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Synopsis
In his seminal 1982 paper, Robert F. Engle described a time series model with a time-varying volatility. Engle showed that this model, which he called ARCH (autoregressive conditionally heteroscedastic), is well-suited for the description of economic and financial price. Nowadays ARCH has been replaced by more general and more sophisticated models, such as GARCH (generalized autoregressive heteroscedastic).
This monograph concentrates on mathematical statistical problems associated with fitting conditionally heteroscedastic time series models to data. This includes the classical statistical issues of consistency and limiting distribution of estimators. Particular attention is addressed to (quasi) maximum likelihood estimation and misspecified models, along to phenomena due to heavy-tailed innovations. The used methods are based on techniques applied to the analysis of stochastic recurrence equations. Proofs and arguments are given wherever possible in full mathematical rigour. Moreover, the theory is illustrated by examples and simulation studies.
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Estimation in Conditionally Herteroscedastic Time Series Models
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Estimation in Conditionally Heteroscedastic Time Series Models (Lecture Notes in Statistics)
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Estimation in Conditionally Heteroscedastic Time Series Models
Edité par
Springer Berlin Heidelberg Nov 2004, 2004
ISBN 10 : 3540211357
ISBN 13 : 9783540211358
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In his seminal 1982 paper, Robert F. Engle described a time series model with a time-varying volatility. Engle showed that this model, which he called ARCH (autoregressive conditionally heteroscedastic), is well-suited for the description of economic and financial price. Nowadays ARCH has been replaced by more general and more sophisticated models, such as GARCH (generalized autoregressive heteroscedastic). This monograph concentrates on mathematical statistical problems associated with fitting conditionally heteroscedastic time series models to data. This includes the classical statistical issues of consistency and limiting distribution of estimators. Particular attention is addressed to (quasi) maximum likelihood estimation and misspecified models, along to phenomena due to heavy-tailed innovations. The used methods are based on techniques applied to the analysis of stochastic recurrence equations. Proofs and arguments are given wherever possible in full mathematical rigour. Moreover, the theory is illustrated by examples and simulation studies. 248 pp. Englisch. N° de réf. du vendeur 9783540211358
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Estimation in Conditionally Heteroscedastic Time Series Models
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Estimation in Conditionally Heteroscedastic Time Series Models
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Etat : New. Print on Demand pp. 248 Illus. N° de réf. du vendeur 7586153
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Estimation in Conditionally Heteroscedastic Time Series Models
impression à la demandeVendeur : Biblios, Frankfurt am main, HESSE, Allemagne
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Etat : New. PRINT ON DEMAND pp. 248. N° de réf. du vendeur 18294588
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Estimation in Conditionally Heteroscedastic Time Series Models
Edité par
Springer Berlin Heidelberg, 2004
ISBN 10 : 3540211357
ISBN 13 : 9783540211358
Neuf
Kartoniert / Broschiert
Vendeur : moluna, Greven, Allemagne
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Kartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. In his seminal 1982 paper, Robert F. Engle described a time series model with a time-varying volatility. Engle showed that this model, which he called ARCH (autoregressive conditionally heteroscedastic), is well-suited for the description of economic and. N° de réf. du vendeur 4885036
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Estimation in Conditionally Heteroscedastic Time Series Models
Edité par
Springer, Springer Vieweg Nov 2004, 2004
ISBN 10 : 3540211357
ISBN 13 : 9783540211358
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -In his seminal 1982 paper, Robert F. Engle described a time series model with a time-varying volatility. Engle showed that this model, which he called ARCH (autoregressive conditionally heteroscedastic), is well-suited for the description of economic and financial price. Nowadays ARCH has been replaced by more general and more sophisticated models, such as GARCH (generalized autoregressive heteroscedastic).This monograph concentrates on mathematical statistical problems associated with fitting conditionally heteroscedastic time series models to data. This includes the classical statistical issues of consistency and limiting distribution of estimators. Particular attention is addressed to (quasi) maximum likelihood estimation and misspecified models, along to phenomena due to heavy-tailed innovations. The used methods are based on techniques applied to the analysis of stochastic recurrence equations. Proofs and arguments are given wherever possible in full mathematical rigour. Moreover, the theory is illustrated by examples and simulation studies.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 248 pp. Englisch. N° de réf. du vendeur 9783540211358
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Estimation in Conditionally Heteroscedastic Time Series Models
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - In his seminal 1982 paper, Robert F. Engle described a time series model with a time-varying volatility. Engle showed that this model, which he called ARCH (autoregressive conditionally heteroscedastic), is well-suited for the description of economic and financial price. Nowadays ARCH has been replaced by more general and more sophisticated models, such as GARCH (generalized autoregressive heteroscedastic). This monograph concentrates on mathematical statistical problems associated with fitting conditionally heteroscedastic time series models to data. This includes the classical statistical issues of consistency and limiting distribution of estimators. Particular attention is addressed to (quasi) maximum likelihood estimation and misspecified models, along to phenomena due to heavy-tailed innovations. The used methods are based on techniques applied to the analysis of stochastic recurrence equations. Proofs and arguments are given wherever possible in full mathematical rigour. Moreover, the theory is illustrated by examples and simulation studies. N° de réf. du vendeur 9783540211358
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Estimation in Conditionally Herteroscedastic Time Series Models
Vendeur : BennettBooksLtd, Los Angeles, CA, Etats-Unis
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paperback. Etat : New. In shrink wrap. Looks like an interesting title! N° de réf. du vendeur Q-3540211357
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