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Analysis of Financial Time Series,9780470414354
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Analysis of Financial Time Series

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Edition:
3rd
ISBN13:

9780470414354

ISBN10:
0470414359
Format:
Hardcover
Pub. Date:
8/30/2010
Publisher(s):
Wiley
List Price: $154.66

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Customer Reviews

Awesome Practical book  April 16, 2011
by


I found the book especially simple as an introduction to this field. This textbook 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 textbook 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. I highly recommend this cheap textbook for all students and readers interested in more detailed treatment.






Analysis of Financial Time Series: 5 out of 5 stars based on 1 user reviews.

Summary

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.

Praise for the Second Edition

". . . too wonderful a book to be missed by anyone who works in time series analysis."—Journal of Statistical Computation and Simulation

"All in all this is an excellent account on financial time series...with plenty of intuitive insight of how exactly these models work..." —MAA Reviews

Since publication of the first edition, Analysis of Financial Time Series has served as one of the most influential and prominent works on the subject. This Third Edition now utilizes the freely available R software package to explore empirical financial data and illustrate related computation and analyses using real-world examples. Retaining the fundamental and hands-on style of its predecessor, this new edition continues to serve as the cornerstone for understanding the important statistical methods and techniques for working with financial data.

Accessible explanations and numerous interesting examples assist readers with understanding analysis and application of univariate financial time series; return series of multiple assets; and Bayesian inference in finance methods. The latest developments in financial econometrics are explored in-depth, such as realized volatility, volatility with skew innovations, conditional value at risk, statistical arbitrage, and applications of duration and dynamic-correlation models. Additional features of the Third Edition include:

Applications of nonlinear duration models throughout all discussion of high-frequency data analysis and market microstructure

Newly added applications of nonlinear models and methods

An updated chapter on multivariate time series analysis that explores the relevance of cointegration to pairs trading

A new, unified approach to value at risk (VaR) via loss function

An introduction to extremal index for dependence data in the discussion of extreme values, quantiles, and value at risk

The use of both R and S-PLUS® software with the book's numerous examples and exercises ensures that readers can reproduce the results shown in the book and apply the detailed steps and procedures to their own work. New and updated exercises throughout provide opportunities to test comprehension of the presented material, and a related Web site houses additional data sets and related software programs.

Analysis of Financial Time Series, Third Edition is an ideal book for introductory courses on time series at the graduate level and a valuable supplement for statistics courses in time series at the upper-undergraduate level. It also serves as an indispensible reference for researchers and practitioners working in business and finance.

Author Biography

RUEY S. TSAY, PhD, is H. G. B. Alexander Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. Dr. Tsay has written over 100 published articles in the areas of business and economic forecasting, data analysis, risk management, and process control, and he is the coauthor of A Course in Time Series Analysis (Wiley). Dr. Tsay is a Fellow of the American Statistical Association, the Institute of Mathematical Statistics, the Royal Statistical Society, and Academia Sinica.

Table of Contents

Financial Time Series and Their Characteristics
Asset Returns
Distributional Properties of Returns
Processes Considered
Linear time series
Stationarity
Autocorrelation
Linear time series
Simple AR models
Simple MA models
Simple ARMA Models
Unit-Root Nonstationarity
Seasonal Models
Regression with Correlated Errors
Consistent Covariance Matrix Estimation
Long-Memory Models
Volatility models
Characteristics of Volatility
Structure of a Model
Model Building
Testing for ARCH Effect
The ARCH Model
The GARCH Model
The Integrated GARCH Model
The GARCH-M Model
The Exponential GARCH Model
The Threshold GARCH Model
The CHARMA Model
Random Coefficient Autoregressive Models
The Stochastic Volatility Model
The Long-Memory Stochastic Volatility Model
Application
Alternative Approaches
Kurtosis of GARCH Models
Nonlinear Models and Their Applications
Nonlinear Models
Modeling
Forecasting
Application
High-Frequency Data Analysis and Market Microstructure
Nonsynchronous Trading
Bid-Ask Spread
Empirical Characteristics of Transactions Data
Models for Price Changes
Duration Models
Nonlinear Duration Models
Bivariate Models for Price Change and Duration
Application
Continuous-Time Models and Their Applications
Options
Some Continuous-Time Stochastic Processes
Ito's Lemma
Distributions of Price and Return
Black-Scholes Equation
Black-Scholes Pricing Formulas
An Extension of Ito's Lemma
Stochastic Integral
Jump Diffusion Models
Estimation of Continuous-Time Models
Extreme Values, Quantiles, and Value at Risk
Value at Risk
RiskMetrics
An Econometric Approach to VaR Calculation
Quantile Estimation
Extreme Value Theory
Extreme Value Approach to VaR
A New Approach to VaR
The Extremal Index
Multivariate Time Series Analysis and Its Applications
Weak Stationarity and Cross-Correlation Matrices
Vector Autoregressive Models
Vector Moving-Average Models
Vector ARMA Models
Unit-Root Nonstationarity and Cointegration
Cointegrated VAR Models
Threshold Cointegration and Arbitrage
Pairs Trading
Principal Component Analysis and Factor Models
A Factor Model
Macroeconometric Factor Models
Fundamental Factor Models
Principal Component Analysis
Statistical Factor Analysis
Asymptotic Principal Component Analysis
Multivariate Volatility Models and Their Applications
Exponentially Weighted Estimate
Some Multivariate GARCH Models
Reparameterization
GARCH Models for Bivariate Returns
Higher Dimensional Volatility Models
Factor-Volatility Models
Application
Multivariate t Distribution
State-Space Models and Kalman Filter
Local Trend Model
Linear State-Space Models
Model Transformation
Kalman Filter and Smoothing
Missing Values
Forecasting
Application
Markov Chain Monte Carlo Methods with Applications
Markov Chain Simulation
Gibbs Sampling
Bayesian Inference
Alternative Algorithm
Linear Regression With Time Series Errors
Missing Values and Outliers
Stochastic Volatility Models
A New Approach to SV Estimation
Markov Switching Models
Forecasting
Other Applications
Table of Contents provided by Publisher. All Rights Reserved.


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