Our Master of Quantitative Finance (MQF) program addresses a growing need for professionals with sophisticated quantitative, computational, and financial modeling skills. The MQF prepares you for a wide range of careers in finance: modeling and managing risk; trading; financial engineering; securities pricing; portfolio management; tactical asset allocation; and financial market regulation.
Our MQF degree is STEM-qualified. We offer students cutting-edge quantitative and programming skills plus unique domain expertise in financial institutions, markets and decisions in all major asset classes, including equity, fixed income, derivatives, and FX markets.
The MQF requires 36 credit hours and comprises core courses and 20+ credits of electives. The core courses provide solid fundamentals in economic, statistical and mathematical models relevant to the finance industry. A flexible set of electives let students become specialists within their chosen career paths. Some students undertake ELPs (experiential learning projects) with industry partners.
The MQF degree can be finished in one to two years. Many students prefer the 2-year period to explore internship opportunities or ELPs with high-profile industry partners.
Modern methods of computational statistics and their application to bot practical problems and research. S-Plus and SAS programming with emphasis on S-Plus. S-Plus objects and functions, and SAS procedures. Topics include data management and graphics, Monte Carlo and simulation, bootstrapping, numerical optimization in statistics, linear and generalized linear models, nonparametric regression, time series analysis.
BUFN610: Financial Management
Focuses on the valuation of the real assets of firms as well as the valuation of stocks and bonds, the primary financial assets in an economy. While details vary, the conceptual foundations of valuation boil down to three themes: time value of money, no-arbitrage, and systematic risk.
BUFN671: Advanced Capital Markets
This course covers modern theories and techniques for investments and asset pricing. The main topics covered are: portfolio theory, pricing models, market efficiency, fixed income investment, forwards and futures, and options.
BUFN640: Financial Data Analytics (previously “Financial Econometrics I)
In this course we study standard techniques used in the econometric analysis of financial data and discuss the underlying techniques and focus on the understanding and interpretation.
BUFN650: Machine Learning in Finance (previously “Financial Econometrics II)
A hands-on and application-oriented short course on data management and financial modeling. It introduces students to basic data management techniques and a variety of analytical models used in finance.
BUFN750: Valuation in Corporate Finance
An advanced topics course in Corporate Finance dealing with valuation. Main topics will be, building pro forma statements, cost of capital, using ratios and comparables to value projects and firms, dicounted cash flow valuations, WACC and APV methods of valuation and Real Option Valuations.
BUFN762: Fixed Income
Describes important financial instruments which have market values that are sensitive to interest rate movements. Develops tools to analyze interest rate sensitivity and value fixed income securities. Defines and explains the vocabulary of the bond management business.
BUFN744: Fixed Income Derivatives
Surveys fixed income assets and related securities such as Exchange-traded bond options; bonds with embedded options; floating rate notes; caps, collars, and floors; floating rate notes with embedded options. Also surveys advanced tools for interest-rate and fixed-income portfolio management, including the use of derivative securities, and the application of binomial trees for analysis of options, and a sound understanding of stochastic yield curves.
BUFN766: Financial Engineering
Introduces and applies various computational techniques useful in the management of equity and fixed income portfolios and the valuation of financial derivatives and fixed income securities. Techniques include Monte Carlo Simulation and binomial/lattice pricing models. Emphasis is on bridging theory with the design of algorithms and models that can be directly applied in practice.
BUFN763: Portfolio Risk Management
Provides training that is important in understanding the investment process - the buy side of the financial world. Specifically, the objective is to provide graduate-level instruction in the following topics, both in theory and in using financial markets data to test the basic theory and practice of portfolio choice and equilibrium pricing models and their implications for efficient portfolios.
BUFN764: Quantitative Investment
Provides an introduction to quantitative techniques of selecting equities, as used commonly among long-short equity hedge funds and other quantitative equity asset management companies. Statistical factor models are developed to locate stocks with higher expected returns, based on the observable characteristics of the stocks. Implementation issues, including statistical estimation, backtesting and portfolio construction, are covered, as is performance evaluation.
BUFN773: Institutional Asset Management
Examines how money is managed by organizations such as university endowments, pension funds, mutual funds, hedge funds, and private equity funds. Involves a mixture of finance and economics and emphasizes the incentives professional money managers face within the context of the organizational structure in which they operate. Particular attention is paid to compensation structures and monitoring mechanisms.
BUFN774: Market Microstructure
The course examines---from theoretical, institutional, and empirical perspectives---how prices in speculative markets are determined by the interaction of traders. Topics covered include market making, informed trading strategies, liquidity, bid-ask spreads, transactions costs, market impact, price manipulation, and high-frequency trading. The course examines markets for equities, bonds, commodities, and foreign exchange. There are several empirical exercises using transactions data.
Hedge Fund Management
Global Equity Fund
Experiential Learning Projects
Special Topic: Big Data in Finance
Special Topic: Textual Analysis in Finance
Special Topic: FinTech