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IEDA4520 Numerical Methods for Financial Engineering
Topics
• Monte Carlo simulation
– Principles of MC methods and derivatives pricing
– Generating sample paths
– Variance reduction techniques
– Estimation sensitivities
– Nested simulation for risk management
• Machine learning methods
– CAPM and multi-factor models
– Regularized linear regression, tree-based methods, kernel methods
– Applications in asset pricing
• Time series models
– Exponential smoothing
– Autogressive models
– Moving average models
Programming We will be using R for instruction, but Python is also acceptable for homework assignments and projects.
Reference Books
• Paul Glasserman (2003). Monte Carlo Methods in Financial Engineering, Springer.
• Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani (2021). An Introduction to Statistical Learning, 2nd edition, Springer. (https://www.statlearning.com/)
• Rob J. Hyndman and George Athanasopoulos (2021). Forecasting: Principles and Practice, 3nd edition, Otexts. (https://otexts.com/fpp3/)
Assessment
• Homework assignments (30%)
• Midterm exam (30%)
• Group project (40%)
Logistics
• Lectures: Monday and Wednesday 9:00–10:20am, Room 5508 (Lift 25-26)
• Tutorials: Tuesday (once every two weeks, 6 times in total) 4:30–5:20pm, Room 3207 (Lift 21)