Actions for Dynamic models for volatility and heavy tails : with applications to financial and economic time series

Email RIS file
Bookmark
Report an Issue

Dynamic models for volatility and heavy tails : with applications to financial and economic time series / Andrew C. Harvey

Author
Harvey, A. C. (Andrew C.)
Published
Cambridge ; New York : Cambridge University Press, 2013.
Physical Description
1 online resource (xviii, 261 pages) : illustrations
Access Online

Availability

I Want It
I Want It

Finding items...

Series
Contents
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., 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., 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*, 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., 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., and Machine generated contents note: 1.1. Unobserved Components and Filters -- 1.2. Independence, White Noise and Martingale Differences -- 1.2.1. 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 -- 2.1. Distributions -- 2.1.1. 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 of Dynamic Conditional Score Models -- 2.3.1. Information Matrix Lemma -- 2.3.2. Information Matrix for the First-Order Model -- 2.3.3. Information Matrix with the δ Parameterization* -- 2.3.4. Asymptotic Distribution -- 2.3.5. Consistency and Asymptotic Normality* -- 2.3.6. Nonstationarity -- 2.3.7. Several Parameters -- 2.4. 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 -- 3.1. 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* -- 3.5. Higher Order Models and the State Space Form* -- 3.5.1. Linear Gaussian Models and the Kalman Filter -- 3.5.2. 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 -- 3.8.1. QARMA Models -- 3.8.2. 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 -- 4.1. Beta-t-EGARCH -- 4.2. Properties of Stationary Beta-t-EGARCH Models -- 4.2.1. Exponential GARCH -- 4.2.2. Moments -- 4.2.3. Autocorrelation Functions of Squares and Powers of 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 -- 4.5.4. Predictive Distribution -- 4.6. Maximum Likelihood Estimation and Inference -- 4.6.1. Asymptotic Theory for Beta-t-EGARCH -- 4.6.2. Monte Carlo Experiments -- 4.6.3. Gamma-GED-EGARCH -- 4.6.4. Leverage -- 4.7. Beta-t-GARCH -- 4.7.1. Properties of First-Order Model -- 4.7.2. Leverage Effects -- 4.7.3. Link with Beta-t-EGARCH -- 4.7.4. Estimation and Inference -- 4.7.5. Gamma-GED-GARCH -- 4.8. Smoothing -- 4.9. Application to Hang Seng and Dow Jones -- 4.10. Two Component Models -- 4.11. Trends, Seasonals and Explanatory Variables in Volatility Equations -- 4.12. Changing Location -- 4.12.1. Explanatory Variables -- 4.12.2. Stochastic Location and Stochastic Scale -- 4.13. Testing for Changing Volatility and Leverage -- 4.13.1. Portmanteau Test for Changing Volatility -- 4.13.2. Martingale Difference Test -- 4.13.3. Leverage -- 4.13.4. Diagnostics -- 4.14. Skew Distributions -- 4.15. Time-Varying Skewness and Kurtosis* -- 5.1. General Properties -- 5.1.1. Heavy Tails -- 5.1.2. Moments and Autocorrelations -- 5.1.3. Forecasts -- 5.1.4. Asymptotic Distribution of Maximum Likelihood Estimators -- 5.2. Generalized Gamma Distribution -- 5.2.1. Moments -- 5.2.2. Forecasts -- 5.2.3. Maximum Likelihood Estimation -- 5.3. Generalized Beta Distribution -- 5.3.1. Log-Logistic Distribution -- 5.3.2. Moments, Autocorrelations and Forecasts -- 5.3.3. Maximum Likelihood Estimation -- 5.3.4. Burr Distribution -- 5.3.5. Generalized Pareto Distribution -- 5.3.6. F Distribution -- 5.4. Log-Normal Distribution -- 5.5. Monte Carlo Experiments -- 5.6. Leverage, Long Memory and Diurnal Variation -- 5.7. Tests and Model Selection -- 5.8. Estimating Volatility from the Range -- 5.8.1. Application to Paris CAC and Dow Jones -- 5.8.2. Range-EGARCH Model -- 5.9. Duration -- 5.10. Realized Volatility -- 5.11. Count Data and Qualitative Observations -- 6.1. Kernel Density Estimation for Time Series -- 6.1.1. Filtering and Smoothing -- 6.1.2. Estimation -- 6.1.3. Correcting for Changing Mean and Variance -- 6.1.4. Specification and Diagnostic Checking -- 6.2. Time-Varying Quantiles -- 6.2.1. Kernel-Based Estimation -- 6.2.2. Direct Estimation of Individual Quantiles -- 6.3. Forecasts -- 6.4. Application to NASDAQ Returns -- 6.4.1. Direct Modelling of Returns -- 6.4.2. ARMA-GARCH Residuals -- 6.4.3. Bandwidth and Tails -- 7.1. Multivariate Distributions -- 7.1.1. Estimation -- 7.1.2. Regression -- 7.1.3. Dynamic Models -- 7.2. Multivariate Location Models -- 7.2.1. Structural Time Series Models -- 7.2.2. DCS Model for the Multivariate t -- 7.2.3. Asymptotic Theory* -- 7.2.4. Regression and Errors in Variables -- 7.3. Dynamic Correlation -- 7.3.1. Bivariate Gaussian Model -- 7.3.2. Time-Varying Parameters in Regression -- 7.3.3. Multivariate t Distribution -- 7.3.4. Tests of Changing Correlation -- 7.4. Dynamic Multivariate Scale -- 7.5. Dynamic Scale and Association -- 7.6. Copulas -- 7.6.1. Copulas and Quantiles -- 7.6.2. Measures of Association -- 7.6.3. Maximum Likelihood Estimation -- 7.6.4. Dynamic Copulas -- 7.6.5. Tests Against Changing Association -- A.1. Unconditional Mean Parameterization -- A.2. Paramerization with δ -- A.3. Leverage -- B.1. Beta-t-EGARCH -- B.2. Gamma-GED-EGARCH -- B.3. Beta-t-GARCH.
Summary
Presents a statistical theory for a class of nonlinear time-series models. The overall approach will be of interest to econometricians and statisticians.
Subject(s)
ISBN
9781107336889 (electronic bk.)
1107336880 (electronic bk.)
1139540939 (electronic bk.)
9781139540933 (electronic bk.)
9781107335226
1107335221
9781107034723
1107034728
9781107630024
1107630029
9781107333567 (electronic bk.)
1107333563 (electronic bk.)
Digital File Characteristics
data file
Bibliography Note
Includes bibliographical references (pages 247-254) and indexes.
View MARC record | catkey: 43625310