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<P>书名：Modelling Financial Time Series with S-PLUS</P>
<P>作者：Eric Zivot &amp; Jiahui Wang</P>
<P>Contents:</P>
<P>1.S and S-PLUS</P>
<P>1.1Intoduction</P>
<P>1.2S Objects</P>
<P>1.3Modeling Functions in S+FinMetrics</P>
<P>1.4S-PLUS Resources</P>
<P>2.Time Series Specification, Manipulation and Visualization in S-PLUS</P>
<P>2.1Introduction</P>
<P>2.2The Specification of "Time Series" Objects in S-PLUS </P>
<P>2.3Time Series Manipulation in S-PLUS</P>
<P>2.4Visualization Time Series in S-PLUS</P>
<P>3.Time Series Concepts</P>
<P>3.1Introduction</P>
<P>3.2Univariate Time Series</P>
<P>3.3Univariate Nonstationary Time Series</P>
<P>3.4Long Memory Time Series</P>
<P>3.5Multivariate Time Series</P>
<P>4.Unit Root Test</P>
<P>4.1Introduction</P>
<P>4.2Testing for Nonstationarity and Stationarity</P>
<P>4.3Autoregressive Unit Root Tests</P>
<P>4.4Stationarity Tests</P>
<P>5.Modeling Extreme Values</P>
<P>5.1Introduction</P>
<P>5.2Modeling Maxima and Worst Cases</P>
<P>5.3Modeling Extreme Over High Thresholds</P>
<P>5.4Hill's Non-parametric Estimator of Tail Index</P>
<P>6.Time Series Regression Modeling</P>
<P>6.1Introduction</P>
<P>6.2Time Series Regression Model</P>
<P>6.3Time Series Regression Using the S+FinMetrics Function OLS</P>
<P>6.4Dynamic Regression</P>
<P>6.5Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimate</P>
<P>6.6Recursive Least Squares Estimation</P>
<P>7.Univariate GARCH Modeling</P>
<P>7.1Introduction </P>
<P>7.2The Basic ARCH Model</P>
<P>7.3The GARCH Model and Its Properties</P>
<P>7.4GARCH Modeling Using S+FinMetrics</P>
<P>7.5GARCH Model Extensions</P>
<P>7.6GARCH Model Selection and Comparison</P>
<P>7.7 GARCH Model Prediction</P>
<P>7.8GARCH Model Simulation</P>
<P>8.Long Memory Time Series Modeling</P>
<P>8.1Introduction</P>
<P>8.2Long Memory Time Series </P>
<P>8.3Statistical Tests for Long Memory </P>
<P>8.4Estimation of Long Memory Parameter</P>
<P>8.5Estimation of FARIMA and SEMIFA Models</P>
<P>8.6Long Memory GARCH Models</P>
<P>8.7Prediction from Long Memory Models</P>
<P>9.Rolling Analysis of Time Series</P>
<P>9.1Introduction</P>
<P>9.2Rolling Descriptive Statistics</P>
<P>9.3Technical Analysis Indicator</P>
<P>9.4Rollling Regression</P>
<P>10.Systems of Regression Equations</P>
<P>10.1Introduction</P>
<P>10.2Systems of Regression Equations</P>
<P>10.3Linear Seemingly Unrelated Regressions</P>
<P>10.4Nonlinear Seemingly Unrelated Regression Models</P>
<P>11.Vector Autoregressive Models for Multivariate Time Series </P>
<P>11.1 Introduction</P>
<P>11.2The Stationary Vector Autoregressive Models</P>
<P>11.3Forecasting</P>
<P>11.4Structure Analysis</P>
<P>11.5An Extended Example</P>
<P>11.6Bayesian Vector Autoregression</P>
<P>12.Cointegration</P>
<P>12.1Introduction</P>
<P>12.2Spurious Regression and Cointegration</P>
<P>12.3Residual-Based Tests for Cointegration</P>
<P>12.4Regression-Based Estimates of Cointegrating Vectors and Error Correction Models</P>
<P>12.5VAR Models and Cointegration</P>
<P>13.Multivariate GARCH Modeling</P>
<P>13.1Introduction</P>
<P>13.2Expotentially Weirhted Covariance Estimate</P>
<P>13.3Diagnol VEC Model</P>
<P>13.4Multivariate GARCH Modeling in FinMetrics</P>
<P>13.5Multivariate GARCH Model Extensions</P>
<P>13.6Multivariate GARCH Prediction</P>
<P>13.7Custom Estimation of GARCH Model</P>
<P>13.8Multivariate GARCH Model Simulation</P>
<P>14.State Space Models</P>
<P>14.1Introduction</P>
<P>14.2State Space Representation</P>
<P>14.3Algorithms</P>
<P>14.4Estimation of State Space Models</P>
<P>14.5Simulation Smoothing</P>
<P>15.Factor Models for Asset Returns</P>
<P>15.1Introduction</P>
<P>15.2Factor Models Specification</P>
<P>15.3Macroeconomic Factor Models for Returns</P>
<P>15.4Fundalmental Factor Models </P>
<P>15.5Statistical Factor Models for Returns</P>
<P>16.Term Structure of Interest Rates</P>
<P>16.1Introduction</P>
<P>16.2Discount, Spot and Forward Rates</P>
<P>16.3Qudratic and Cubic Spline Interpolation</P>
<P>16.4Smoothing Spline Interpolation</P>
<P>16.5Nelson-Siegel Function</P>
<P>17.Robust Change Detection</P>
<P>17.1Introduction</P>
<P>17.2REGARIMA Models</P>
<P>17.3Robust Fitting of REGARIMA Models</P>
<P>17.4Prediction Using REGARIMA Models</P>
<P>17.5Controling Robust Fitting of REGARIMA Models</P><br>[/Point][/UseMoney]<br>

[此贴子已经被作者于2006-2-16 0:55:13编辑过]