Estimation in Conditionally Heteroscedastic Time Series Models / Lecture Notes in Statistics Bd.181 (PDF)

Name: Estimation in Conditionally Heteroscedastic Time Series Models / Lecture Notes in Statistics Bd.181
Brand: Springer-Verlag GmbH
SKU: 29643233
Price: 53.49 EUR
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(Sprache: Englisch)

Autor: Daniel Straumann

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Estimation in Conditionally Heteroscedastic Time Series Models / Lecture Notes in Statistics Bd.181, Daniel Straumann

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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 Nowadays ARCH has been replaced by more general and more sophisticated models, such as GARCH (generalized autoregressive heteroscedastic).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.

Inhaltsverzeichnis zu „Estimation in Conditionally Heteroscedastic Time Series Models / Lecture Notes in Statistics Bd.181 (PDF)“

Introduction.- Financial time series: facts and models.- Some mathematical tools.- Parameter estimation: an overview.- The QMLE in heteroscedastic time series models: a stochastic recurrence equations approach.- Maximum-likelihood estimation in conditionally heteroscedastic time series models.- Whittle estimation in a heavy-tailed GARCH (1,1) model.

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Bibliographische Angaben

Autor: Daniel Straumann
2006, 2005, 228 Seiten, Englisch
Verlag: Springer-Verlag GmbH
ISBN-10: 3540269789
ISBN-13: 9783540269786
Erscheinungsdatum: 27.01.2006

Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.

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Dateiformat: PDF
Größe: 4.92 MB
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Englisch

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From the reviews of the first edition:

"The book deals with conditionally heteroscedastic time series models. It covers classical and new topics of parameter estimation in such models. … There are a lot of various examples and remarks which clarify the presented general results. Some numerical examples and simulations are given. Detailed discussions and comparisons with known results are presented in each chapter." (Andrew Olenko, Zentralblatt MATH, Vol. 1086, 2006)

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