Applied Financial Econometrics with R
Forecasting, volatility, and asset-pricing models
Preface
This book is an applied introduction to financial econometrics with R. The first chapters build the time-series toolkit used throughout the book: forecast origins, temporal validation, model comparison, and interpretation of prediction errors. They use a monthly sales series as a pedagogical example because the forecasting logic is easier to see before moving to financial applications. The later chapters apply the same equation-to-code approach to financial econometrics: GARCH conditional volatility and Fama-French asset pricing models. The emphasis is on mathematical notation, R implementation, model comparison, and interpretation of time-dependent or cross-sectional results.
This material relies on Hyndman and Athanasopoulos (2021), Hull (2020), and Matt Dancho Business Science IO freely available code examples in R. Some mathematical background is developed only as far as needed to connect model notation, data analysis, code, graphics, and forecast accuracy (R Core Team 2024). As in the philosophy of Knuth (1984), the objective is to explain to human beings what we want a computer to do as literate programming. This is a work in progress and it is under revision.
What’s new in this edition
- Financial econometrics identity: The book now connects time-series forecasting tools, volatility modeling, and asset pricing econometrics.
- Four-chapter structure: Chapter 1 covers classical forecasting with
fpp3. Chapter 2 extends the same sales forecasting problem with supervised machine-learning forecasting models and benchmark comparisons. Chapter 3 introduces GARCH conditional volatility forecasting. Chapter 4 estimates CAPM and Fama-French asset pricing models. - Book format: The material is organized as a Quarto book with chapter navigation, search, local section navigation, code folding, copy buttons, reading progress, and back-to-top controls.