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.
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge ; New York :
Cambridge University Press,
2013.
|
| Schriftenreihe: | Econometric Society monographs ;
no. 52. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Presents a statistical theory for a class of nonlinear time-series models. The overall approach will be of interest to econometricians and statisticians. |
| Beschreibung: | 1 online resource (xviii, 261 pages) : illustrations |
| Bibliographie: | Includes bibliographical references (pages 247-254) and indexes. |
| ISBN: | 9781107336889 1107336880 1139540939 9781139540933 9781107335226 1107335221 9781107333567 1107333563 |
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| 100 | 1 | |a Harvey, A. C. |q (Andrew C.) |1 https://id.oclc.org/worldcat/entity/E39PBJxxj79qDhWGrRQTFHdbBP |0 http://id.loc.gov/authorities/names/n81064640 | |
| 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. | |
| 650 | 0 | |a Econometrics. |0 http://id.loc.gov/authorities/subjects/sh85040763 | |
| 650 | 0 | |a Finance |x Mathematical models. |0 http://id.loc.gov/authorities/subjects/sh85048260 | |
| 650 | 0 | |a Time-series analysis. |0 http://id.loc.gov/authorities/subjects/sh85135430 | |
| 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 Finanzas |x Modelos matemáticos |2 embne | |
| 650 | 0 | 7 | |a Análisis de series temporales |2 embucm |
| 650 | 7 | |a Econometrics |2 fast | |
| 650 | 7 | |a Finance |x Mathematical models |2 fast | |
| 650 | 7 | |a Time-series analysis |2 fast | |
| 650 | 7 | |a Nichtlineare Zeitreihenanalyse |2 gnd |0 http://d-nb.info/gnd/4276267-4 | |
| 650 | 7 | |a Wahrscheinlichkeitsverteilung |2 gnd |0 http://d-nb.info/gnd/4121894-2 | |
| 650 | 7 | |a Dynamisches Modell |2 gnd |0 http://d-nb.info/gnd/4150932-8 | |
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| 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. |0 http://id.loc.gov/authorities/names/n84716218 | |
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| 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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Datensatz im Suchindex
| DE-BY-FWS_katkey | ZDB-4-EBA-ocn857489673 |
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| _version_ | 1816882243394600960 |
| adam_text | |
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| author | Harvey, A. C. (Andrew C.) |
| author_GND | http://id.loc.gov/authorities/names/n81064640 |
| author_facet | Harvey, A. C. (Andrew C.) |
| author_role | |
| author_sort | Harvey, A. C. |
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| callnumber-raw | HB139 .H369 2013eb |
| callnumber-search | HB139 .H369 2013eb |
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| callnumber-subject | HB - Economic Theory and Demography |
| collection | ZDB-4-EBA |
| 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. |
| ctrlnum | (OCoLC)857489673 |
| dewey-full | 330.01/5195 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 330 - Economics |
| dewey-raw | 330.01/5195 |
| dewey-search | 330.01/5195 |
| dewey-sort | 3330.01 45195 |
| dewey-tens | 330 - Economics |
| discipline | Wirtschaftswissenschaften |
| format | Electronic eBook |
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code="a">(OCoLC)857489673</subfield><subfield code="z">(OCoLC)843079230</subfield><subfield code="z">(OCoLC)862787028</subfield><subfield code="z">(OCoLC)1085072197</subfield><subfield code="z">(OCoLC)1087479284</subfield><subfield code="z">(OCoLC)1107776443</subfield><subfield code="z">(OCoLC)1109946317</subfield><subfield code="z">(OCoLC)1111154996</subfield><subfield code="z">(OCoLC)1117843689</subfield><subfield code="z">(OCoLC)1153005143</subfield><subfield code="z">(OCoLC)1170693991</subfield><subfield code="z">(OCoLC)1171233332</subfield><subfield code="z">(OCoLC)1228587581</subfield></datafield><datafield tag="050" ind1=" " ind2="4"><subfield code="a">HB139</subfield><subfield code="b">.H369 2013eb</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">BUS</subfield><subfield code="x">069000</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">BUS</subfield><subfield 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C.