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| Document Type: | Book |
|---|---|
| All Authors / Contributors: |
Ruey S Tsay |
| ISBN: | 9780470890813 0470890819 |
| OCLC Number: | 775590876 |
| Description: | xiv, 390 pages : illustrations ; 25 cm |
| Contents: | 1 Financial Data and their Properties 1 -- 1.1 Asset Returns 2 -- 1.2 Bond Yields and Prices 7 -- 1.3 Implied Volatility 10 -- 1.4 R Packages and Demonstrations 12 -- 1.4.1 Installation of R Packages 12 -- 1.4.2 The Quantmod Package 12 -- 1.4.3 Some Basic R Commands 16 -- 1.5 Examples of Financial Data 17 -- 1.6 Distributional Properties of Returns 20 -- 1.6.1 Review of Statistical Distributions and Their Moments 20 -- 1.7 Visualization of Financial Data 27 -- 1.8 Some Statistical Distributions 32 -- 1.8.1 Normal Distribution 32 -- 1.8.2 Lognormal Distribution 32 -- 1.8.3 Stable Distribution 33 -- 1.8.4 Scale Mixture of Normal Distributions 33 -- 1.8.5 Multivariate Returns 34 -- Exercises 36 -- References 37 -- 2 Linear Models for Financial Time Series 39 -- 2.1 Stationarity 40 -- 2.2 Correlation and Autocorrelation Function 43 -- 2.3 White Noise and Linear Time Series 50 -- 2.4 Simple Autoregressive Models 51 -- 2.4.1 Properties of AR Models 52 -- 2.4.2 Identifying AR Models in Practice 60 -- 2.4.3 Goodness of Fit 67 -- 2.4.4 Forecasting 67 -- 2.5 Simple Moving Average Models 69 -- 2.5.1 Properties of MA Models 72 -- 2.5.2 Identifying MA Order 73 -- 2.5.3 Estimation 74 -- 2.5.4 Forecasting Using MA Models 75 -- 2.6 Simple ARMA Models 78 -- 2.6.1 Properties of ARMA (1,1) Models 79 -- 2.6.2 General ARMA Models 80 -- 2.6.3 Identifying ARMA Models 81 -- 2.6.4 Forecasting Using an ARMA Model 84 -- 2.6.5 Three Model Representations for an ARMA Model 84 -- 2.7 Unit-Root Nonstationarity 86 -- 2.7.1 Random Walk 86 -- 2.7.2 Random Walk with Drift 88 -- 2.7.3 Trend-Stationary Time Series 90 -- 2.7.4 General Unit-Root Nonstationary Models 91 -- 2.7.5 Unit-Root Test 91 -- 2.8 Exponential Smoothing 96 -- 2.9 Seasonal Models 98 -- 2.9.1 Seasonal Differencing 99 -- 2.9.2 Multiplicative Seasonal Models 101 -- 2.9.3 Seasonal Dummy Variable 107 -- 2.10 Regression Models with Time Series Errors 110 -- 2.11 Long-Memory Models 117 -- 2.12 Model Comparison and Averaging 120 -- 2.12.1 In-sample Comparison 120 -- 2.12.2 Out-of-sample Comparison 121 -- 2.12.3 Model Averaging 125 -- Exercises 125 -- References 127 -- 3 Case Studies of Linear Time Series 128 -- 3.1 Weekly Regular Gasoline Price 129 -- 3.1.1 Pure Time Series Model 130 -- 3.1.2 Use of Crude Oil Prices 133 -- 3.1.3 Use of Lagged Crude Oil Prices 134 -- 3.1.4 Out-of-Sample Predictions 135 -- 3.2 Global Temperature Anomalies 140 -- 3.2.1 Unit-Root Stationarity 141 -- 3.2.2 Trend-Nonstationarity 145 -- 3.2.3 Model Comparison 148 -- 3.2.4 Long-Term Prediction 150 -- 3.2.5 Discussion 153 -- 3.3 US Monthly Unemployment Rates 157 -- 3.3.1 Univariate Time Series Models 157 -- 3.3.2 An Alternative Model 161 -- 3.3.3 Model Comparison 165 -- 3.3.4 Use of Initial Jobless Claims 165 -- 3.3.5 Comparison 173 -- Exercises 174 -- References 175 -- 4 Asset Volatility and Volatility Models 176 -- 4.1 Characteristics of Volatility 177 -- 4.2 Structure of a Model 178 -- 4.3 Model Building 181 -- 4.4 Testing for ARCH Effect 182 -- 4.5 The ARCH Model 185 -- 4.5.1 Properties of ARCH Models 186 -- 4.5.2 Advantages and Weaknesses of ARCH Models 187 -- 4.5.3 Building an ARCH Model 188 -- 4.5.4 Some Examples 193 -- 4.6 The GARCH Model 199 -- 4.6.1 An Illustrative Example 201 -- 4.6.2 Forecasting Evaluation 210 -- 4.6.3 A Two-Pass Estimation Method 