MS in Financial Engineering Curriculum

MSFE Curriculum


MS in Financial Engineering Curriculum

All students enrolled in the MS in Financial Engineering Program must successfully complete 30 graduate credit hours in a common set of analytical, financial, and computational courses. The program includes a capstone practicum project with a financial services firm. The students will have first hand experience solving real-world problems and make final presentations to a group of potential recruiters. The program design allows students to complete the course requirements in one or two calendar years, provided the set of prerequisites are met.

MS in Financial Engineering Curriculum Overview

Analytical Core required courses: 

MATH 467 STOCHASTIC CALCULUS (3 CREDITS): Brownian Motion, Martingales. Introduction to the theory of Stochastic Calculus, Itô Formula, and Stochastic Differential Equations, Black-Scholes model. Development of the Martingale Representation Theorem and Girsanov's theorem for change of measure. Multidimensional Stochastic Calculus. Applications to different problems from finance, physics, biology, etc.

MATH 468 FINANCIAL STOCHASTIC ANALYSIS (3 CREDITS): Expect to be challenged via rigorous use of theoretical framework to derivation (mathematical proofs).  Problems are created in a way that make students think about the material. Some homework requires coding in order to price options. Using programming of choice (python, excel, etc.) learn how to price derivative securities using binomial options pricing and Black-Scholes models.  Application of Stochastic Calculus to the pricing of a variety of financial instruments: multiple stock models, American and exotic options, and foreign currency interest rate.  Hedging and pricing by arbitrage in the setting of binary trees and Black-Scholes model. Heath-Jarrow-Morton model for the term structure of interest rates and short rate models. 

Programing Language Used: Python

Select one of the following Statistics courses:


Using programing language learn the probabilistic and statistical functions relevant to real world analysis.  Theory and applications of stochastic processes. Limit theorems, introduction to random walks, Markov chains, Poisson processes, birth and death processes, and Brownian motion. Applications to financial mathematics, biology, business and engineering.

Programing Language Used: R

MATH 312 STATISTICAL COMPUTING AND APPLICATIONS (3 CREDITS): Use of statistical computing packages; exploratory data analysis; Monte Carlo methods; randomization and resampling, application and interpretation of a variety of statistical methods in real world problems.

Select one of the following computation modeling courses:

ECO 415 ECONOMETRICS I (3 CREDITS): Learn how to analyze computer output of regression models.  Computer applications of standard econometric techniques using regression analysis in a single-equation context. Discussion of problems of multicollinearity, heteroscedasticity and autocorrelation. An introduction to simultaneous equation models, identification and estimation problems.

Programing Language Used: R


Select one of the following Industrial Engineering courses:

ISE 426 OPTIMIZATION MODELS AND APPLICATIONS (3 CREDITS): Modeling and analysis of operations research problems using techniques form mathematical programming. Linear programming, integer programming, multicriteria optimization, stochastic programming and nonlinear programming using an algebraic modeling language. This course is a version of IE 316 for graduate students, with research projects and advanced assignments. Closed to students who have taken IE 316.

Programing Language Used: AMPL

ISE 429 STOCHASTIC MODELS AND APPLICATIONS (3 CREDITS): Introduction to stochastic process modeling and analysis techniques and applications. Generalization of the Poisson process; renewal theory, queueing, and reliability; Brownian motion and stationary processes. This course is a version of IE 39 for graduate students, with research projects and advanced assignments. Closed to students who have taken IE 339. 

​Finance Core required courses:

GBUS 421 ADVANCED INVESTMENTS (3 CREDITS): Course provides comprehensive and exhaustive lecture implementation. Advanced topics relating to specific areas within investment finance such as valuation/security analysis; portfolio/risk management; fixed investment securities; mutual funds; hedge funds; microstructure; and trading. Consent of designated finance faculty representative required.  Make extensive use of Bloomberg Terminals to complete assignments based on real world calculations (pricing bonds, duration/convexity, analyzing CMOs)

Bloomberg Terminal and Excel Used

GBUS 422 DERIVATIVES AND RISK MANAGEMENT (3 CREDITS): Project based course focused on tracking the price value of options and futures to see how price changes affect a margin account.  The theory and application of a variety of derivative instruments (options, futures contracts, etc.) used in corporation finance and the financial services industry. The focus is on the risk management application vs. a rigorous development of option pricing theory and similar topics. Consent of designated finance faculty representative required.

GBUS 424 ADVANCED TOPICS IN FINANCIAL MANAGEMENT (3 CREDITS): Advanced and stimulating case study based topics relating to specific areas of corporate finance such as: theoretical and empirical examination of recent developments in financial management, asset valuation and capital budgeting including the role of uncertainty, imprecise forecasts, risk preferences, inflation, market conditions, and the global marketplace, working capital management, leasing, mergers, and financing. The course content may vary between instructors or each time the course is offered. Consent of designated finance representative.

Programing Language Used: Python, SAS / Matlab

Computing Core required course:

ISE 447 FINANCIAL OPTIMIZATION (3 CREDITS): Making optimal financial decisions under uncertainty. Financial topics include asset/liability management, option pricing and hedging, risk management, and portfolio optimization. Optimization techniques covered include linear and nonlinear programming, integer programming, dynamic programming, and stochastic programming. Emphasis on use of modeling languages and solvers in financial applications. Requires basic knowledge of linear programming and probability. This course is a version of IE 347 for graduate students and requires advanced assignments. Credit will not be given for both IE 347 and IE 447.

Capstone Practicum:

GBUS 492 PRACTICUM CAPSTONE (3 CREDITS): This course provides an engagement with real-world business problems/projects over the entire course of the semester. Projects often go beyond the semester and enable the student to be continuously engaged in developing new skills and enhancing their network.  A Capstone Project is the creation of an analysis, tool, product of potential value to the project sponsor (Consulting, Blockchain, Financial, Government entities). By working on real-world projects, with real-world data, students will use techniques and data science tools learned in course works but also gleaned from the project itself. Students will benefit from the interaction with business executives and thus enhance their job network.  Students may also be exposed to tools, techniques not covered within the curriculum therefore enhancing the students overall reach.

Programing Language Used: Varied

Certificate Programs are available in Data Science & Financial Analytics, Quantitative Risk Management or Financial Operations Research by completing an additional two courses for a total of 36 credit hours.

Candidates for the MS in Financial Engineering degree do not need to apply initially for certificate programs - students would meet with the Program Directors to select their certificate choice (if any) once they are enrolled in the program.

Students with equivalent courses from an undergraduate degree program will be given credit for fulfilling the field requirement and will be permitted to replace the credits from the list of approved electives. The program director must approve the student’s choice of electives.

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