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: <ul>
<li>Analysis and application of univariate financial time series</li> <li>The return series of multiple assets</li> <li>Bayesian inference in finance methods</li> </ul>
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.

Analysis of Financial Time Series

Business & Money

Tsay, Ruey S.

Economics

wiley

A complete set of statistical tools for beginning financial analysts from a leading authority Written by one of the leading experts on the topic, An Introduction to Analysis of Financial Data with R explores basic concepts of visualization of financial data. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-world empirical research. The author supplies a hands-on introduction to the analysis of financial data using the freely available R software package and case studies to illustrate actual implementations of the discussed methods. The book begins with the basics of financial data, discussing their summary statistics and related visualization methods. Subsequent chapters explore basic time series analysis and simple econometric models for business, finance, and economics as well as related topics including: <ul>
<li>Linear time series analysis, with coverage of exponential smoothing for forecasting and methods for model comparison</li> <li>Different approaches to calculating asset volatility and various volatility models</li> <li>High-frequency financial data and simple models for price changes, trading intensity, and realized volatility</li> <li>Quantitative methods for risk management, including value at risk and conditional value at risk</li> <li>Econometric and statistical methods for risk assessment based on extreme value theory and quantile regression</li> </ul>
Throughout the book, the visual nature of the topic is showcased through graphical representations in R, and two detailed case studies demonstrate the relevance of statistics in finance. A related website features additional data sets and R scripts so readers can create their own simulations and test their comprehension of the presented techniques. An Introduction to Analysis of Financial Data with R is an excellent book for introductory courses on time series and business statistics at the upper-undergraduate and graduate level. The book is also an excellent resource for researchers and practitioners in the fields of business, finance, and economics who would like to enhance their understanding of financial data and today's financial markets.

An Introduction to Analysis of Financial Data with R

New statistical methods and future directions of research in time series A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, and signal extraction. They then move on to advanced topics, focusing on heteroscedastic models, nonlinear time series models, Bayesian time series analysis, nonparametric time series analysis, and neural networks. Multivariate time series coverage includes presentations on vector ARMA models, cointegration, and multivariate linear systems. Special features include: <ul>
<li>Contributions from eleven of the worldâ??s leading figures in time series</li> <li>Shared balance between theory and application</li> <li>Exercise series sets</li> <li>Many real data examples</li> <li>Consistent style and clear, common notation in all contributions</li> <li>60 helpful graphs and tables</li> </ul>
Requiring no previous knowledge of the subject, A Course in Time Series Analysis is an important reference and a highly useful resource for researchers and practitioners in statistics, economics, business, engineering, and environmental analysis. An Instructor's Manual presenting detailed solutions to all the problems in he book is available upon request from the Wiley editorial department.

A Course in Time Series Analysis

Science & Math

Tiao, George C.

Peña, Daniel

Mathematics

Wiley-Interscience

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: <ul>
<li>Value at Risk (VaR)</li> <li>High-frequency financial data analysis</li> <li>Markov Chain Monte Carlo (MCMC) methods</li> <li>Derivative pricing using jump diffusion with closed-form formulas</li> <li>VaR calculation using extreme value theory based on a non-homogeneous two-dimensional Poisson process</li> <li>Multivariate volatility models with time-varying correlations</li> </ul>
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.

Provides statistical tools and techniques needed to understand today's financial markets 
The Second Edition of this critically acclaimed text provides a comprehensive and systematic introduction to financial econometric models and their applications in modeling and predicting financial time series data. This latest edition continues to emphasize empirical financial data and focuses on real-world examples. Following this approach, readers will master key aspects of financial time series, including volatility modeling, neural network applications, market microstructure and high-frequency financial data, continuous-time models and Ito's Lemma, Value at Risk, multiple returns analysis, financial factor models, and econometric modeling via computation-intensive methods. The author begins with the basic characteristics of financial time series data, setting the foundation for the three main topics: <ul>
<li>Analysis and application of univariate financial time series</li> <li>Return series of multiple assets</li> <li>Bayesian inference in finance methods</li> </ul>
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 the most current data, providing readers with more opportunities to put the models and methods into practice. Among the new material added to the text, readers will find: <ul>
<li>Consistent covariance estimation under heteroscedasticity and serial correlation</li> <li>Alternative approaches to volatility modeling</li> <li>Financial factor models</li> <li>State-space models</li> <li>Kalman filtering</li> <li>Estimation of stochastic diffusion models</li> </ul>
The tools provided in this text aid readers in developing a deeper understanding of financial markets through firsthand experience in working with financial data. This is an ideal textbook for MBA students as well as a reference for researchers and professionals in business and finance.

