Johns Hopkins University 3 credits Applied Mathematics & Statistics
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). Prerequisite(s): EN.553.644 AND EN.553.627
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