36-466 Special Topics: Statistical Methods in Finance
9 units, Carnegie Mellon University
Statistical methods are fundamental to modern finance. As financial data grow in volume and complexity, statistical models are crucial to quantifying the risks and potential rewards of various financial products, for both those buying and selling. This course introduces core statistical techniques used in finance while reinforcing key concepts from prior statistics coursework. Topics will include, but are not limited to, the following: model calibration to historical data, uncertainty quantification, benefits and limitations of geometric Brownian motion, Poisson process and jump models for asset prices, interest rate and yield curve models, CAPM and factor models, portfolio optimization, and financial time series models. Students will also learn data exploration tools essential for quantitative analysis. Python will be used throughout the course since it is the standard package used in finance. No prior experience with Python is required.
Prerequisites: (36-226 (min C) or 36-326 (min C) or 36-236 (min C)) and (21-242 or 21-240 or 21-241)
Offered: Fall 2026, Fall 2025
Courseweave needs JavaScript to show sections, workloads and planning tools.