Téléchargez le livre : Estimation in Conditionally Heteroscedastic Time Series Models

Estimation in Conditionally Heteroscedastic Time Series Models



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Éditeur :

Springer


Collection :

Lecture Notes in Statistics

Paru le : 2006-01-27



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Description
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 Nowadays ARCH has been replaced by more general and more sophisticated models, such as GARCH (generalized autoregressive heteroscedastic).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.
Pages
228 pages
Collection
Lecture Notes in Statistics
Parution
2006-01-27
Marque
Springer
EAN papier
9783540211358
EAN PDF
9783540269786
Informations sur l'ebook
Nombre pages copiables
2
Nombre pages imprimables
22
Taille du fichier
4762 Ko
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Prix
52,74 €

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