Analysis of Financial Time Series (Wiley Series in Probability and Statistics)

Multivariate Time Series Analysis: With R and Financial Applications

An accessible guide to the multivariate time series tools used in numerous real-world applications Multivariate Time Series Analysis: With R and Financial Applications is the much anticipated sequel coming from one of the most influential and prominent experts on the topic of time series. Through a fundamental balance of theory and methodology, the book supplies readers with a comprehensible approach to financial econometric models and their applications to real-world empirical research. Differing from the traditional approach to multivariate time series, the book focuses on reader comprehension by emphasizing structural specification, which results in simplified parsimonious VAR MA modeling. Multivariate Time Series Analysis: With R and Financial Applications utilizes the freely available R software package to explore complex data and illustrate related computation and analyses. Featuring the techniques and methodology of multivariate linear time series, stationary VAR models, VAR MA time series and models, unitroot process, factor models, and factor-augmented VAR models, the book includes: • Over 300 examples and exercises to reinforce the presented content • User-friendly R subroutines and research presented throughout to demonstrate modern applications • Numerous datasets and subroutines to provide readers with a deeper understanding of the material Multivariate Time Series Analysis is an ideal textbook for graduate-level courses on time series and quantitative finance and upper-undergraduate level statistics courses in time series. The book is also an indispensable reference for researchers and practitioners in business, finance, and econometrics.

An accessible guide to the multivariate time series tools used in numerous real-world applications Multivariate Time Series Analysis: With R and Financial Applications is the much anticipated sequel coming from one of the most influential and prominent experts on the topic of time series. Through a fundamental balance of theory and methodology, the book supplies readers with a comprehensible approach to financial econometric models and their applications to real-world empirical research. Differing from the traditional approach to multivariate time series, the book focuses on reader comprehension by emphasizing structural specification, which results in simplified parsimonious VAR MA modeling. Multivariate Time Series Analysis: With R and Financial Applications utilizes the freely available R software package to explore complex data and illustrate related computation and analyses. Featuring the techniques and methodology of multivariate linear time series, stationary VAR models, VAR MA time series and models, unitroot process, factor models, and factor-augmented VAR models, the book includes: • Over 300 examples and exercises to reinforce the presented content • User-friendly R subroutines and research presented throughout to demonstrate modern applications • Numerous datasets and subroutines to provide readers with a deeper understanding of the material Multivariate Time Series Analysis is an ideal textbook for graduate-level courses on time series and quantitative finance and upper-undergraduate level statistics courses in time series. The book is also an indispensable reference for researchers and practitioners in business, finance, and econometrics.

A comprehensive resource that draws a balance between theory and applications of nonlinear time series analysis Nonlinear Time Series Analysis offers an important guide to both parametric and nonparametric methods, nonlinear state-space models, and Bayesian as well as classical approaches to nonlinear time series analysis. The authors―noted experts in the field―explore the advantages and limitations of the nonlinear models and methods and review the improvements upon linear time series models. The need for this book is based on the recent developments in nonlinear time series analysis, statistical learning, dynamic systems and advanced computational methods. Parametric and nonparametric methods and nonlinear and non-Gaussian state space models provide a much wider range of tools for time series analysis. In addition, advances in computing and data collection have made available large data sets and high-frequency data. These new data make it not only feasible, but also necessary to take into consideration the nonlinearity embedded in most real-world time series. This vital guide: • Offers research developed by leading scholars of time series analysis • Presents R commands making it possible to reproduce all the analyses included in the text • Contains real-world examples throughout the book • Recommends exercises to test understanding of material presented • Includes an instructor solutions manual and companion website Written for students, researchers, and practitioners who are interested in exploring nonlinearity in time series, Nonlinear Time Series Analysis offers a comprehensive text that explores the advantages and limitations of the nonlinear models and methods and demonstrates the improvements upon linear time series models.

Nonlinear Time Series Analysis (Wiley Series in Probability and Statistics)

Chen, Rong

See the best price to sell, buy, or rent books by Tsay, Ruey S.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Tsay, Ruey S.

See the best price to sell, buy, or rent books by Tsay, Ruey S.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Analysis of Financial Time Series

An Introduction to Analysis of Financial Data with R

A Course in Time Series Analysis

Analysis of Financial Time Series

Analysis of Financial Time Series

Analysis of Financial Time Series (Wiley Series in Probability and Statistics)

Multivariate Time Series Analysis: With R and Financial Applications

Multivariate Time Series Analysis: With R and Financial Applications

Multivariate Time Series Analysis: With R and Financial Applications

Nonlinear Time Series Analysis (Wiley Series in Probability and Statistics)