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Ruey S. Tsay
An Introduction To Analysis of Financial Data With R的数据与R程序代码
含原书目录、第一章、索引、勘误以及第一至第七章的数据、R程序代码、部分习题代码
  
An Introduction To Analysis of Financial Data With R的数据与R程序代码
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請問有內容嗎?

没有完整的电子书，仅含含原书目录、第一章、索引、勘误
以及第一至第七章的数据、R程序代码、部分习题代码（原书共7章）

目录
Preface  xiii
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 Stationary  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
Index  387