</subfield><subfield code="q">(Andrew C.)</subfield><subfield code="1">https://id.oclc.org/worldcat/entity/E39PBJxxj79qDhWGrRQTFHdbBP</subfield><subfield code="0">http://id.loc.gov/authorities/names/n81064640</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Dynamic models for volatility and heavy tails :</subfield><subfield code="b">with applications to financial and economic time series /</subfield><subfield code="c">Andrew C. Harvey.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Cambridge ;</subfield><subfield code="a">New York :</subfield><subfield code="b">Cambridge University Press,</subfield><subfield code="c">2013.</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource (xviii, 261 pages) :</subfield><subfield code="b">illustrations</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="347" ind1=" " ind2=" "><subfield code="a">data file</subfield></datafield><datafield tag="490" ind1="1" ind2=" "><subfield code="a">Econometric society monographs ;</subfield><subfield code="v">52</subfield></datafield><datafield tag="504" ind1=" " ind2=" "><subfield code="a">Includes bibliographical references (pages 247-254) and indexes.</subfield></datafield><datafield tag="588" ind1="0" ind2=" "><subfield code="a">Print version record.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Presents a statistical theory for a class of nonlinear time-series models. The overall approach will be of interest to econometricians and statisticians.</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="6">880-01</subfield><subfield code="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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="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*</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="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.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Econometrics.</subfield><subfield code="0">http://id.loc.gov/authorities/subjects/sh85040763</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Finance</subfield><subfield code="x">Mathematical models.</subfield><subfield code="0">http://id.loc.gov/authorities/subjects/sh85048260</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Time-series analysis.</subfield><subfield code="0">http://id.loc.gov/authorities/subjects/sh85135430</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Économétrie.</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Finances</subfield><subfield code="x">Modèles mathématiques.</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Série chronologique.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">BUSINESS & ECONOMICS</subfield><subfield code="x">Economics</subfield><subfield code="x">General.</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">BUSINESS & ECONOMICS</subfield><subfield code="x">Reference.</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Finanzas</subfield><subfield code="x">Modelos matemáticos</subfield><subfield code="2">embne</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Análisis de series temporales</subfield><subfield code="2">embucm</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Econometrics</subfield><subfield code="2">fast</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Finance</subfield><subfield code="x">Mathematical models</subfield><subfield code="2">fast</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Time-series analysis</subfield><subfield code="2">fast</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Nichtlineare Zeitreihenanalyse</subfield><subfield code="2">gnd</subfield><subfield code="0">http://d-nb.info/gnd/4276267-4</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Wahrscheinlichkeitsverteilung</subfield><subfield code="2">gnd</subfield><subfield code="0">http://d-nb.info/gnd/4121894-2</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Dynamisches Modell</subfield><subfield code="2">gnd</subfield><subfield code="0">http://d-nb.info/gnd/4150932-8</subfield></datafield><datafield tag="758" ind1=" " ind2=" "><subfield code="i">has work:</subfield><subfield code="a">Dynamic models for volatility and heavy tails (Text)</subfield><subfield code="1">https://id.oclc.org/worldcat/entity/E39PCGJpw7pCVdj6grKJ89vtmm</subfield><subfield code="4">https://id.oclc.org/worldcat/ontology/hasWork</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Print version:</subfield><subfield code="a">Harvey, A.C. (Andrew C.).