An Introduction to Analysis of Financial Data

This book provides a systematic and mathematically accessible introduction to financial econometric models and their applications in modeling and predicting financial time series data. It emphasizes 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. S-Plus® commands and illustrations are used extensively throughout the book in order to highlight accurate interpretations and graphical representations of financial data. Exercises are included in order to provide readers with more opportunities to put the models and methods into everyday practice. The tools provided in the text aid readers in developing a deeper understanding of financial markets through firsthand experience in working with financial data, most importantly without needless computation.

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. A Fellow of the American Statistical Association, the Institute of Mathematical Statistics, and Academia Sinica, Dr. Tsay is author of Analysis of Financial Time Series, Third Edition and coauthor of A Course in Time Series Analysis.

Financial Data and Their Properties	p. 1
Asset Returns	p. 2
Bond Yields and Prices	p. 7
Implied Volatility	p. 10
R Packages and Demonstrations	p. 11
Examples of Financial Data	p. 17
Distributional Properties of Returns	p. 20
Visualization of Financial Data	p. 27
Some Statistical Distributions	p. 32
Linear Models for Financial Times Series	p. 39
Stationarity	p. 40
Autocorrelation	p. 43
Linear time series	p. 49
Simple AR models	p. 51
Simple MA models	p. 69
Simple ARMA Models	p. 78
Unit-Root Nonstationarity	p. 86
Exponential Smoothing	p. 94
Seasonal Models	p. 97
Regression with Correlated Errors	p. 108
Long-Memory Models	p. 115
Model Comparison and Averaging	p. 118
Case Studies of Linear Time Series	p. 127
Weekly Regular Gasoline Price	p. 128
Global Temperature Anomalies	p. 139
U.S. Monthly Unemployment Rates	p. 156
Volatility models	p. 175
Characteristics of Volatility	p. 176
Structure of a Model	p. 177
Model Building	p. 180
Testing for ARCH Effect	p. 180
The ARCH Model	p. 184
The GARTH Model	p. 197
The Integrated GARCH Model	p. 209
The GARCH-M Model	p. 211
The Exponential GARCH Model	p. 213
The Threshold GARCH Model	p. 219
Asymmetric Power ARCH Models	p. 221
An Non-symmetric GARCH Model	p. 224
The Stochastic Volatility Model	p. 226
Long-Memory Stochastic Volatility Models	p. 227
Alternative Approaches	p. 229
Applications of Volatility Models	p. 241
GARCH Volatility Terms Structure	p. 242
Option Pricing and Hedging	p. 245
Time-varying Correlations and Betas	p. 248
Minimum Variance Portfolios	p. 256
Prediction	p. 260
High-Frequency Financial Data	p. 271
Nonsynchronous Trading	p. 272
Bid - Ask Spread of Trading Prices	p. 275
Empirical Characteristics of Trading Data	p. 278
Models for Price Changes	p. 285
Duration Models	p. 296
Realized Volatility	p. 305
Value at Risk	p. 325
Risk Measure and Coherence	p. 326
Remarks on Calculating Risk Measures	p. 334
RiskMetrics	p. 335
An Econometric Approach	p. 342
Quantile Estimation	p. 349
Extreme Value Theory	p. 354
An Extreme Value Approach to VaR	p. 364
Peaks Over Thresholds	p. 369
The Stationary Loss Processes	p. 378
Table of Contents provided by Publisher. All Rights Reserved.

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An Introduction to Analysis of Financial Data With R

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Table of Contents

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