Estimation in Conditionally Heteroscedastic Time Series Models

Name: Estimation in Conditionally Heteroscedastic Time Series Models
Brand: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
SKU: 9783540211358
Price: 1147 CZK
Availability: InStock

Autor: D. Straumann

Jazyk:

Vazba: Brožovaná

Vydavatel: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG

Dostupnost: Skladem u dodavatele

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In his seminal 1982 paper, Robert F. Engle described a time series model witha time-varying volatili...

Informace o knize

Autor

D. Straumann

Jazyk

Angličtina

Vazba

Kniha - Brožovaná

Vydáno

2004

Stránek

228

EAN

9783540211358

ISBN

3540211357

Enbook ID

01559119

Vydavatel

Springer-Verlag Berlin and Heidelberg GmbH & Co. KG

Hmotnost

780

Rozměry

155 x 235 x 14

Kompletní popis

In his seminal 1982 paper, Robert F. Engle described a time series model witha 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 Nowadays ARCH has been replaced by more general and more sophisticated models, such as GARCH (generalized autoregressive heteroscedastic).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 Heteroscedastic Time Series Models

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