</subfield><subfield code="t">Dynamic models for volatility and heavy tails</subfield><subfield code="z">9781107034723</subfield><subfield code="w">(DLC) 2012036508</subfield><subfield code="w">(OCoLC)811777444</subfield></datafield><datafield tag="830" ind1=" " ind2="0"><subfield code="a">Econometric Society monographs ;</subfield><subfield code="v">no. 52.</subfield><subfield code="0">http://id.loc.gov/authorities/names/n84716218</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="l">FWS01</subfield><subfield code="p">ZDB-4-EBA</subfield><subfield code="q">FWS_PDA_EBA</subfield><subfield code="u">https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=533825</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="880" ind1="0" ind2="0"><subfield code="6">505-01/(S</subfield><subfield code="g">Machine generated contents note:</subfield><subfield code="g">1.1.</subfield><subfield code="t">Unobserved Components and Filters --</subfield><subfield code="g">1.2.</subfield><subfield code="t">Independence, White Noise and Martingale Differences --</subfield><subfield code="g">1.2.1.</subfield><subfield code="t">Law of Iterated Expectations and Optimal Predictions --</subfield><subfield code="g">1.2.2.</subfield><subfield code="t">Definitions and Properties --</subfield><subfield code="g">1.3.</subfield><subfield code="t">Volatility --</subfield><subfield code="g">1.3.1.</subfield><subfield code="t">Stochastic Volatility --</subfield><subfield code="g">1.3.2.</subfield><subfield code="t">Generalized Autoregressive Conditional Heteroscedasticity --</subfield><subfield code="g">1.3.3.</subfield><subfield code="t">Exponential GARCH --</subfield><subfield code="g">1.3.4.</subfield><subfield code="t">Variance, Scale and Outliers --</subfield><subfield code="g">1.3.5.</subfield><subfield code="t">Location/Scale Models --</subfield><subfield code="g">1.4.</subfield><subfield code="t">Dynamic Conditional Score Models --</subfield><subfield code="g">1.5.</subfield><subfield code="t">Distributions and Quantiles --</subfield><subfield code="g">1.6.</subfield><subfield code="t">Plan of Book --</subfield><subfield code="g">2.1.</subfield><subfield code="t">Distributions --</subfield><subfield code="g">2.1.1.</subfield><subfield code="t">Student's t Distribution --</subfield><subfield code="g">2.1.2.</subfield><subfield code="t">General Error Distribution --</subfield><subfield code="g">2.1.3.</subfield><subfield code="t">Beta Distribution --</subfield><subfield code="g">2.1.4.</subfield><subfield code="t">Gamma Distribution --</subfield><subfield code="g">2.2.</subfield><subfield code="t">Maximum Likelihood --</subfield><subfield code="g">2.2.1.</subfield><subfield code="t">Student's t Distribution --</subfield><subfield code="g">2.2.2.</subfield><subfield code="t">General Error Distribution --</subfield><subfield code="g">2.2.3.</subfield><subfield code="t">Gamma Distribution --</subfield><subfield code="g">2.2.4.</subfield><subfield code="t">Consistency and Asymptotic Normality* --</subfield><subfield code="g">2.3.</subfield><subfield code="t">Maximum Likelihood Estimation of Dynamic Conditional Score Models --</subfield><subfield code="g">2.3.1.</subfield><subfield code="t">Information Matrix Lemma --</subfield><subfield code="g">2.3.2.</subfield><subfield code="t">Information Matrix for the First-Order Model --</subfield><subfield code="g">2.3.3.</subfield><subfield code="t">Information Matrix with the δ Parameterization* --</subfield><subfield code="g">2.3.4.</subfield><subfield code="t">Asymptotic Distribution --</subfield><subfield code="g">2.3.5.</subfield><subfield code="t">Consistency and Asymptotic Normality* --</subfield><subfield code="g">2.3.6.</subfield><subfield code="t">Nonstationarity --</subfield><subfield code="g">2.3.7.</subfield><subfield code="t">Several Parameters --</subfield><subfield code="g">2.4.</subfield><subfield code="t">Higher Order Models* --</subfield><subfield code="g">2.5.</subfield><subfield code="t">Tests --</subfield><subfield code="g">2.5.1.