Online Financial Engineering Master's Program

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 Itô's lemma, the diffusion equation and parabolic partial differential equations, 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. Prerequisite: Multivariable Calculus, Ma/FE610, and programming in C, C++, or Java.

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 then focuses on financial risk management with emphasis on Value-at-Risk (VAR) methods using general and parametric distributions and VAR as a risk measure. Real world scenarios are studied.

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, two-factor interest rate models, Heath-Jarrow-Merton (HJM) models and credit derivatives: credit default swaps (CDS), collateralized debt obligations (CDOs), and Mortgage- backed securities (MGS).

Three credits for the degree of Master of Science (Financial Engineering). This course is typically conducted as a one-on-one course between a faculty member and a student. A student may take up to two special problems courses in a master’s degree program. A department technical report is required as the final product for this course.

This course covers the design and implementation of financial models using object oriented programming. It discusses advanced applications on quantitative finance with special emphasis on derivatives pricing.

The course offers an overview of modern financial markets for various securities: equities, FX, and fixed income, different types of traders, orders, and market structures, market microstructure models used for describing price formation in dealer markets (inventory models and information-based models), models of the limit-order markets, optimal order execution: optimal order slicing, and maker-versus-taker strategies. The course introduces several typical trading strategies by introducing technical analysis, including trend, momentum, and oscillator-based strategies, arbitrage trading strategies, including pair trading, implementation and methods of strategies back-testing.

This course deals with basic financial derivatives theory, arbitrage, hedging, and risk. The theory discusses Itô's lemma, the diffusion equation and parabolic partial differential equations, 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. Prerequisite: Multivariable Calculus, Ma/FE610, and programming in C, C++, or Java.

This course investigates statistical methods implemented in multiple quantitative trading strategies with emphasis on automated trading and based on combined technical-analytic and fundamental indicators to enhance the trade-decision making mechanism. Topics explore high-frequency finance, markets and data, time series, microscopic operators, and micro-patterns. Methodologies include, but not limited to, Bayesian classifiers, weak classifiers, boosting and general meta- algorithmic emerging methods of machine learning applied to trading strategies. Back-testing and assessment of model risk are explored.