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Titlebook: Estimation in Conditionally Heteroscedastic Time Series Models; Daniel Straumann Book 2005 Springer-Verlag Berlin Heidelberg 2005 Estimato

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書(shū)目名稱Estimation in Conditionally Heteroscedastic Time Series Models
編輯Daniel Straumann
視頻videohttp://file.papertrans.cn/316/315782/315782.mp4
叢書(shū)名稱Lecture Notes in Statistics
圖書(shū)封面Titlebook: Estimation in Conditionally Heteroscedastic Time Series Models;  Daniel Straumann Book 2005 Springer-Verlag Berlin Heidelberg 2005 Estimato
描述.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 Nowadays ARCH has been replaced by more general and more sophisticated models, such as GARCH (generalized autoregressive heteroscedastic).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. .
出版日期Book 2005
關(guān)鍵詞Estimator; Financial Time Series; Fitting; GARCH; Likelihood; Parameter; innovation; time series; volatility
版次1
doihttps://doi.org/10.1007/b138400
isbn_softcover978-3-540-21135-8
isbn_ebook978-3-540-26978-6Series ISSN 0930-0325 Series E-ISSN 2197-7186
issn_series 0930-0325
copyrightSpringer-Verlag Berlin Heidelberg 2005
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0930-0325 stic recurrence equations. Proofs and arguments are given wherever possible in full mathematical rigour. Moreover, the theory is illustrated by examples and simulation studies. .978-3-540-21135-8978-3-540-26978-6Series ISSN 0930-0325 Series E-ISSN 2197-7186
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,Whittle Estimation in a Heavy—tailed GARCH(1,1) Model,
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https://doi.org/10.1007/b138400Estimator; Financial Time Series; Fitting; GARCH; Likelihood; Parameter; innovation; time series; volatility
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