</subfield><subfield code="t">Serial Correlation --</subfield><subfield code="g">2.5.2.</subfield><subfield code="t">Goodness of Fit of Distributions --</subfield><subfield code="g">2.5.3.</subfield><subfield code="t">Residuals --</subfield><subfield code="g">2.5.4.</subfield><subfield code="t">Model Fit --</subfield><subfield code="g">2.6.</subfield><subfield code="t">Explanatory Variables --</subfield><subfield code="g">3.1.</subfield><subfield code="t">Dynamic Student's t Location Model --</subfield><subfield code="g">3.2.</subfield><subfield code="t">Basic Properties --</subfield><subfield code="g">3.2.1.</subfield><subfield code="t">Generalization and Reduced Form --</subfield><subfield code="g">3.2.2.</subfield><subfield code="t">Moments of the Observations --</subfield><subfield code="g">3.2.3.</subfield><subfield code="t">Autocorrelation Function --</subfield><subfield code="g">3.3.</subfield><subfield code="t">Maximum Likelihood Estimation --</subfield><subfield code="g">3.3.1.</subfield><subfield code="t">Asymptotic Distribution of the Maximum Likelihood Estimator --</subfield><subfield code="g">3.3.2.</subfield><subfield code="t">Monte Carlo Experiments --</subfield><subfield code="g">3.3.3.</subfield><subfield code="t">Application to U.S. GDP --</subfield><subfield code="g">3.4.</subfield><subfield code="t">Parameter Restrictions* --</subfield><subfield code="g">3.5.</subfield><subfield code="t">Higher Order Models and the State Space Form* --</subfield><subfield code="g">3.5.1.</subfield><subfield code="t">Linear Gaussian Models and the Kalman Filter --</subfield><subfield code="g">3.5.2.</subfield><subfield code="t">DCS Model --</subfield><subfield code="g">3.5.3.</subfield><subfield code="t">QARMA Models --</subfield><subfield code="g">3.6.</subfield><subfield code="t">Trend and Seasonality --</subfield><subfield code="g">3.6.1.</subfield><subfield code="t">Local Level Model --</subfield><subfield code="g">3.6.2.</subfield><subfield code="t">Application to Weekly Hours of Employees in U.S. Manufacturing --</subfield><subfield code="g">3.6.3.</subfield><subfield code="t">Local Linear Trend --</subfield><subfield code="g">3.6.4.</subfield><subfield code="t">Stochastic Seasonal --</subfield><subfield code="g">3.6.5.</subfield><subfield code="t">Application to Rail Travel --</subfield><subfield code="g">3.6.6.</subfield><subfield code="t">QARIMA and Seasonal QARIMA Models* --</subfield><subfield code="g">3.7.</subfield><subfield code="t">Smoothing --</subfield><subfield code="g">3.7.1.</subfield><subfield code="t">Weights --</subfield><subfield code="g">3.7.2.</subfield><subfield code="t">Smoothing Recursions for Linear State Space Models --</subfield><subfield code="g">3.7.3.</subfield><subfield code="t">Smoothing Recursions for DCS Models --</subfield><subfield code="g">3.7.4.</subfield><subfield code="t">Conditional Mode Estimation and the Score --</subfield><subfield code="g">3.8.</subfield><subfield code="t">Forecasting --</subfield><subfield code="g">3.8.1.</subfield><subfield code="t">QARMA Models --</subfield><subfield code="g">3.8.2.</subfield><subfield code="t">State Space Form* --</subfield><subfield code="g">3.9.</subfield><subfield code="t">Components and Long Memory --</subfield><subfield code="g">3.10.</subfield><subfield code="t">General Error Distribution --</subfield><subfield code="g">3.11.</subfield><subfield code="t">Skew Distributions --</subfield><subfield code="g">3.11.1.</subfield><subfield code="t">How to Skew a Distribution --</subfield><subfield code="g">3.11.2.</subfield><subfield code="t">Dynamic Skew-t Location Model --</subfield><subfield code="g">4.1.</subfield><subfield code="t">Beta-t-EGARCH --</subfield><subfield code="g">4.2.</subfield><subfield code="t">Properties of Stationary Beta-t-EGARCH Models --</subfield><subfield code="g">4.2.1.</subfield><subfield code="t">Exponential GARCH --</subfield><subfield code="g">4.2.2.</subfield><subfield code="t">Moments --</subfield><subfield code="g">4.2.3.</subfield><subfield code="t">Autocorrelation Functions of Squares and Powers of Absolute Values --</subfield><subfield code="g">4.2.4.</subfield><subfield code="t">Autocorrelations and Kurtosis --</subfield><subfield code="g">4.3.</subfield><subfield code="t">Leverage Effects --</subfield><subfield code="g">4.4.