Financial Engineering is a multi-disciplinary field where mathematics, statistics, information technology, and computer science are used for solving practical problems that arise in finance. We offer undergraduate courses in financial engineering and an Economics and Finance Track for Industrial Engineering majors. The Master of Science in Financial Engineering program is jointly run by the Department of Industrial and Enterprise Systems Engineering and the Department of Finance. Students are trained with fundamental economic and finance theory as well as state-of-the-art mathematical, statistical, and computing techniques. Our faculty has expertise in areas including derivative securities valuation, optimal portfolio execution, portfolio management, model calibration, volatility modeling, statistical inference of financial models, financial networks, risk management, and high frequency trading. PhD students in financial engineering work with their advisors and develop theory and efficient numerical methods for solving various financial problems.
The suggested list of courses is a recommendation. Graduate students should meet with their advisor to finalize course plans each semester. Detailed course information may be found here.
ISE Courses (first year)
- IE 410 Stochastic Processes & Applic
- IE 411 Optimizaton of Large Systems
- IE 420 Financial Engineering
- IE 510 Applied Nonlinear Programming
- IE 522 Statistical Methods in Finance
- IE 523 Financial Computing
- IE 525 Numerical Methods in Finance
- IE 526 Stochastic Calculus in Finance
ISE Courses (second year)
- IE 524 Optimization in Finance
- IE 515 Stochastic Simulation
- IE 598 Clustering and Approx Method
- IE 598 Inference in Graphical Models
Departments: MATH, STAT, ECON, FIN, ECE, CS
Subjects: Probability theory, stochastic process, deterministic and stochastic control, regression, time series, statistical computing, econometrics, macroeconomics, microeconomics, complex analysis, PDE, numerical methods, data mining/machine learning/pattern recognition/statistical learning, optimization, etc.