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Dynamic models for volatility and heavy tails : with applications to financial and economic time series /
Presents a statistical theory for a class of nonlinear time-series models. The overall approach will be of interest to econometricians and statisticians.
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cambridge ; New York :
Cambridge University Press,
2013.
|
| Colección: | Econometric Society monographs ;
no. 52. |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Harvey, A. C. |q (Andrew C.) | |
| 245 | 1 | 0 | |a Dynamic models for volatility and heavy tails : |b with applications to financial and economic time series / |c Andrew C. Harvey. |
| 264 | 1 | |a Cambridge ; |a New York : |b Cambridge University Press, |c 2013. | |
| 300 | |a 1 online resource (xviii, 261 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a data file | ||
| 490 | 1 | |a Econometric society monographs ; |v 52 | |
| 504 | |a Includes bibliographical references (pages 247-254) and indexes. | ||
| 588 | 0 | |a Print version record. | |
| 520 | |a Presents a statistical theory for a class of nonlinear time-series models. The overall approach will be of interest to econometricians and statisticians. | ||
| 505 | 0 | |6 880-01 |a Preface; Acronyms and Abbreviations; 1 Introduction; 1.1 Unobserved Components and Filters; 1.2 Independence, White Noise and Martingale Differences; 1.2.1 The Law of Iterated Expectations and Optimal Predictions; 1.2.2 Definitions and Properties; 1.3 Volatility; 1.3.1 Stochastic Volatility; 1.3.2 Generalized Autoregressive Conditional Heteroscedasticity; 1.3.3 Exponential GARCH; 1.3.4 Variance, Scale and Outliers; 1.3.5 Location/Scale Models; 1.4 Dynamic Conditional Score Models; 1.5 Distributions and Quantiles; 1.6 Plan of Book; 2 Statistical Distributions and Asymptotic Theory. | |
| 505 | 8 | |a 2.1 Distributions2.1.1 Student's t Distribution; 2.1.2 General Error Distribution; 2.1.3 Beta Distribution; 2.1.4 Gamma Distribution; 2.2 Maximum Likelihood; 2.2.1 Student's t Distribution; 2.2.2 General Error Distribution; 2.2.3 Gamma Distribution; 2.2.4 Consistency and Asymptotic Normality*; 2.3 Maximum Likelihood Estimation; 2.3.1 An Information Matrix Lemma; 2.3.2 Information Matrix for the First-Order Model; 2.3.3 Information Matrix with the 0=x""010E Parameterization*; 2.3.4 Asymptotic Distribution; 2.3.5 Consistency and Asymptotic Normality*; 2.3.6 Nonstationarity. | |
| 505 | 8 | |a 2.3.7 Several Parameters2.4 Higher Order Models; 2.5 Tests; 2.5.1 Serial Correlation; 2.5.2 Goodness of Fit of Distributions; 2.5.3 Residuals; 2.5.4 Model Fit; 2.6 Explanatory Variables; 3 Location; 3.1 Dynamic Student's t Location Model; 3.2 Basic Properties; 3.2.1 Generalization and Reduced Form; 3.2.2 Moments of the Observations; 3.2.3 Autocorrelation Function; 3.3 Maximum Likelihood Estimation; 3.3.1 Asymptotic Distribution of the Maximum Likelihood Estimator; 3.3.2 Monte Carlo Experiments; 3.3.3 Application to U.S. GDP; 3.4 Parameter Restrictions* | |