</subfield><subfield code="t">Gamma-GED-EGARCH --</subfield><subfield code="g">4.5.</subfield><subfield code="t">Forecasting --</subfield><subfield code="g">4.5.1.</subfield><subfield code="t">Beta-t-EGARCH --</subfield><subfield code="g">4.5.2.</subfield><subfield code="t">Gamma-GED-EGARCH --</subfield><subfield code="g">4.5.3.</subfield><subfield code="t">Integrated Exponential Models --</subfield><subfield code="g">4.5.4.</subfield><subfield code="t">Predictive Distribution --</subfield><subfield code="g">4.6.</subfield><subfield code="t">Maximum Likelihood Estimation and Inference --</subfield><subfield code="g">4.6.1.</subfield><subfield code="t">Asymptotic Theory for Beta-t-EGARCH --</subfield><subfield code="g">4.6.2.</subfield><subfield code="t">Monte Carlo Experiments --</subfield><subfield code="g">4.6.3.</subfield><subfield code="t">Gamma-GED-EGARCH --</subfield><subfield code="g">4.6.4.</subfield><subfield code="t">Leverage --</subfield><subfield code="g">4.7.</subfield><subfield code="t">Beta-t-GARCH --</subfield><subfield code="g">4.7.1.</subfield><subfield code="t">Properties of First-Order Model --</subfield><subfield code="g">4.7.2.</subfield><subfield code="t">Leverage Effects --</subfield><subfield code="g">4.7.3.</subfield><subfield code="t">Link with Beta-t-EGARCH --</subfield><subfield code="g">4.7.4.</subfield><subfield code="t">Estimation and Inference --</subfield><subfield code="g">4.7.5.</subfield><subfield code="t">Gamma-GED-GARCH --</subfield><subfield code="g">4.8.</subfield><subfield code="t">Smoothing --</subfield><subfield code="g">4.9.</subfield><subfield code="t">Application to Hang Seng and Dow Jones --</subfield><subfield code="g">4.10.</subfield><subfield code="t">Two Component Models --</subfield><subfield code="g">4.11.</subfield><subfield code="t">Trends, Seasonals and Explanatory Variables in Volatility Equations --</subfield><subfield code="g">4.12.</subfield><subfield code="t">Changing Location --</subfield><subfield code="g">4.12.1.</subfield><subfield code="t">Explanatory Variables --</subfield><subfield code="g">4.12.2.</subfield><subfield code="t">Stochastic Location and Stochastic Scale --</subfield><subfield code="g">4.13.</subfield><subfield code="t">Testing for Changing Volatility and Leverage --</subfield><subfield code="g">4.13.1.</subfield><subfield code="t">Portmanteau Test for Changing Volatility --</subfield><subfield code="g">4.13.2.</subfield><subfield code="t">Martingale Difference Test --</subfield><subfield code="g">4.13.3.</subfield><subfield code="t">Leverage --</subfield><subfield code="g">4.13.4.</subfield><subfield code="t">Diagnostics --</subfield><subfield code="g">4.14.</subfield><subfield code="t">Skew Distributions --</subfield><subfield code="g">4.15.</subfield><subfield code="t">Time-Varying Skewness and Kurtosis* --</subfield><subfield code="g">5.1.</subfield><subfield code="t">General Properties --</subfield><subfield code="g">5.1.1.</subfield><subfield code="t">Heavy Tails --</subfield><subfield code="g">5.1.2.</subfield><subfield code="t">Moments and Autocorrelations --</subfield><subfield code="g">5.1.3.</subfield><subfield code="t">Forecasts --</subfield><subfield code="g">5.1.4.</subfield><subfield code="t">Asymptotic Distribution of Maximum Likelihood Estimators --</subfield><subfield code="g">5.2.</subfield><subfield code="t">Generalized Gamma Distribution --</subfield><subfield code="g">5.2.1.</subfield><subfield code="t">Moments --</subfield><subfield code="g">5.2.2.</subfield><subfield code="t">Forecasts --</subfield><subfield code="g">5.2.3.</subfield><subfield code="t">Maximum Likelihood Estimation --</subfield><subfield code="g">5.3.</subfield><subfield code="t">Generalized Beta Distribution --</subfield><subfield code="g">5.3.1.</subfield><subfield code="t">Log-Logistic Distribution --</subfield><subfield code="g">5.3.2.</subfield><subfield code="t">Moments, Autocorrelations and Forecasts --</subfield><subfield code="g">5.3.3.</subfield><subfield code="t">Maximum Likelihood Estimation --</subfield><subfield code="g">5.3.4.</subfield><subfield code="t">Burr Distribution --</subfield><subfield code="g">5.3.5.