The field of financial econometrics has undergone a rapid explosion in the last decade due to recent developments in theory and software. This book represents an up-to-date overview of theory and methods in the area, using S-PLUS. The first edition appeared around 2002 and since then it has been widely used. Six new chapters have been added, including non-linear regime switching models, copulas, continuous time models, conditional density models and generalized and efficient methods of estimation. Overall, 23 chapters contain cutting edge developments in univariate and multivariate time series models for analysing financial time series data by using S-PLUS. It will be helpful to graduate students and researchers in econometrics and finance, and to practitioners working in the area of finance.

The first two chapters give a basic introduction to the S-PLUS software package. The second chapter illustrates time series data specification, manipulation and visualization. Chapters 3–9 cover a variety of topics in modelling univariate time series data. They provide a background for univariate time series, including autoregressive moving average models, the background and rationale for the unit root test, modelling extreme events such as stock-market crashes, currency crises and trading scandals . Time series regression models are introduced in Chapter 6 and basic autoregressive conditional heteroscedasticity (ARCH) and generalized ARCH (GARCH) models are described in Chapter 7. Long memory time series models are discussed in Chapter 8. Chapter 9 introduces a rolling analysis of time series models and covers technical analysis and moving average methods.

Chapters 10–15 cover models for the analysis of multivariate time series data. Methods for modelling and analysing systems of linear and non-linear regression equations are introduced, followed by a focus on classical and Bayesian vector autoregressive models for multivariate time series data. Chapter 12 deals specifically with co-integration and introduces error correction models. Chapter 13 extends univariate GARCH models to the multivariate context and shows how multivariate GARCH models can be used to model conditional heteroscedasticity. Chapter 14 covers state space models and Chapter 15 deals with multifactor models for asset returns. Chapter 16 covers aspects of modelling time series arising from fixed income securities and derivatives. Chapter 17 describes robust regression autoregressive integrated moving average models that are combinations of autoregressive integrated moving average and regression models.

Chapters 18–23 are new to this edition. Chapter 18 covers recent developments in the area of non-linear time series models, including tests of non-linearity, threshold autoregressive models and Markov switching state space models. Chapter 19 gives an overview of copula function methodology for modelling arbitrary bivariate distribution asset returns, whereas Chapter 20 introduces continuous time models for financial time series. Chapter 21 describes generalized method-of-moment estimation for linear and non-linear models with applications in economics and finance. Chapter 22 provides a general overview of semi-non-parametric conditional density models and introduces a comprehensive set of S-PLUS functions for the estimation of these models. The final chapter gives a general overview of the efficient method-of-moment methodology and draws connections with the generalized method-of-moments and maximum likelihood methodologies.

Overall, the book is well organized and clearly written. It has a good mixture of theory and practical applications. Examples using S-PLUS are given throughout. However, the reader must be familiar with S-PLUS and have a background in mathematical statistics—this book is not suitable for a beginner. It may be recommended as a text for advanced Master of Business Administration courses in financial econometrics for students with a basic knowledge of S-PLUS, and for library purchase.

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