MSFE Program Schedule

Includes linear equations, matrix algebra, multivariate calculus, static optimization, comparative static analysis, constrained optimization, and Kuhn-Tucker conditions.

Begins with a review of probability and statistics. Remainder of course is spent discussing the Classical linear regression model, least squares and maximum likelihood estimation, finite and asymptotic sample properties, inference, prediction, and nonlinear optimization.

Fixed income expertise is a crucial skill set for success in a financial career. Course topics include interest rates, term structure, risk, valuation, and credit analysis of the major market segments (treasuries, corporates, asset-backed and international bonds).

Students manage an actual portfolio. Topics include portfolio management and security analysis with the current macroeconomic context.

Introduction to quantitative methods and computer applications applicable in financial modeling. Covers financial statement modeling, asset allocation, risk analysis, scenario generation, and option pricing through the introduction and use of econometric modeling, decision analysis, simulation, and optimization techniques using modern software.

Among other financial principles, this course covers multipored capital budgeting under uncertainty, real options analysis, optimal capital structures, acquisition valuation, and optimal dividend policy.

Analyzes consumer and producer theory exploring the implications for resource allocation and market efficiency and then gradually transitioning through intertemporal economics to finance theory.

Students manage an actual portfolio. Topics include portfolio management and security analysis within the current macroeconomic context.

This course teaches analysis of financial data and computational tools in the study of modern applied financial econometrics.

Focuses on functionality of financial markets: how financial markets are structured, the externalities associated with speculation and liquidity provision within these markets, measurement of transaction costs, and the origin of liquidity and volatility.

This course covers forwards, futures, swaps and options. By the end of the course, students will have a solid understanding of how these products work, how they are used, how they are priced and how financial institutions trade them for the purposes of speculation and hedging.

This course introduces several fundamental concepts and methods for machine learning, including basic learning algorithms and techniques, and their applications in economics and finance, as well as general questions related to analyzing and handling large data sets.

Master’s level research.