</subfield><subfield code="t">Generalized Pareto Distribution --</subfield><subfield code="g">5.3.6.</subfield><subfield code="t">F Distribution --</subfield><subfield code="g">5.4.</subfield><subfield code="t">Log-Normal Distribution --</subfield><subfield code="g">5.5.</subfield><subfield code="t">Monte Carlo Experiments --</subfield><subfield code="g">5.6.</subfield><subfield code="t">Leverage, Long Memory and Diurnal Variation --</subfield><subfield code="g">5.7.</subfield><subfield code="t">Tests and Model Selection --</subfield><subfield code="g">5.8.</subfield><subfield code="t">Estimating Volatility from the Range --</subfield><subfield code="g">5.8.1.</subfield><subfield code="t">Application to Paris CAC and Dow Jones --</subfield><subfield code="g">5.8.2.</subfield><subfield code="t">Range-EGARCH Model --</subfield><subfield code="g">5.9.</subfield><subfield code="t">Duration --</subfield><subfield code="g">5.10.</subfield><subfield code="t">Realized Volatility --</subfield><subfield code="g">5.11.</subfield><subfield code="t">Count Data and Qualitative Observations --</subfield><subfield code="g">6.1.</subfield><subfield code="t">Kernel Density Estimation for Time Series --</subfield><subfield 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code="t">ARMA-GARCH Residuals --</subfield><subfield code="g">6.4.3.</subfield><subfield code="t">Bandwidth and Tails --</subfield><subfield code="g">7.1.</subfield><subfield code="t">Multivariate Distributions --</subfield><subfield code="g">7.1.1.</subfield><subfield code="t">Estimation --</subfield><subfield code="g">7.1.2.</subfield><subfield code="t">Regression --</subfield><subfield code="g">7.1.3.</subfield><subfield code="t">Dynamic Models --</subfield><subfield code="g">7.2.</subfield><subfield code="t">Multivariate Location Models --</subfield><subfield code="g">7.2.1.</subfield><subfield code="t">Structural Time Series Models --</subfield><subfield code="g">7.2.2.</subfield><subfield code="t">DCS Model for the Multivariate t --</subfield><subfield code="g">7.2.3.</subfield><subfield code="t">Asymptotic Theory* --</subfield><subfield code="g">7.2.4.</subfield><subfield code="t">Regression and Errors in Variables --</subfield><subfield code="g">7.3.</subfield><subfield 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| id | ZDB-4-EBA-ocn857489673 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:25:31Z |
| institution | BVB |
| isbn | 9781107336889 1107336880 1139540939 9781139540933 9781107335226 1107335221 9781107333567 1107333563 |
| language | English |
| lccn | 2012036508 |
| oclc_num | 857489673 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (xviii, 261 pages) : illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 2013 |
| publishDateSearch | 2013 |
| publishDateSort | 2013 |
| publisher | Cambridge University Press, |
| record_format | marc |
| series | Econometric Society monographs ; |
| series2 | Econometric society monographs ; |
| spelling | Harvey, A. C. (Andrew C.) https://id.oclc.org/worldcat/entity/E39PBJxxj79qDhWGrRQTFHdbBP http://id.loc.gov/authorities/names/n81064640 Dynamic models for volatility and heavy tails : with applications to financial and economic time series / Andrew C. Harvey. Cambridge ; New York : Cambridge University Press, 2013. 1 online resource (xviii, 261 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier data file Econometric society monographs ; 52 Includes bibliographical references (pages 247-254) and indexes. Print version record. Presents a statistical theory for a class of nonlinear time-series models. The overall approach will be of interest to econometricians and statisticians. 