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An introduction to analysis of financial data with R / / Ruey S. Tsay
| An introduction to analysis of financial data with R / / Ruey S. Tsay |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, c2013 |
| Descrizione fisica | 1 online resource (416 pages) : illustrations, graphs |
| Disciplina | 332.0285/133 |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Finance -- Econometric models
Time-series analysis Econometrics R (Computer program language) |
| ISBN |
1-119-01346-1
1-119-01345-3 9781119013457 |
| Classificazione |
331.19
332.0285/133 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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 -- 4.7 The Integrated Garch Model -- 4.8 The Garch-M Model -- 4.9 The Exponential Garch Model -- 4.9.1 An Illustrative Example -- 4.9.2 An Alternative Model Form -- 4.9.3 Second Example -- 4.9.4 Forecasting Using an EGARCH Model -- 4.10 The Threshold Garch Model -- 4.11 Asymmetric Power Arch Models -- 4.12 Nonsymmetric Garch Model -- 4.13 The Stochastic Volatility Model -- 4.14 Long-memory Stochastic Volatility Models -- 4.15 Alternative Approaches -- 4.15.1 Use of High Frequency Data -- 4.15.2 Use of Daily Open, High, Low, and Close Prices -- Exercises -- References -- 5: Applications of Volatility Models -- 5.1 Garch Volatility Term Structure. 5.1.1 Term Structure -- 5.2 Option Pricing and Hedging -- 5.3 Time-varying Correlations and Betas -- 5.3.1 Time-varying Betas -- 5.4 Minimum Variance Portfolios -- 5.5 Prediction -- Exercises -- References -- 6: High Frequency Financial Data -- 6.1 Nonsynchronous Trading -- 6.2 Bid-ask Spread of Trading Prices -- 6.3 Empirical Characteristics of Trading Data -- 6.4 Models for Price Changes -- 6.4.1 Ordered Probit Model -- 6.4.2 a Decomposition Model -- 6.5 Duration Models -- 6.5.1 Diurnal Component -- 6.5.2 The ACD Model -- 6.5.3 Estimation -- 6.6 Realized Volatility -- 6.6.1 Handling Microstructure Noises -- 6.6.2 Discussion -- Appendix A: Some Probability Distributions -- Appendix B: Hazard Function -- Exercises -- References -- 7: Value at Risk -- 7.1 Risk Measure and Coherence -- 7.1.1 Value at Risk (VaR) -- 7.1.2 Expected Shortfall -- 7.2 Remarks on Calculating Risk Measures -- 7.3 Riskmetrics -- 7.3.1 Discussion -- 7.3.2 Multiple Positions -- 7.4 an Econometric Approach -- 7.4.1 Multiple Periods -- 7.5 Quantile Estimation -- 7.5.1 Quantile and Order Statistics -- 7.5.2 Quantile Regression -- 7.6 Extreme Value Theory -- 7.6.1 Review of Extreme Value Theory -- 7.6.2 Empirical Estimation -- 7.6.3 Application to Stock Returns -- 7.7 an Extreme Value Approach to Var -- 7.7.1 Discussion -- 7.7.2 Multiperiod Var -- 7.7.3 Return Level -- 7.8 Peaks over Thresholds -- 7.8.1 Statistical Theory -- 7.8.2 Mean Excess Function -- 7.8.3 Estimation -- 7.8.4 An Alternative Parameterization -- 7.9 The Stationary Loss Processes -- Exercises -- References -- Index. |
| Record Nr. | UNINA-9911089747203321 |
| Hoboken, N.J., : Wiley, c2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
An introduction to analysis of financial data with R / / Ruey S. Tsay
| An introduction to analysis of financial data with R / / Ruey S. Tsay |
| Autore | Tsay Ruey S. <1951-> |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2013 |
| Descrizione fisica | XIV, 400 s |
| Disciplina | 332.0285/133 |
| Collana | Wiley Series in Probability and Statistics |
| Soggetto topico |
Finance - Econometric models
Time-series analysis Econometrics R (Computer program language) |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-01346-1
1-119-01345-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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 |
| Record Nr. | UNINA-9910463991503321 |
Tsay Ruey S. <1951->
|
||
| Hoboken, New Jersey : , : Wiley, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
An introduction to analysis of financial data with R / / Ruey S. Tsay
| An introduction to analysis of financial data with R / / Ruey S. Tsay |
| Autore | Tsay Ruey S. <1951-> |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2013 |
| Descrizione fisica | 1 online resource (416 pages) : illustrations, graphs |
