An Introduction to Analysis of Financial Data with R

Detalles Bibliográficos
Autor principal: Tsay, Ruey S.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated, 2012.
Colección:New York Academy of Sciences Ser.
Temas:
Electronic books.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright
  • Contents
  • Preface
  • 1: Financial Data and Their Properties
  • 1.1 Asset Returns
  • 1.2 Bond Yields and Prices
  • 1.3 Implied Volatility
  • 1.4 R Packages and Demonstrations
  • 1.4.1 Installation of R Packages
  • 1.4.2 The Quantmod Package
  • 1.4.3 Some Basic R Commands
  • 1.5 Examples of Financial Data
  • 1.6 Distributional Properties of Returns
  • 1.6.1 Review of Statistical Distributions and Their Moments
  • 1.7 Visualization of Financial Data
  • 1.8 Some Statistical Distributions
  • 1.8.1 Normal Distribution
  • 1.8.2 Lognormal Distribution
  • 1.8.3 Stable Distribution
  • 1.8.4 Scale Mixture of Normal Distributions
  • 1.8.5 Multivariate Returns
  • Exercises
  • References
  • 2: Linear Models for Financial Time Series
  • 2.1 Stationarity
  • 2.2 Correlation and Autocorrelation Function
  • 2.3 White Noise and Linear Time Series
  • 2.4 Simple Autoregressive Models
  • 2.4.1 Properties of AR Models
  • 2.4.2 Identifying Ar Models in Practice
  • 2.4.3 Goodness of Fit
  • 2.4.4 Forecasting
  • 2.5 Simple Moving Average Models
  • 2.5.1 Properties of MA Models
  • 2.5.2 Identifying MA Order
  • 2.5.3 Estimation
  • 2.5.4 Forecasting Using MA Models
  • 2.6 Simple Arma Models
  • 2.6.1 Properties of ARMA(1,1) Models
  • 2.6.2 General ARMA Models
  • 2.6.3 Identifying ARMA Models
  • 2.6.4 Forecasting Using an ARMA Model
  • 2.6.5 Three Model Representations for an ARMA Model
  • 2.7 Unit-root Nonstationarity
  • 2.7.1 Random Walk
  • 2.7.2 Random Walk with Drift
  • 2.7.3 Trend-stationary Time Series
  • 2.7.4 General Unit-root Nonstationary Models
  • 2.7.5 Unit-root Test
  • 2.8 Exponential Smoothing
  • 2.9 Seasonal Models
  • 2.9.1 Seasonal Differencing
  • 2.9.2 Multiplicative Seasonal Models
  • 2.9.3 Seasonal Dummy Variable
  • 2.10 Regression Models with Time Series Errors
  • 2.11 Long-memory Models
  • 2.12 Model Comparison and Averaging
  • 2.12.1 In-sample Comparison
  • 2.12.2 Out-of-sample Comparison
  • 2.12.3 Model Averaging
  • Exercises
  • References
  • 3: Case Studies of Linear Time Series
  • 3.1 Weekly Regular Gasoline Price
  • 3.1.1 Pure Time Series Model
  • 3.1.2 Use of Crude Oil Prices
  • 3.1.3 Use of Lagged Crude Oil Prices
  • 3.1.4 Out-of-sample Predictions
  • 3.2 Global Temperature Anomalies
  • 3.2.1 Unit-root Stationarity
  • 3.2.2 Trend-nonstationarity
  • 3.2.3 Model Comparison
  • 3.2.4 Long-term Prediction
  • 3.2.5 Discussion
  • 3.3 Us Monthly Unemployment Rates
  • 3.3.1 Univariate Time Series Models
  • 3.3.2 An Alternative Model
  • 3.3.3 Model Comparison
  • 3.3.4 Use of Initial Jobless Claims
  • 3.3.5 Comparison
  • Exercises
  • References
  • 4: Asset Volatility and Volatility Models
  • 4.1 Characteristics of Volatility
  • 4.2 Structure of a Model
  • 4.3 Model Building
  • 4.4 Testing for ARCH Effect
  • 4.5 The Arch Model
  • 4.5.1 Properties of ARCH Models
  • 4.5.2 Advantages and Weaknesses of ARCH Models
  • 4.5.3 Building an ARCH Model
  • 4.5.4 Some Examples

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