1. Introduction to R
2. Linear Regression
3. Transition from Linear to Nonlinear Regression
4. Nonlinear Regression Modeling
5. The Logistic Regression
6. The Poisson Regression: Models for Count Data
7. Autoregressive Integrated Moving-Average Models
8. Generalized AutoRegressive Conditional Heteroskedasticity Model
9. Cointegration
10. Financial Statistical Modeling in Risk and Wealth Management
Bibliography
Biography
Jenny K. Chen graduated with a Master's and Bachelor's degree in the Department of Statistics and Data Science at Cornell University. With expertise honed through academic pursuits and her current role as a quantitative product manager at Morgan Stanley, she is particularly interested in the applications of statistical modelling in finance and portfolio management. She was the youngest published author at the Joint Statistical Meetings in 2016 and has published several research papers in statistical modelling and data analytics.
"...this text stands out as a thorough introduction that effectively bridges classical statistical methods with real-world financial applications. Its seamless integration of R code, data-driven demonstrations, and stepwise advancement of concepts ensures that readers will not only understand how to run a model, but also why it is suitable for particular financial questions. For instructors teaching finance-focused data analytics and for professionals seeking to enhance their statistical skill set within R, Financial Data Analytics with R is a recommended addition to the bookshelf."
- Tony Sit, Financial Data Analytics with R: Monte-Carlo Validation. Journal of the American Statistical Association, July 2025
