On the Distribution Estimation of Power Threshold Garch Processes

The aim of this article is to estimate the probability distribution of power threshold generalized autoregressive conditional heteroskedasticity processes by establishing bounds for their finite dimensional laws. These bounds only depend on the parameters of the model and on the distribution functio...
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Autor*in:	Gonçalves, Esmeralda [verfasserIn] Leite, Joana Mendes‐Lopes, NazarÉ

Format:	Artikel
Sprache:	Englisch

Erschienen:	2016

Rechteinformationen:	Nutzungsrecht: Copyright © 2016 Wiley Publishing Ltd

Schlagwörter:	threshold GARCH processes Finite dimensional laws Studies Estimating techniques Stochastic models Probability distribution Time series

Übergeordnetes Werk:	Enthalten in: Journal of time series analysis - Oxford : Wiley-Blackwell, 1980, 37(2016), 5, Seite 579-602
Übergeordnetes Werk:	volume:37 ; year:2016 ; number:5 ; pages:579-602

Links:	Volltext Link aufrufen Link aufrufen

DOI / URN:	10.1111/jtsa.12173

Katalog-ID:	OLC1982383518

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spelling	10.1111/jtsa.12173 doi PQ20170206 (DE-627)OLC1982383518 (DE-599)GBVOLC1982383518 (PRQ)p1593-45defbce3e1c3674ab595ac73e95cdbb2a781f799f99226959c1cde00ceee9f20 (KEY)0102813820160000037000500579onthedistributionestimationofpowerthresholdgarchpr DE-627 ger DE-627 rakwb eng 510 DNB 31.73 bkl Gonçalves, Esmeralda verfasserin aut On the Distribution Estimation of Power Threshold Garch Processes 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The aim of this article is to estimate the probability distribution of power threshold generalized autoregressive conditional heteroskedasticity processes by establishing bounds for their finite dimensional laws. These bounds only depend on the parameters of the model and on the distribution function of its independent generating process. The application of this study to some particular models allows us to conjecture that this procedure is an adequate alternative to the corresponding estimation using the empirical distribution functions, particularly useful in the development of control charts for this kind of models. Nutzungsrecht: Copyright © 2016 Wiley Publishing Ltd threshold GARCH processes Finite dimensional laws Studies Estimating techniques Stochastic models Probability distribution Time series Leite, Joana oth Mendes‐Lopes, NazarÉ oth Enthalten in Journal of time series analysis Oxford : Wiley-Blackwell, 1980 37(2016), 5, Seite 579-602 (DE-627)130624454 (DE-600)796625-8 (DE-576)016130901 0143-9782 nnns volume:37 year:2016 number:5 pages:579-602 http://dx.doi.org/10.1111/jtsa.12173 Volltext http://onlinelibrary.wiley.com/doi/10.1111/jtsa.12173/abstract http://search.proquest.com/docview/1806618143 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_26 GBV_ILN_60 GBV_ILN_70 GBV_ILN_2015 31.73 AVZ AR 37 2016 5 579-602
allfields_unstemmed	10.1111/jtsa.12173 doi PQ20170206 (DE-627)OLC1982383518 (DE-599)GBVOLC1982383518 (PRQ)p1593-45defbce3e1c3674ab595ac73e95cdbb2a781f799f99226959c1cde00ceee9f20 (KEY)0102813820160000037000500579onthedistributionestimationofpowerthresholdgarchpr DE-627 ger DE-627 rakwb eng 510 DNB 31.73 bkl Gonçalves, Esmeralda verfasserin aut On the Distribution Estimation of Power Threshold Garch Processes 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The aim of this article is to estimate the probability distribution of power threshold generalized autoregressive conditional heteroskedasticity processes by establishing bounds for their finite dimensional laws. These bounds only depend on the parameters of the model and on the distribution function of its independent generating process. The application of this study to some particular models allows us to conjecture that this procedure is an adequate alternative to the corresponding estimation using the empirical distribution functions, particularly useful in the development of control charts for this kind of models. Nutzungsrecht: Copyright © 2016 Wiley Publishing Ltd threshold GARCH processes Finite dimensional laws Studies Estimating techniques Stochastic models Probability distribution Time series Leite, Joana oth Mendes‐Lopes, NazarÉ oth Enthalten in Journal of time series analysis Oxford : Wiley-Blackwell, 1980 37(2016), 5, Seite 579-602 (DE-627)130624454 (DE-600)796625-8 (DE-576)016130901 0143-9782 nnns volume:37 year:2016 number:5 pages:579-602 http://dx.doi.org/10.1111/jtsa.12173 Volltext http://onlinelibrary.wiley.com/doi/10.1111/jtsa.12173/abstract http://search.proquest.com/docview/1806618143 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_26 GBV_ILN_60 GBV_ILN_70 GBV_ILN_2015 31.73 AVZ AR 37 2016 5 579-602
