Statistics and Data Analysis for Financial Engineering (Ruppert, D.; 2011) [Book Reviews]

This is a graduate textbook that originated from a masters' level course taught by the author in the financial engineering program at Cornell University. It is very well written and strikes just the right balance between rigor and accessibility as well as between the breadth of coverage and being easy to navigate. This book is a revised and very significantly expanded version of an earlier book by the same author, Statistics and Finance: An Introduction (Springer 2004). More than half of the material is either new or substantially revised. About one-quarter of the book is devoted to the financial and econometric models widely used for analyzing financial instruments: Chapter 3 is an introduction to bonds; portfolio theory is covered in Chapter 11; the capital asset pricing model (CAPM) in Chapter 16; factor models in Chapter 17; autoregressive conditional heteroskedasticity models in Chapter 18; and basics of risk management in Chapter 19. The rest of the book describes a variety of probabilistic and statistical tools of great importance in the analysis and modeling of financial signals, such as linear regression, cointegration, copulas, heavy-tailed distributions, bootstrapping, autoregressive models, principal component analysis, Markov chain Monte Carlo, and nonparametric regression.