Estimation in Conditionally Heteroscedastic Time Series Models

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 repla...

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Detalles Bibliográficos
Autor principal: Straumann, Daniel
Lenguaje:eng
Publicado: Springer 2006
Materias:
Mathematical Physics and Mathematics
Acceso en línea:https://dx.doi.org/10.1007/b138400
http://cds.cern.ch/record/1413596
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author Straumann, Daniel
author_facet Straumann, Daniel
author_sort Straumann, Daniel
collection CERN
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 Nowadays ARCH has been replaced by more general and more sophisticated models, such as GARCH (generalized autoregressive heteroscedastic).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 is
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publishDate 2006
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spelling cern-14135962021-04-22T00:41:57Zdoi:10.1007/b138400http://cds.cern.ch/record/1413596engStraumann, DanielEstimation in Conditionally Heteroscedastic Time Series ModelsMathematical Physics and MathematicsIn 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 Nowadays ARCH has been replaced by more general and more sophisticated models, such as GARCH (generalized autoregressive heteroscedastic).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 isSpringeroai:cds.cern.ch:14135962006
spellingShingle Mathematical Physics and Mathematics
Straumann, Daniel
Estimation in Conditionally Heteroscedastic Time Series Models
title Estimation in Conditionally Heteroscedastic Time Series Models
title_full Estimation in Conditionally Heteroscedastic Time Series Models
title_fullStr Estimation in Conditionally Heteroscedastic Time Series Models
title_full_unstemmed Estimation in Conditionally Heteroscedastic Time Series Models
title_short Estimation in Conditionally Heteroscedastic Time Series Models
title_sort estimation in conditionally heteroscedastic time series models
topic Mathematical Physics and Mathematics
url https://dx.doi.org/10.1007/b138400
http://cds.cern.ch/record/1413596
work_keys_str_mv AT straumanndaniel estimationinconditionallyheteroscedastictimeseriesmodels

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