880-01 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. Econometrics. http://id.loc.gov/authorities/subjects/sh85040763 Finance Mathematical models. http://id.loc.gov/authorities/subjects/sh85048260 Time-series analysis. http://id.loc.gov/authorities/subjects/sh85135430 Économétrie. Finances Modèles mathématiques. Série chronologique. BUSINESS & ECONOMICS Economics General. bisacsh BUSINESS & ECONOMICS Reference. bisacsh Finanzas Modelos matemáticos embne Análisis de series temporales embucm Econometrics fast Finance Mathematical models fast Time-series analysis fast Nichtlineare Zeitreihenanalyse gnd http://d-nb.info/gnd/4276267-4 Wahrscheinlichkeitsverteilung gnd http://d-nb.info/gnd/4121894-2 Dynamisches Modell gnd http://d-nb.info/gnd/4150932-8 has work: Dynamic models for volatility and heavy tails (Text) https://id.oclc.org/worldcat/entity/E39PCGJpw7pCVdj6grKJ89vtmm https://id.oclc.org/worldcat/ontology/hasWork Print version: Harvey, A.C. (Andrew C.). Dynamic models for volatility and heavy tails 9781107034723 (DLC) 2012036508 (OCoLC)811777444 Econometric Society monographs ; no. 52. http://id.loc.gov/authorities/names/n84716218 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=533825 Volltext 505-01/(S 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. |
| spellingShingle | Harvey, A. C. (Andrew C.) Dynamic models for volatility and heavy tails : with applications to financial and economic time series / Econometric Society monographs ; 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. Econometrics. http://id.loc.gov/authorities/subjects/sh85040763 Finance Mathematical models. http://id.loc.gov/authorities/subjects/sh85048260 Time-series analysis. http://id.loc.gov/authorities/subjects/sh85135430 Économétrie. Finances Modèles mathématiques. Série chronologique. BUSINESS & ECONOMICS Economics General. bisacsh BUSINESS & ECONOMICS Reference. bisacsh Finanzas Modelos matemáticos embne Análisis de series temporales embucm Econometrics fast Finance Mathematical models fast Time-series analysis fast Nichtlineare Zeitreihenanalyse gnd http://d-nb.info/gnd/4276267-4 Wahrscheinlichkeitsverteilung gnd http://d-nb.info/gnd/4121894-2 Dynamisches Modell gnd http://d-nb.info/gnd/4150932-8 |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85040763 http://id.loc.gov/authorities/subjects/sh85048260 http://id.loc.gov/authorities/subjects/sh85135430 http://d-nb.info/gnd/4276267-4 http://d-nb.info/gnd/4121894-2 http://d-nb.info/gnd/4150932-8 |
| title | Dynamic models for volatility and heavy tails : with applications to financial and economic time series / |
| title_auth | Dynamic models for volatility and heavy tails : with applications to financial and economic time series / |
| title_exact_search | Dynamic models for volatility and heavy tails : with applications to financial and economic time series / |
| title_full | Dynamic models for volatility and heavy tails : with applications to financial and economic time series / Andrew C. Harvey. |
| title_fullStr | Dynamic models for volatility and heavy tails : with applications to financial and economic time series / Andrew C. Harvey. |
| title_full_unstemmed | Dynamic models for volatility and heavy tails : with applications to financial and economic time series / Andrew C. Harvey. |
| title_short | Dynamic models for volatility and heavy tails : |
| title_sort | dynamic models for volatility and heavy tails with applications to financial and economic time series |
| title_sub | with applications to financial and economic time series / |
| topic | Econometrics. http://id.loc.gov/authorities/subjects/sh85040763 Finance Mathematical models. http://id.loc.gov/authorities/subjects/sh85048260 Time-series analysis. http://id.loc.gov/authorities/subjects/sh85135430 Économétrie. Finances Modèles mathématiques. Série chronologique. BUSINESS & ECONOMICS Economics General. bisacsh BUSINESS & ECONOMICS Reference. bisacsh Finanzas Modelos matemáticos embne Análisis de series temporales embucm Econometrics fast Finance Mathematical models fast Time-series analysis fast Nichtlineare Zeitreihenanalyse gnd http://d-nb.info/gnd/4276267-4 Wahrscheinlichkeitsverteilung gnd http://d-nb.info/gnd/4121894-2 Dynamisches Modell gnd http://d-nb.info/gnd/4150932-8 |
| topic_facet | Econometrics. Finance Mathematical models. Time-series analysis. Économétrie. Finances Modèles mathématiques. Série chronologique. BUSINESS & ECONOMICS Economics General. BUSINESS & ECONOMICS Reference. Finanzas Modelos matemáticos Análisis de series temporales Econometrics Finance Mathematical models Time-series analysis Nichtlineare Zeitreihenanalyse Wahrscheinlichkeitsverteilung Dynamisches Modell |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=533825 |
| work_keys_str_mv | AT harveyac dynamicmodelsforvolatilityandheavytailswithapplicationstofinancialandeconomictimeseries |