3.0

credits

Average Course Rating

Computational and numerical methods in financial methods. Topics include Monte Carlo and finite difference methods, optimization, linear programming, dynamic programming, parametric estimation, model calibration, and time series analysis, with a focus on financial applications, such as path dependent options, stochastic volatility models, local volatility models, American options, multi-asset derivatives, interest rate models, and portfolio management. Designed for second-year graduate students in financial mathematics or data science who have a foundational knowledge of finance and familiarity with computing. Prerequisites: Introduction to Financial Derivatives 553.644, Stochastic Processes and Applications to Finance 553.627, and proficiency in a programming language (Python, C, Matlab, or R).