210 -- 4.7 The Integrated GARCH Model 211 -- 4.8 The GARCH-M Model 213 -- 4.9 The Exponential Garch Model 215 -- 4.9.1 An Illustrative Example 217 -- 4.9.2 An Alternative Model Form 218 -- 4.9.3 Second Example 218 -- 4.9.4 Forecasting Using an EGARCH Model 220 -- 4.10 The Threshold Garch Model 222 -- 4.11 Asymmetric Power ARCH Models 224 -- 4.12 Nonsymmetric GARCH Model 226 -- 4.13 The Stochastic Volatility Model 228 -- 4.14 Long-Memory Stochastic Volatility Models 230 -- 4.15 Alternative Approaches 232 -- 4.15.1 Use of High Frequency Data 232 -- 4.15.2 Use of Daily Open, High, Low, and Close Prices 235 -- Exercises 239 -- References 241 -- 5 Applications of Volatility Models 243 -- 5.1 Garch Volatility Term Structure 244 -- 5.1.1 Term Structure 246 -- 5.2 Option Pricing and Hedging 248 -- 5.3 Time-Varying Correlations and Betas 251 -- 5.3.1 Time-Varying Betas 256 -- 5.4 Minimum Variance Portfolios 259 -- 5.5 Prediction 263 -- Exercises 271 -- References 272 -- 6 High Frequency Financial Data 274 -- 6.1 Nonsynchronous Trading 275 -- 6.2 Bid-Ask Spread of Trading Prices 279 -- 6.3 Empirical Characteristics of Trading Data 282 -- 6.4 Models for Price Changes 285 -- 6.4.1 Ordered Probit Model 288 -- 6.4.2 A Decomposition Model 293 -- 6.5 Duration Models 298 -- 6.5.1 Diurnal Component 299 -- 6.5.2 The ACD Model 301 -- 6.5.3 Estimation 303 -- 6.6 Realized Volatility 308 -- 6.6.1 Handling Microstructure Noises 313 -- 6.6.2 Discussion 317 -- Appendix A Some Probability Distributions 320 -- Appendix B Hazard Function 323 -- Exercises 324 -- References 325 -- 7 Value at Risk 327 -- 7.1 Risk Measure and Coherence 328 -- 7.1.1 Value at Risk (VaR) 329 -- 7.1.2 Expected Shortfall 334 -- 7.2 Remarks on Calculating Risk Measures 336 -- 7.3 Riskmetrics 337 -- 7.3.1 Discussion 342 -- 7.3.2 Multiple Positions 343 -- 7.4 An Econometric Approach 345 -- 7.4.1 Multiple Periods 348 -- 7.5 Quantile Estimation 352 -- 7.5.1 Quantile and Order Statistics 353 -- 7.5.2 Quantile Regression 354 -- 7.6 Extreme Value Theory 358 -- 7.6.1 Review of Extreme Value Theory 358 -- 7.6.2 Empirical Estimation 361 -- 7.6.3 Application to Stock Returns 363 -- 7.7 An Extreme Value Approach to Var 368 -- 7.7.1 Discussion 370 -- 7.7.2 Multiperiod VaR 371 -- 7.7.3 Return Level 371 -- 7.8 Peaks Over Thresholds 372 -- 7.8.1 Statistical Theory 373 -- 7.8.2 Mean Excess Function 374 -- 7.8.3 Estimation 376 -- 7.8.4 An Alternative Parameterization 378 -- 7.9 The Stationary Loss Processes 381 -- Exercises 383 -- References 384. |
| Series Title: | Wiley series in probability and statistics. |
| Responsibility: | Ruey S. Tsay. |
Abstract:
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.
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Publisher Synopsis
I found this book highly informative and interesting to read. The proper mix of theory and hands-on programming examples makes it recommended reading for both R programmers interested in finance and financial analysts with a basic programming background. Well written and following a clear and defined logical layout, the author has written a current reference text on using a powerful open-source programming language for typical financial analysis. (Computing Reviews, 25 March 2014) All in all, this book is a good and useful introduction to financial time series with many real-world examples. It is suitable for use both as a textbook and for self-study, with exercises provided at the end of each chapter. (International Statistical Review, 14 June 2013) Read more...
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