| 505 | 8 | |a 3.5 Higher Order Models and the State Space Form*3.5.1 Linear Gaussian Models and the Kalman Filter; 3.5.2 The DCS Model; 3.5.3 QARMA Models; 3.6 Trend and Seasonality; 3.6.1 Local Level Model; 3.6.2 Application to Weekly Hours of Employees in U.S. Manufacturing; 3.6.3 Local Linear Trend; 3.6.4 Stochastic Seasonal; 3.6.5 Application to Rail Travel; 3.6.6 QARIMA and Seasonal QARIMA Models*; 3.7 Smoothing; 3.7.1 Weights; 3.7.2 Smoothing Recursions for Linear State Space Models; 3.7.3 Smoothing Recursions for DCS Models; 3.7.4 Conditional Mode Estimation and the Score; 3.8 Forecasting. | |
| 505 | 8 | |a 3.8.1 QARMA Models3.8.2 State Space Form*; 3.9 Components and Long Memory; 3.10 General Error Distribution; 3.11 Skew Distributions; 3.11.1 How to Skew a Distribution; 3.11.2 Dynamic Skew-t Location Model; 4 Scale; 4.1 Beta-tttt-EGARCH; 4.2 Properties of Stationary Beta-tttt-EGARCH Models; 4.2.1 Exponential GARCH; 4.2.2 Moments; 4.2.3 Autocorrelation Functions of Squares and Powersof Absolute Values; 4.2.4 Autocorrelations and Kurtosis; 4.3 Leverage Effects; 4.4 Gamma-GED-EGARCH; 4.5 Forecasting; 4.5.1 Beta-t-EGARCH; 4.5.2 Gamma-GED-EGARCH; 4.5.3 Integrated Exponential Models. | |
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Econometrics. | |
| 650 | 0 | |a Finance |x Mathematical models. | |
| 650 | 0 | |a Time-series analysis. | |
| 650 | 6 | |a Économétrie. | |
| 650 | 6 | |a Finances |x Modèles mathématiques. | |
| 650 | 6 | |a Série chronologique. | |
| 650 | 7 | |a BUSINESS & ECONOMICS |x Economics |x General. |2 bisacsh | |
| 650 | 7 | |a BUSINESS & ECONOMICS |x Reference. |2 bisacsh | |
| 650 | 7 | |a Econometrics. |2 fast |0 (OCoLC)fst00901574 | |
| 650 | 7 | |a Finance |x Mathematical models. |2 fast |0 (OCoLC)fst00924398 | |
| 650 | 7 | |a Time-series analysis. |2 fast |0 (OCoLC)fst01151190 | |
| 650 | 7 | |a Nichtlineare Zeitreihenanalyse |2 gnd | |
| 650 | 7 | |a Wahrscheinlichkeitsverteilung |2 gnd | |
| 650 | 7 | |a Dynamisches Modell |2 gnd | |
| 776 | 0 | 8 | |i Print version: |a Harvey, A.C. (Andrew C.). |t Dynamic models for volatility and heavy tails |z 9781107034723 |w (DLC) 2012036508 |w (OCoLC)811777444 |
| 830 | 0 | |a Econometric Society monographs ; |v no. 52. | |
| 856 | 4 | 0 | |u https://ebsco.uam.elogim.com/login.aspx?direct=true&scope=site&db=nlebk&AN=533825 |z Texto completo |
| 880 | 0 | 0 | |6 505-01/(S |g Machine generated contents note: |g 1.1. |t Unobserved Components and Filters -- |g 1.2. |t Independence, White Noise and Martingale Differences -- |g 1.2.1. |t Law of Iterated Expectations and Optimal Predictions -- |g 1.2.2. |t Definitions and Properties -- |g 1.3. |t Volatility -- |g 1.3.1. |t Stochastic Volatility -- |g 1.3.2. |t Generalized Autoregressive Conditional Heteroscedasticity -- |g 1.3.3. |t Exponential GARCH -- |g 1.3.4. |t Variance, Scale and Outliers -- |g 1.3.5. |t Location/Scale Models -- |g 1.4. |t Dynamic Conditional Score Models -- |g 1.5. |t Distributions and Quantiles -- |g 1.6. |t Plan of Book -- |g 2.1. |t Distributions -- |g 2.1.1. |t Student's t Distribution -- |g 2.1.2. |t General Error Distribution -- |g 2.1.3. |t Beta Distribution -- |g 2.1.4. |t Gamma Distribution -- |g 2.2. |t Maximum Likelihood -- |g 2.2.1. |t Student's t Distribution -- |g 2.2.2. |t General Error Distribution -- |g 2.2.3. |t Gamma Distribution -- |g 2.2.4. |t Consistency and