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allfieldsSound	10.1111/jtsa.12173 doi PQ20170206 (DE-627)OLC1982383518 (DE-599)GBVOLC1982383518 (PRQ)p1593-45defbce3e1c3674ab595ac73e95cdbb2a781f799f99226959c1cde00ceee9f20 (KEY)0102813820160000037000500579onthedistributionestimationofpowerthresholdgarchpr DE-627 ger DE-627 rakwb eng 510 DNB 31.73 bkl Gonçalves, Esmeralda verfasserin aut On the Distribution Estimation of Power Threshold Garch Processes 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The aim of this article is to estimate the probability distribution of power threshold generalized autoregressive conditional heteroskedasticity processes by establishing bounds for their finite dimensional laws. These bounds only depend on the parameters of the model and on the distribution function of its independent generating process. The application of this study to some particular models allows us to conjecture that this procedure is an adequate alternative to the corresponding estimation using the empirical distribution functions, particularly useful in the development of control charts for this kind of models. Nutzungsrecht: Copyright © 2016 Wiley Publishing Ltd threshold GARCH processes Finite dimensional laws Studies Estimating techniques Stochastic models Probability distribution Time series Leite, Joana oth Mendes‐Lopes, NazarÉ oth Enthalten in Journal of time series analysis Oxford : Wiley-Blackwell, 1980 37(2016), 5, Seite 579-602 (DE-627)130624454 (DE-600)796625-8 (DE-576)016130901 0143-9782 nnns volume:37 year:2016 number:5 pages:579-602 http://dx.doi.org/10.1111/jtsa.12173 Volltext http://onlinelibrary.wiley.com/doi/10.1111/jtsa.12173/abstract http://search.proquest.com/docview/1806618143 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_26 GBV_ILN_60 GBV_ILN_70 GBV_ILN_2015 31.73 AVZ AR 37 2016 5 579-602
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author	Gonçalves, Esmeralda
spellingShingle	Gonçalves, Esmeralda ddc 510 bkl 31.73 misc threshold GARCH processes misc Finite dimensional laws misc Studies misc Estimating techniques misc Stochastic models misc Probability distribution misc Time series On the Distribution Estimation of Power Threshold Garch Processes
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title_auth	On the Distribution Estimation of Power Threshold Garch Processes
abstract	The aim of this article is to estimate the probability distribution of power threshold generalized autoregressive conditional heteroskedasticity processes by establishing bounds for their finite dimensional laws. These bounds only depend on the parameters of the model and on the distribution function of its independent generating process. The application of this study to some particular models allows us to conjecture that this procedure is an adequate alternative to the corresponding estimation using the empirical distribution functions, particularly useful in the development of control charts for this kind of models.
abstractGer	The aim of this article is to estimate the probability distribution of power threshold generalized autoregressive conditional heteroskedasticity processes by establishing bounds for their finite dimensional laws. These bounds only depend on the parameters of the model and on the distribution function of its independent generating process. The application of this study to some particular models allows us to conjecture that this procedure is an adequate alternative to the corresponding estimation using the empirical distribution functions, particularly useful in the development of control charts for this kind of models.
abstract_unstemmed	The aim of this article is to estimate the probability distribution of power threshold generalized autoregressive conditional heteroskedasticity processes by establishing bounds for their finite dimensional laws. These bounds only depend on the parameters of the model and on the distribution function of its independent generating process. The application of this study to some particular models allows us to conjecture that this procedure is an adequate alternative to the corresponding estimation using the empirical distribution functions, particularly useful in the development of control charts for this kind of models.
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title_short	On the Distribution Estimation of Power Threshold Garch Processes
url	http://dx.doi.org/10.1111/jtsa.12173 http://onlinelibrary.wiley.com/doi/10.1111/jtsa.12173/abstract http://search.proquest.com/docview/1806618143
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author2	Leite, Joana Mendes‐Lopes, NazarÉ
author2Str	Leite, Joana Mendes‐Lopes, NazarÉ
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author2_role	oth oth
doi_str	10.1111/jtsa.12173
up_date	2024-07-03T17:03:15.148Z
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