| Disciplina | 332.0285/133 |
| Collana | Wiley Series in Probability and Statistics |
| Soggetto topico |
Econometrics
Finance - Econometric models R (Computer program language) Time-series analysis |
| ISBN |
1-119-01346-1
1-119-01345-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910796088503321 |
|
Tsay Ruey S. <1951->
|
||
| Hoboken, New Jersey : , : Wiley, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
An introduction to analysis of financial data with R / / Ruey S. Tsay
| An introduction to analysis of financial data with R / / Ruey S. Tsay |
| Autore | Tsay Ruey S. <1951-> |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2013 |
| Descrizione fisica | 1 online resource (416 pages) : illustrations, graphs |
| Disciplina | 332.0285/133 |
| Collana | Wiley Series in Probability and Statistics |
| Soggetto topico |
Econometrics
Finance - Econometric models R (Computer program language) Time-series analysis |
| ISBN |
1-119-01346-1
1-119-01345-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910813796903321 |
|
Tsay Ruey S. <1951->
|
||
| Hoboken, New Jersey : , : Wiley, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
An introduction to analysis of financial data with R / / Ruey S. Tsay
| An introduction to analysis of financial data with R / / Ruey S. Tsay |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, c2013 |
| Descrizione fisica | 1 online resource (416 pages) : illustrations, graphs |
| Disciplina | 332.0285/133 |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Finance -- Econometric models
Time-series analysis Econometrics R (Computer program language) |
| ISBN |
1-119-01346-1
1-119-01345-3 9781119013457 |
| Classificazione |
331.19
332.0285/133 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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 -- 4.7 The Integrated Garch Model -- 4.8 The Garch-M Model -- 4.9 The Exponential Garch Model -- 4.9.1 An Illustrative Example -- 4.9.2 An Alternative Model Form -- 4.9.3 Second Example -- 4.9.4 Forecasting Using an EGARCH Model -- 4.10 The Threshold Garch Model -- 4.11 Asymmetric Power Arch Models -- 4.12 Nonsymmetric Garch Model -- 4.13 The Stochastic Volatility Model -- 4.14 Long-memory Stochastic Volatility Models -- 4.15 Alternative Approaches -- 4.15.1 Use of High Frequency Data -- 4.15.2 Use of Daily Open, High, Low, and Close Prices -- Exercises -- References -- 5: Applications of Volatility Models -- 5.1 Garch Volatility Term Structure. 5.1.1 Term Structure -- 5.2 Option Pricing and Hedging -- 5.3 Time-varying Correlations and Betas -- 5.3.1 Time-varying Betas -- 5.4 Minimum Variance Portfolios -- 5.5 Prediction -- Exercises -- References -- 6: High Frequency Financial Data -- 6.1 Nonsynchronous Trading -- 6.2 Bid-ask Spread of Trading Prices -- 6.3 Empirical Characteristics of Trading Data -- 6.4 Models for Price Changes -- 6.4.1 Ordered Probit Model -- 6.4.2 a Decomposition Model -- 6.5 Duration Models -- 6.5.1 Diurnal Component -- 6.5.2 The ACD Model -- 6.5.3 Estimation -- 6.6 Realized Volatility -- 6.6.1 Handling Microstructure Noises -- 6.6.2 Discussion -- Appendix A: Some Probability Distributions -- Appendix B: Hazard Function -- Exercises -- References -- 7: Value at Risk -- 7.1 Risk Measure and Coherence -- 7.1.1 Value at Risk (VaR) -- 7.1.2 Expected Shortfall -- 7.2 Remarks on Calculating Risk Measures -- 7.3 Riskmetrics -- 7.3.1 Discussion -- 7.3.2 Multiple Positions -- 7.4 an Econometric Approach -- 7.4.1 Multiple Periods -- 7.5 Quantile Estimation -- 7.5.1 Quantile and Order Statistics -- 7.5.2 Quantile Regression -- 7.6 Extreme Value Theory -- 7.6.1 Review of Extreme Value Theory -- 7.6.2 Empirical Estimation -- 7.6.3 Application to Stock Returns -- 7.7 an Extreme Value Approach to Var -- 7.7.1 Discussion -- 7.7.2 Multiperiod Var -- 7.7.3 Return Level -- 7.8 Peaks over Thresholds -- 7.8.1 Statistical Theory -- 7.8.2 Mean Excess Function -- 7.8.3 Estimation -- 7.8.4 An Alternative Parameterization -- 7.9 The Stationary Loss Processes -- Exercises -- References -- Index. |
| Record Nr. | UNINA-9911133978103321 |
| Hoboken, N.J., : Wiley, c2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||