivatives, including exotic options and interest rate products, based on partial differential equations with analytic and numerical methods.
• Fin 551 Financial Decision Making: a theoretical yet introductory study of security markets including stocks, bonds, futures, and options; arbitrage, risk neutral valuation of derivative securities, and optimal consumption/investment problems.

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• Fin 571 Empirical Issues in Finance: empirical applications focusing on financial equity data. The course will introduce robust statistical methods, compare them to standard methods, and present their application to problems in Finance.
• Math 574 Applied Optimal Control: introduction to optimal control theory; calculus of variations, maximum principle, dynamic programming, feedback control, linear systems with quadratic criteria, singular control, optimal filtering, stochastic control.
• Math 584 Applied Stochastic Models: applications of stochastic models, diffusion approximations , Brownian motion, stochastic calculus, stochastically perturbed dynamical systems, first passage times.
• MCS 571 Numerical Methods for Partial Differential Equations: finite difference methods for parabolic, elliptic, and hyperbolic differential equations: explicit, Crank-Nicolson implicit, alternating directions implicit, Jacobi, Gauss-Seidel, successive over- relaxation, conjugate gradient, Lax-Wendroff.
• Math 590 Principles of Financial Mathematics: basic principles of asset evaluations, discrete time model, arbitrage and martingales, complete markets, supermartingales and Snell Envelope, Brownian motion, the market price of risk.
• Math 481 Applied Partial Differential Equations: initial value and boundary value problems for second order linear equations, Eigenfunction expansions and Strum-Liouville theory, Green's functions, Fourier transform, Laplace transform.