Financial Engineering Program Area
Discover the tools of quantitative finance and financial technology (fintech) used across a wide range of institutions, from commercial banks to hedge funds. Learn the methods of asset pricing, market efficiency, arbitrage, and derivative analysis. Prepare for a career in fintech through rigorous analytic exploration and examination of financial markets, and related mechanisms, including techniques for risk assessment and management.
Key Topics: Financial markets, arbitrage-free pricing, stochastic calculus, Black-Scholes theory and extensions, derivative pricing, portfolio hedging, and complete and incomplete markets.
Area Lead: Romesh Saigal
IOE 510 Linear Programming I
Advisory Prerequisites: MATH 217, 417, or 419. (3 credits)
Formulation of problems from the private and public sectors using the mathematical model of linear programming. Development of the simplex algorithm; duality theory and economic interpretations. Post-optimality (sensitivity) analysis application and interpretations. Introduction to transportation and assignment problems; special purpose algorithms and advanced computational techniques. Students have opportunities to formulate and solve models developed from more complex case studies and to use various computer programs.
IOE 515 Stochastic Processes
Prerequisites: IOE 316 or Stats 310. (3 credits)
Introduction to non-measure theoretic stochastic processes. Poisson processes, renewal processes, and discrete-time Markov chains. Applications in queueing systems, reliability, and inventory control.
IOE 453 Derivative Instruments
Prerequisites: IOE 201, IOE 310, IOE 366. (3 credits)
The main objectives of the course are first, to provide the students with a thorough understanding of the theory of pricing derivatives in the absence of arbitrage, and second, to develop the mathematical and numerical tools necessary to calculate derivative security prices. We begin by exploring financial markets and the implications of the absence of static arbitrage. We study, for instance, forward and futures contracts. We proceed to develop the implications of no arbitrage in dynamic trading models: the binomial and Black-Scholes models. The theory is applied to hedging and risk management.
IOE 500 IOE MS Seminar
Advisory Prerequisites: IOE master’s student or permission of instructor. (1 credit)
Seminars presented by academic speakers and industry leaders to describe contemporary applications of industrial and operations engineering models and techniques to master’s students in IOE. The focus is on applications but research challenges are addressed as needed. Many speakers also address potential career opportunities for MS students in IOE.
Suggested courses to further explore the financial engineering program area
IOE 452 Corporate Finance
IOE 552 Financial Engineering I
IOE 553 Financial Engineering II
Math 506 Stochastic Analysis for Finance
Math 623 Computational Finance
IOE 511 Continuous Optimization
IOE 512 Dynamic Programming
IOE 516 Stochastic Processes II
IOE 565 Time Series Analysis
Math 625 (or Stat 625) Applied Probability and Stochastic Modeling
Math 626 (or Stat 626) Probability and Random Processes
Stat 500 Applied Statistics
Stat 620 Theory of Probability
IOE 460 Decision Analysis
IOE 517 Game Theory and Operations Applications
IOE 561 Risk Analysis I
IOE 473 Advanced Data Analytics
IOE 570 Experimental Design
EECS 492 Introduction to Artificial Intelligence
EECS 561 Machine Learning