Zivot E., Wang J. Modeling Financial Time Series with S-PLUS

2nd ed. — Springer, 2011. — 1009 p.This book is a guide to analyzing and modeling financial time series using S-PLUS and S+FinMetrics. It is a unique blend of econometric theory, fi- nancial models, data analysis, and statistical programming. It serves as a user’s guide for Insightful’s S+FinMetrics module of statistical functions for financial time series analysis and financial econometrics as well as a gen- eral reference for models used in applied financial econometrics. The format of the chapters in the book is to give a reasonably complete description of a statistical model and how it works followed by illustrations of how to analyze the model using S-PLUS and the functions in S+FinMetrics. In this way, the book stands alone as an introduction to financial time se- ries analysis as well as a user’s guide for S+FinMetrics. It also highlights the general analysis of time series data using the new time series objects introduced in S-PLUS 6.
The chapters in the book cover univariate and multivariate models for analyzing financial time series using S-PLUS and the functions in S+FinMetrics. Chapter one gives a general overview of the use of S-PLUS 6 and highlights certain aspects of the language for statistical modeling. Chapter two intro- duces the new time series objects in S-PLUS 6 and illustrates the specifica- tion, manipulation and visualization of these objects. Chapter three surveys time series concepts used throughout the book. Chapters four through eight cover a variety of topics in the modeling of univariate financial time series, including testing for unit roots, extreme value theory, time series regression models, GARCH models of volatility, and long memory models. Chapter nine introduces rolling analyses of time series models and covers related topics such as technical analysis of financial time series and moving aver- age methods for high frequency data. Chapters ten through fifteen cover models for the analysis of multivariate financial time series. Topics include systems of regression equations, classical and Bayesian vector autoregres- sive models, cointegration, factor models, multivariate GARCH models, and state space models. Chapter 16 covers aspects of modeling time series arising from fixed income financial assets. Chapter 17, written by Victor Yohai and Jiahui Wang, describes robust REGARIMA models that al- low for structural change. Chapters 18 through 23 are new to the Second Edition of the book. These new chapters cover nonlinear regime-switching models, copulas, continuous-time models, the generalized method of moments, semi-nonparametric conditional density models, and the efficient method of moments.