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• Computational Finance Seminars in MSCS at UIC

• Finance Workshop in CBA at UIC, every 2nd Thursday 2-3:30pm in 2250UH

• UIC MSCS Computational Finance Options: Webpage version of Department MSCS hand out describing the Computational Finance Track Options for Master's Degree in MISI, Master's Degree in Mathematics with the Applied Math Option, and the Ph.D. Degree in Mathematics with the Applied Math Option, as well as other information on faculty and track or collateral courses.

• Graduate Admission Application Procedures and Deadlines: Webpage for Admission Applications, but see main Graduate Studies page links for more information.

• Professor Gilbert W. Bassett, Jr., Department of Finance (also Professor of Economics),
  • Econometrics
  • Statistics, Financial Markets
  • Energy and the Environment
  • Decision, Risk, and Voting
  • Statistics and Sports

• Professor Jerry L. Bona, Department of Mathematics, Statistics, and Computer Science
  • Mathematical Economics
  • Fluid Mechanics
  • Partial Differential Equations
  • Numerical Analysis
  • Oceanography.

• Professor Floyd B. Hanson (RETIRED), Department of Mathematics, Statistics, and Computer Science
  • Portfolio Optimization Subject to Jump Events
  • Computational Finance and Financial Engineering
  • Computational Stochastic Control
  • Stochastic Modeling Applications
  • Mathematical Bioeconomics

• Professor Charles Knessl, Department of Mathematics, Statistics, and Computer Science
  • American Options and Free Boundary Problems
  • Stochastic Modeling
  • Asymptotic methods
  • Singular perturbation
  • Queueing Theory

• Adjunct Professor Alexander Lipton-Lifschitz (RETIRED), Department of Mathematics, Statistics, and Computer Science,
  and US Manager of Equity Derivatives, Convertible Unit and Global FX, in Global Modelling & Analytics Group, of Credit Suisse First Boston LLP, New York, NY.
  • Financial Engineering
  • Fluid Dynamics
  • Mathematical Physics
    
    

• Professor Stanley R. Pliska (RETIRED), Department of Finance
  • Risk Sensitive Portfolio Management
  • Dynamic Asset Allocation with Imperfect Information
  • Portfolio Management with Taxes and Transaction Costs
  • Interest Rate Derivatives and Term Structure Models

• Professor Charles Tier (RETIRED), Department of Mathematics, Statistics, and Computer Science
  • Analysis of Stochastic Models
  • Queuing Theory
  • Financial Mathematics
  • Numerical Analysis

• Professor Stephen S. T. Yau, Department of Mathematics, Statistics, and Computer Science
  • Control
  • Information Theory
  • Financial Mathematics

• Fin 551 Financial Decision Making, Stanley R. Pliska
  Description: This course will cover much of Professor Pliska's book Introduction to Mathematical Finance, which as the name suggests is a theoretical yet introductory study of security markets including stocks, bonds, futures, and options. Considerable attention will be. given to topics in financial economics such as arbitrage, risk neutral valuation of derivative securities, and optimal consumption/investment problems where the objective is to maximize expected utility. Although the course will involve mathematics presented in a rigorous fashion, the only mathematical prerequisites are knowledge about probability, calculus, linear algebra, and optimization, so economics students who have completed the first year of graduate study should be well qualified. No particular courses in finance are prerequisites, but students should come with an interest in financial markets.

• Math 586 Computational Finance, Charles Tier
  Description: The course begins with a review of financial derivatives and their applications. Models of financial derivatives will be presented such as Black-Scholes model. Models of exotic options as well as interest rate products will be studied in depth. The approach will be based on partial differential equations using analytic and numerical methods.

• Fin 571 Empirical Issues in Finance, Gilbert W. Bassett, Jr.
  Description: The course will cover 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. Problems to be considered include the predictability of asset returns, analysis of market microstructure, multifactor models for predicting returns, risk neutral distributions implicit in options prices, and forecasting portfolio tracking error.

• Math 574 Applied Optimal Control. Floyd B. Hanson.
  Description: 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. Charles Knessl.
  Description: Applications of stochastic models in chemistry, physics, biology, queueing, filtering, and stochastic control, diffusion approximations , Brownian motion, stochastic calculus, stochastically perturbed dynamical systems, first passage times.

• MCS 571 Numerical Methods for Partial Differential Equations. Floyd B. Hanson.
  Description: 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, Fourier stability.

• Math 480 Applied Partial Differential Equations. Various Instructors.
  Description: Initial value and boundary value problems for second order linear equations, Eigenfunction expansions and Strum-Liouville theory, Green's functions, Fourier transform,. Characteristics,. Laplace transform.

• Math 590 Principles of Financial Mathematics, Stephen S. T. Yau
  Description: 1. Probability Theory, 2. Financial Instruments, 3. Basic Principles of Asset Evaluations, 4. One Period Asset Valuation, One Period Pricing Model, 5. Discrete Time Model, Arbitrage and Martingales, Complete Markets, 6. American Options, Supermartingales and Snell Envelope, Stopping Time, Decomposition of Supermartingales, 7. Continuous Time Model for Stock Prices, Brownian Motion, Continuous Martingale Stochastic Calculus, 8. Black-Scholes Differential Equation, The Market Price of Risk.

See Also Full Set of Industrial Math Courses in which Computational Financial Mathematics is a Track:
Mathematical and Information Sciences for Industry (MISI) Courses

See Also the HomePage for Our
Mathematical and Information Sciences for Industry (MISI) Program