Asymptotic Normality* -- |g 2.3. |t Maximum Likelihood Estimation of Dynamic Conditional Score Models -- |g 2.3.1. |t Information Matrix Lemma -- |g 2.3.2. |t Information Matrix for the First-Order Model -- |g 2.3.3. |t Information Matrix with the δ Parameterization* -- |g 2.3.4. |t Asymptotic Distribution -- |g 2.3.5. |t Consistency and Asymptotic Normality* -- |g 2.3.6. |t Nonstationarity -- |g 2.3.7. |t Several Parameters -- |g 2.4. |t Higher Order Models* -- |g 2.5. |t Tests -- |g 2.5.1. |t Serial Correlation -- |g 2.5.2. |t Goodness of Fit of Distributions -- |g 2.5.3. |t Residuals -- |g 2.5.4. |t Model Fit -- |g 2.6. |t Explanatory Variables -- |g 3.1. |t Dynamic Student's t Location Model -- |g 3.2. |t Basic Properties -- |g 3.2.1. |t Generalization and Reduced Form -- |g 3.2.2. |t Moments of the Observations -- |g 3.2.3. |t Autocorrelation Function -- |g 3.3. |t Maximum Likelihood Estimation -- |g 3.3.1. |t Asymptotic Distribution of the Maximum Likelihood Estimator -- |g 3.3.2. |t Monte Carlo Experiments -- |g 3.3.3. |t Application to U.S. GDP -- |g 3.4. |t Parameter Restrictions* -- |g 3.5. |t Higher Order Models and the State Space Form* -- |g 3.5.1. |t Linear Gaussian Models and the Kalman Filter -- |g 3.5.2. |t DCS Model -- |g 3.5.3. |t QARMA Models -- |g 3.6. |t Trend and Seasonality -- |g 3.6.1. |t Local Level Model -- |g 3.6.2. |t Application to Weekly Hours of Employees in U.S. Manufacturing -- |g 3.6.3. |t Local Linear Trend -- |g 3.6.4. |t Stochastic Seasonal -- |g 3.6.5. |t Application to Rail Travel -- |g 3.6.6. |t QARIMA and Seasonal QARIMA Models* -- |g 3.7. |t Smoothing -- |g 3.7.1. |t Weights -- |g 3.7.2. |t Smoothing Recursions for Linear State Space Models -- |g 3.7.3. |t Smoothing Recursions for DCS Models -- |g 3.7.4. |t Conditional Mode Estimation and the Score -- |g 3.8. |t Forecasting -- |g 3.8.1. |t QARMA Models -- |g 3.8.2. |t State Space Form* -- |g 3.9. |t Components and Long Memory -- |g 3.10. |t General Error Distribution -- |g 3.11. |t Skew Distributions -- |g 3.11.1. |t How to Skew a Distribution -- |g 3.11.2. |t Dynamic Skew-t Location Model -- |g 4.1. |t Beta-t-EGARCH -- |g 4.2. |t Properties of Stationary Beta-t-EGARCH Models -- |g 4.2.1. |t Exponential GARCH -- |g 4.2.2. |t Moments -- |g 4.2.3. |t Autocorrelation Functions of Squares and Powers of Absolute Values -- |g 4.2.4. |t Autocorrelations and Kurtosis -- |g 4.3. |t Leverage Effects -- |g 4.4. |t Gamma-GED-EGARCH -- |g 4.5. |t Forecasting -- |g 4.5.1. |t Beta-t-EGARCH -- |g 4.5.2. |t Gamma-GED-EGARCH -- |g 4.5.3. |t Integrated Exponential Models -- |g 4.5.4. |t Predictive Distribution -- |g 4.6. |t Maximum Likelihood Estimation and Inference -- |g 4.6.1. |t Asymptotic Theory for Beta-t-EGARCH -- |g 4.6.2. |t Monte Carlo Experiments -- |g 4.6.3. |t Gamma-GED-EGARCH -- |g 4.6.4. |t Leverage -- |g 4.7. |t Beta-t-GARCH -- |g 4.7.1. |t Properties of First-Order Model -- |g 4.7.2. |t Leverage Effects -- |g 4.7.3. |t Link with Beta-t-EGARCH -- |g 4.7.4. |t Estimation and Inference -- |g 4.7.5. |t Gamma-GED-GARCH -- |g 4.8. |t Smoothing -- |g 4.9. |t Application to Hang Seng and Dow Jones -- |g 4.10. |t Two Component Models -- |g 4.11. |t Trends, Seasonals and Explanatory