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Basic optimization models, theory and methods for financial engineering including linear, quadratic, nonlinear, dynamic integer, and stochastic programming; applications to portfolio selection, index fund tracking, asset management, arbitrage detection, option pricing and risk management; optimization software for classes of optimization problems. Projects requiring building optimization models based on financial market data and solutions using optimization solvers. Course Information: 2 graduate hours. No professional credit. May be repeated in the same or separate semesters if topics vary to a maximum of 4 hours. Prerequisite: FIN 500 and MATH 257 or equivalent.

Elective for MSFE students. Covers practical optimization tools and techniques, including dynamic programming, stochastic programming, robust optimization; and their applications to risk evaluation, asset/liability/cash management, and order executions. Projects requiring building optimization models based on financial market data and solutions using optimization solvers.