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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.

Required by MSFE students. Covers Basic optimization models, theory and methods for financial engineering, including linear and integer programming, nonlinear optimization, and their applications to portfolio management, index fund tracking, and arbitrage detection; optimization software to solve optimization problems.