Courses in an MS in Financial Engineering Program

How to Earn an MS in Financial Engineering

Programs for a Master of Science in Financial Engineering require a bachelor's degree and take around two years to complete. Courses within each program may vary and may include some of those listed below.

Statistics in Finance

Statistics in finance, or statistical learning, introduces students to information theory and the thermodynamic approach. Students learn and practice data conditioning, extrapolation, and model dissection. Courses like this may also cover other data mining techniques, clustering structures, classification and Radial Basis Functions. Students may also complete case studies to further develop their understanding and skills in financial applications.

Derivatives

Programs may include a course on derivatives, which focuses on fixed-income securities. Students study financial instruments that are sensitive to changes in interest rates such as mortgage-backed securities. Topics covered may include strips and swaps, treasury securities, credit derivatives, as well as the structure of interest rates. Students may also study models such as the Health-Jarrow-Merton model and other one- and two-factor interest rate models.

Risk Management

Within risk management courses, students analyze different financial investments. Students will learn to assess and manage those risks, as well as how risks affect pricing. Courses may cover historical financial crises to better understand their causes and how to prevent a crisis by managing risks. Other topics covered may include futures and their pricing, options contracts, spots pricing, hedging strategies and theoretical approaches.

Computational Finance

In computational finance courses, students learn methods of financial computations that are currently used by financial analysts. Topics covered typically include algorithms, regression and simulation techniques, as well as computational models. Students then learn to apply these models in financial forecasting as well as risk management.

Stochastic Processes

Courses in stochastic processes not only cover stochastic processes but also probability theory. Other topics covered may include stochastic calculus, Brownian motion, Markov chains, as well as renewal and queuing theory. Students may study interest rates models in the course. Some programs may feature more than one class on stochastic processes.

Machine Learning

In machine learning classes, students learn the main components of machine learning within finance. The components covered include applications and principles of statistical learning. Other topics covered may include boosting, logistic regression, neural networks and decision trees. Students may also use various software programs for practice as well as implementation.

Portfolio Theory

Portfolio theory courses introduce portfolio management and how to properly choose and balance portfolio assets. Topics covered typically include combination and balance options, risk measures, mergers, and deferral options. This course is fundamental for students who later create and maintain portfolios for their clients.

Corporate Finance

In corporate finance courses, students learn about various financial topics that apply to corporations and other organizations. Topics covered may include cash flow and valuations, capital structure and equity, as well as mergers and acquisitions. Corporate finance courses are a core class in some programs, while others may require them to already be covered as a prerequisite.

Financial Markets

Financial markets courses introduce students to financial engineering and markets. Students may learn about financial market history and regulation, bond and money markets, as well as trading. Students typically can expand their understanding of financial markets, as well as develop skills in market analysis and valuations.

Econometrics

In econometrics courses, students learn about measures of economics and finance. These courses typically cover statistical techniques and programming languages. Additionally, students may learn about econometric models such as time series models and linear regression models.

Admittance Requirements for an MS in Financial Engineering

MS in Financial Engineering programs typically require applicants to hold a relevant bachelor's degree or a bachelor's degree with qualifying coursework. Additionally, many programs require a specific GPA, usually around 3.0, a resume, references, and a personal statement. Some programs may also require an entrance exam, such as the GRE. Most programs consist of around 30 credit hours and can be completed in two years, although students with qualifying undergraduate credits may be able to transfer them for a shorter program completion time.

Master of Science in Financial Engineering programs typically consist of nine or ten classes, and can often be completed in two years. Courses included in the MS programs may include portfolio theory, machine learning, corporate finance and econometrics.