This book represents an integration of theory, methods, and examples using the S-PLUS statistical modeling language and the S+FinMetrics module to facilitate the practice of financial econometrics. It is the first book to show the power of S-PLUS for the analysis of time series data. It is written for researchers and practitioners in the finance industry, academic researchers in economics and finance, and advanced MBA and graduate students in economics and finance. Readers are assumed to have a basic knowledge of S-PLUS and a solid grounding in basic statistics and time series concepts. This edition covers S+FinMetrics 2.0 and includes new chapters.

The field of financial econometrics has exploded over the last decade This book represents an integration of theory, methods, and examples using the S-PLUS statistical modeling language and the S+FinMetrics module to facilitate the practice of financial econometrics. This is the first book to show the power of S-PLUS for the analysis of time series data. It is written for researchers and practitioners in the finance industry, academic researchers in economics and finance, and advanced MBA and graduate students in economics and finance. Readers are assumed to have a basic knowledge of S-PLUS and a solid grounding in basic statistics and time series concepts. This Second Edition is updated to cover S+FinMetrics 2.0 and includes new chapters on copulas, nonlinear regime switching models, continuous-time financial models, generalized method of moments, semi-nonparametric conditional density models, and the efficient method of moments. Eric Zivot is an associate professor and Gary Waterman Distinguished Scholar in the Economics Department, and adjunct associate professor of finance in the Business School at the University of Washington. He regularly teaches courses on econometric theory, financial econometrics and time series econometrics, and is the recipient of the Henry T. Buechel Award for Outstanding Teaching. He is an associate editor of Studies in Nonlinear Dynamics and Econometrics. He has published papers in the leading econometrics journals, including Econometrica, Econometric Theory, the Journal of Business and Economic Statistics, Journal of Econometrics, and the Review of Economics and Statistics. Jiahui Wang is an employee of Ronin Capital LLC. He received a Ph.D. in Economics from the University of Washington in 1997. He has published in leading econometrics journals such as Econometrica and Journal of Business and Economic Statistics, and is the Principal Investigator of National Science Foundation SBIR grants. In 2002 Dr. Wang was selected as one of the "2000 Outstanding Scholars of the 21st Century" by International Biographical Centre.

Provides statistical tools and techniques needed to understandtoday's financial markets The Second Edition of this critically acclaimed text provides acomprehensive and systematic introduction to financial econometricmodels and their applications in modeling and predicting financialtime series data. This latest edition continues to emphasizeempirical financial data and focuses on real-world examples.Following this approach, readers will master key aspects offinancial time series, including volatility modeling, neuralnetwork applications, market microstructure and high-frequencyfinancial data, continuous-time models and Ito's Lemma, Value atRisk, multiple returns analysis, financial factor models, andeconometric modeling via computation-intensive methods. The author begins with the basic characteristics of financialtime series data, setting the foundation for the three maintopics: Analysis and application of univariate financial timeseries Return series of multiple assets Bayesian inference in finance methods This new edition is a thoroughly revised and updated text,including the addition of S-Plus® commands and illustrations.Exercises have been thoroughly updated and expanded and include themost current data, providing readers with more opportunities to putthe models and methods into practice. Among the new material addedto the text, readers will find: Consistent covariance estimation under heteroscedasticity andserial correlation Alternative approaches to volatility modeling Financial factor models State-space models Kalman filtering Estimation of stochastic diffusion models The tools provided in this text aid readers in developing adeeper understanding of financial markets through firsthandexperience in working with financial data. This is an idealtextbook for MBA students as well as a reference for researchersand professionals in business and finance.

This book provides a broad, mature, and systematic introduction to current financial econometric models and their applications to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout the book to apply the models and methods described. The author begins with basic characteristics of financial time series data before covering three main topics: Analysis and application of univariate financial time series The return series of multiple assets Bayesian inference in finance methods Key features of the new edition include additional coverage of modern day topics such as arbitrage, pair trading, realized volatility, and credit risk modeling; a smooth transition from S-Plus to R; and expanded empirical financial data sets. The overall objective of the book is to provide some knowledge of financial time series, introduce some statistical tools useful for analyzing these series and gain experience in financial applications of various econometric methods.

Fundamental topics and new methods in time series analysis Analysis of Financial Time Series provides a comprehensive and systematic introduction to financial econometric models and their application to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout the book to apply the models and methods described. The author begins with basic characteristics of financial time series data before covering three main topics: analysis and application of univariate financial time series; the return series of multiple assets; and Bayesian inference in finance methods. Timely topics and recent results include: Value at Risk (VaR) High-frequency financial data analysis Markov Chain Monte Carlo (MCMC) methods Derivative pricing using jump diffusion with closed-form formulas VaR calculation using extreme value theory based on a non-homogeneous two-dimensional Poisson process Multivariate volatility models with time-varying correlations Ideal as a fundamental introduction to time series for MBA students or as a reference for researchers and practitioners in business and finance, Analysis of Financial Time Series offers an in-depth and up-to-date account of these vital methods.

The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R Labs with real-data exercises, and graphical and analytic methods for modeling and diagnosing modeling errors. These methods are critical because financial engineers now have access to enormous quantities of data. To make use of this data, the powerful methods in this book for working with quantitative information, particularly about volatility and risks, are essential. Strengths of this fully-revised edition include major additions to the R code and the advanced topics covered. Individual chapters cover, among other topics, multivariate distributions, copulas, Bayesian computations, risk management, and cointegration. Suggested prerequisites are basic knowledge of statistics and probability, matrices and linear algebra, and calculus. There is an appendix on probability, statistics and linear algebra. Practicing financial engineers will also find this book of interest.

A new edition of the comprehensive, hands-on guide to financial time series, now featuring S-Plus® and R software Time Series: Applications to Finance with R and S-Plus®, Second Edition is designed to present an in-depth introduction to the conceptual underpinnings and modern ideas of time series analysis. Utilizing interesting, real-world applications and the latest software packages, this book successfully helps readers grasp the technical and conceptual manner of the topic in order to gain a deeper understanding of the ever-changing dynamics of the financial world. With balanced coverage of both theory and applications, this Second Edition includes new content to accurately reflect the current state-of-the-art nature of financial time series analysis. A new chapter on Markov Chain Monte Carlo presents Bayesian methods for time series with coverage of Metropolis-Hastings algorithm, Gibbs sampling, and a case study that explores the relevance of these techniques for understanding activity in the Dow Jones Industrial Average. The author also supplies a new presentation of statistical arbitrage that includes discussion of pairs trading and cointegration. In addition to standard topics such as forecasting and spectral analysis, real-world financial examples are used to illustrate recent developments in nonstandard techniques, including: Nonstationarity Heteroscedasticity Multivariate time series State space modeling and stochastic volatility Multivariate GARCH Cointegration and common trends The book's succinct and focused organization allows readers to grasp the important ideas of time series. All examples are systematically illustrated with S-Plus® and R software, highlighting the relevance of time series in financial applications. End-of-chapter exercises and selected solutions allow readers to test their comprehension of the presented material, and a related Web site features additional data sets. Time Series: Applications to Finance with R and S-Plus® is an excellent book for courses on financial time series at the upper-undergraduate and beginning graduate levels. It also serves as an indispensible resource for practitioners working with financial data in the fields of statistics, economics, business, and risk management.