> For the complete documentation index, see [llms.txt](https://cfrm.franco.biz/program/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://cfrm.franco.biz/program/second-quarter/cfrm-502-financial-data-science.md).

# CFRM 502: Financial Data Science

> Reading Material:
>
> * *Statistics and Data Analysis for Financial Engineering with R*, D. Ruppert.
> * *Introduction to Time Series and Forecasting*, P, Brockwell.

This course covers the main statistical methods used for analyzing financial data. Our focus will be roughly evenly divided between the theory of these methods and their implementation using the R programming language. The topics covered will include linear regression, multiple linear regression, nonlinear regression, nonparametric regression, time series analysis, ARMA and ARIMA models, GARCH models, cointegration and VAR models.

1. Linear Regression
2. Multiple Linear Regression
3. Nonlinear Regression
4. Nonparametric Regression
5. Time Series Analysis
6. ARMA and ARIMA models
7. GARCH models
8. Cointegration
9. VaR Models