An introduction to analysis of financial data with R / / Ruey S. Tsay

• Record Nr.: UNINA9910463991503321
• Autore: Tsay Ruey S. <1951->
• Pubbl/distr/stampa: Hoboken, New Jersey : , : Wiley, , 2013; ©2013
• ISBN: 1-119-01346-1; 1-119-01345-3
• Descrizione fisica: XIV, 400 s
• Collana: Wiley Series in Probability and Statistics
• Disciplina: 332.0285/133
• Soggetti: Finance - Econometric models; Time-series analysis; Econometrics; R (Computer program language); Electronic books.
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Includes bibliographical references and index.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 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 ; 4.6 the Garch Model ; 4.6.1 An Illustrative Example ; 4.6.2 Forecasting Evaluation; 4.6.3 A Two-pass Estimation Method
• Sommario/riassunto: 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. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-world empirical research. The author supplies a hands-on introduction to the analysis of financial data using the freely available R software package and case