Curriculum Overview

This course provides the mathematical foundation for understanding modern financial theory. It includes topics such as basic probability, random variables, discrete continuous distributions, random processes, Brownian motion and an introduction to Ito’s calculus. Applications to financial instruments are discussed throughout the course.

This course deals with basic financial derivatives theory, arbitrage, hedging and risk. The theory discusses Ito’s lemma, the diffusion equation and parabolic partial differential equations, and the Black-Scholes model and formulae. The course includes applications of asset price random walks, the log-normal distribution and estimating volatility from historic data. Numerical techniques, such as finite difference and binomial methods, are used to value options for practical examples. Financial information and software packages available on the Internet are used for modeling and analysis.

This course provides computational tools used in industry by the modern financial analyst. The current financial models and algorithms are further studied and numerically analyzed using regression and time series analysis, decision methods, and simulation techniques. The results are applied to forecasting involving asset pricing, hedging, portfolio and risk assessment, some portfolio and risk management models, investment strategies, and other relevant financial problems. Emphasis will be placed on using modern software.

This course introduces the modern portfolio theory and optimal portfolio selection using optimization techniques, such as linear programming. Topics include contingent investment decisions, deferral options, combination options, and mergers and acquisitions. The course introduces various concepts of financial risk measures.

This course deals with fixed-income securities and interest-rate sensitive instruments. Topics include term structure of interest rates, treasury securities, strips, swaps, swaptions, one-factor and two-factor interest rate models, Heath-Jarrow-Merton (HJM) models, and credit derivatives — credit default swaps, collateralized debt obligations, and mortgage-backed securities.

This course is designed for FE students undertaking a research or a project in financial engineering either individually or as a group. The project may be suggested either by faculty members or industry senior managers associated with your internship, as well as any internship that a student may receive through this course. The goal of this course is to involve students in developing research skills, communication skills while keeping their interest in result-oriented techniques. The ability to work on a research-oriented project in a group environment and train their professional presentation and scientific writing skills lead to a competitive graduate who is ready to lead in the work place.

This concentration emphasizes the design and implementation of financial trading systems in dynamic markets, with special focus on how software and automated decision support systems play roles in trading strategies. To complete this concentration, students must take the following courses:

• FE 545 Design, Patterns and Derivatives Pricing

• FE 570 Market Microstructure and Trading Strategies

• FE 620 Pricing and Hedging

• FE 670 Algorithmic Trading Strategies

The Financial Analytics concentration emphasizes statistical learning methods and database skills, preparing students to develop tools to manage enterprise-level challenges. Students apply data-driven solutions to complex financial problems in preparation for an industry in increasing need of such skills. To complete this concentration, students must take the following courses:

• FE 582 Foundations of Financial Data Science (2 credits)

• FE 513 Financial Lab: Database Design (1-credit lab)

• FE 590 Statistical Learning in Finance

• FA 595 Financial Technology (FinTech)

• FE 550 Data Visualization Applications

Technology’s impact on market fundamentals means managers must understand the financial system, its environment and the risk measures that help quantify risk in its multiple hierarchies. Courses in this concentration emphasize a blend of technology and business to help graduates see the financial landscape from a systemic perspective, and to analyze and manage risk efficiently. To complete this concentration, students must take the following courses:

• FE 535 Introduction to Risk Management

• FE 610 Stochastic Calculus for Financial Engineers

• FE 635 Financial Enterprise Risk Engineering

• FE 655 Systemic Risk and Financial Regulation

Students also complete an additional elective with advisor permission.

Proper statistical analysis, supported by new technology tools, helps managers assess markets and build products to create competitive advantages for the enterprise. This concentration gives students insight into technology-driven opportunities in finance through advanced data analytics. To complete this concentration, students must take the following courses:

• FE 541 Applied Statistics with Applications in Finance

• FE 542 Time Series and Applications to Finance

• FE 590 Statistical Learning in Finance

• FE 610 Stochastic Calculus for Financial Engineers

This concentration goes beyond basic programming and computing skills to teach students to use quantitative models to manage large financial data sets. Students learn financial computing models, financial databases, financial engineering software and specialized programming languages. To complete this concentration, students must take the following courses:

• FE 505 Financial Lab: Technical Writing in Finance (1 credit)

• FE 522 C++ Programming in Finance

• FE 511 Introduction to Bloomberg and Thomson Reuters (1-credit lab)

• FE 699 Project in Financial Computing (2 credits)

Students also complete an additional elective with advisor permission.