Variables in Volatility Equations -- |g 4.12. |t Changing Location -- |g 4.12.1. |t Explanatory Variables -- |g 4.12.2. |t Stochastic Location and Stochastic Scale -- |g 4.13. |t Testing for Changing Volatility and Leverage -- |g 4.13.1. |t Portmanteau Test for Changing Volatility -- |g 4.13.2. |t Martingale Difference Test -- |g 4.13.3. |t Leverage -- |g 4.13.4. |t Diagnostics -- |g 4.14. |t Skew Distributions -- |g 4.15. |t Time-Varying Skewness and Kurtosis* -- |g 5.1. |t General Properties -- |g 5.1.1. |t Heavy Tails -- |g 5.1.2. |t Moments and Autocorrelations -- |g 5.1.3. |t Forecasts -- |g 5.1.4. |t Asymptotic Distribution of Maximum Likelihood Estimators -- |g 5.2. |t Generalized Gamma Distribution -- |g 5.2.1. |t Moments -- |g 5.2.2. |t Forecasts -- |g 5.2.3. |t Maximum Likelihood Estimation -- |g 5.3. |t Generalized Beta Distribution -- |g 5.3.1. |t Log-Logistic Distribution -- |g 5.3.2. |t Moments, Autocorrelations and Forecasts -- |g 5.3.3. |t Maximum Likelihood Estimation -- |g 5.3.4. |t Burr Distribution -- |g 5.3.5. |t Generalized Pareto Distribution -- |g 5.3.6. |t F Distribution -- |g 5.4. |t Log-Normal Distribution -- |g 5.5. |t Monte Carlo Experiments -- |g 5.6. |t Leverage, Long Memory and Diurnal Variation -- |g 5.7. |t Tests and Model Selection -- |g 5.8. |t Estimating Volatility from the Range -- |g 5.8.1. |t Application to Paris CAC and Dow Jones -- |g 5.8.2. |t Range-EGARCH Model -- |g 5.9. |t Duration -- |g 5.10. |t Realized Volatility -- |g 5.11. |t Count Data and Qualitative Observations -- |g 6.1. |t Kernel Density Estimation for Time Series -- |g 6.1.1. |t Filtering and Smoothing -- |g 6.1.2. |t Estimation -- |g 6.1.3. |t Correcting for Changing Mean and Variance -- |g 6.1.4. |t Specification and Diagnostic Checking -- |g 6.2. |t Time-Varying Quantiles -- |g 6.2.1. |t Kernel-Based Estimation -- |g 6.2.2. |t Direct Estimation of Individual Quantiles -- |g 6.3. |t Forecasts -- |g 6.4. |t Application to NASDAQ Returns -- |g 6.4.1. |t Direct Modelling of Returns -- |g 6.4.2. |t ARMA-GARCH Residuals -- |g 6.4.3. |t Bandwidth and Tails -- |g 7.1. |t Multivariate Distributions -- |g 7.1.1. |t Estimation -- |g 7.1.2. |t Regression -- |g 7.1.3. |t Dynamic Models -- |g 7.2. |t Multivariate Location Models -- |g 7.2.1. |t Structural Time Series Models -- |g 7.2.2. |t DCS Model for the Multivariate t -- |g 7.2.3. |t Asymptotic Theory* -- |g 7.2.4. |t Regression and Errors in Variables -- |g 7.3. |t Dynamic Correlation -- |g 7.3.1. |t Bivariate Gaussian Model -- |g 7.3.2. |t Time-Varying Parameters in Regression -- |g 7.3.3. |t Multivariate t Distribution -- |g 7.3.4. |t Tests of Changing Correlation -- |g 7.4. |t Dynamic Multivariate Scale -- |g 7.5. |t Dynamic Scale and Association -- |g 7.6. |t Copulas -- |g 7.6.1. |t Copulas and Quantiles -- |g 7.6.2. |t Measures of Association -- |g 7.6.3. |t Maximum Likelihood Estimation -- |g 7.6.4. |t Dynamic Copulas -- |g 7.6.5. |t Tests Against Changing Association -- |g A.1. |t Unconditional Mean Parameterization -- |g A.2. |t Paramerization with δ -- |g A.3. |t Leverage -- |g B.1. |t Beta-t-EGARCH -- |g B.2. |t Gamma-GED-EGARCH -- |g B.3. |t Beta-t-GARCH. |
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