An Introduction to Analysis of Financial Data With R
by Tsay, Ruey S.Edition: 1st
Format: Hardcover
Pub. Date: 2012-10-29
Publisher(s): Wiley
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Summary
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.
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. 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.
Table of Contents
| 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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