A Time-Varying Coefficient Double Threshold GARCH Model with Explanatory Variables

In this article, we consider the nonparametric inference for the time-varying coefficient double-threshold generalized autoregressive conditional heteroscedastic models. The quasi-maximum exponential likelihood estimators (QMELEs) of the model’s parameters and the asymptotic properties of the estima...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Tongwei Zhang [verfasserIn] Lianyan Fu [verfasserIn] Dehui Wang [verfasserIn] Zhuoxi Yu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	time-varying coefficient threshold model QMELE asymptotic properties financial time series explanatory variables

Übergeordnetes Werk:	In: Axioms - MDPI AG, 2012, 12(2023), 5, p 476
Übergeordnetes Werk:	volume:12 ; year:2023 ; number:5, p 476

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/axioms12050476

Katalog-ID:	DOAJ094419795

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allfields	10.3390/axioms12050476 doi (DE-627)DOAJ094419795 (DE-599)DOAJ381af02930874fe48ae08ab2c217e95d DE-627 ger DE-627 rakwb eng QA1-939 Tongwei Zhang verfasserin aut A Time-Varying Coefficient Double Threshold GARCH Model with Explanatory Variables 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this article, we consider the nonparametric inference for the time-varying coefficient double-threshold generalized autoregressive conditional heteroscedastic models. The quasi-maximum exponential likelihood estimators (QMELEs) of the model’s parameters and the asymptotic properties of the estimators are obtained. The simulation study implies that the distribution of the estimators is asymptotically normal. A real data application to stock returns is given. Both the simulations and real data example imply that the model and the QMELE are proper, compatible and accurately fit the financial time series data of the Nikkei 225. time-varying coefficient threshold model QMELE asymptotic properties financial time series explanatory variables Mathematics Lianyan Fu verfasserin aut Dehui Wang verfasserin aut Zhuoxi Yu verfasserin aut In Axioms MDPI AG, 2012 12(2023), 5, p 476 (DE-627)718622030 (DE-600)2661511-3 20751680 nnns volume:12 year:2023 number:5, p 476 https://doi.org/10.3390/axioms12050476 kostenfrei https://doaj.org/article/381af02930874fe48ae08ab2c217e95d kostenfrei https://www.mdpi.com/2075-1680/12/5/476 kostenfrei https://doaj.org/toc/2075-1680 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2023 5, p 476
spelling	10.3390/axioms12050476 doi (DE-627)DOAJ094419795 (DE-599)DOAJ381af02930874fe48ae08ab2c217e95d DE-627 ger DE-627 rakwb eng QA1-939 Tongwei Zhang verfasserin aut A Time-Varying Coefficient Double Threshold GARCH Model with Explanatory Variables 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this article, we consider the nonparametric inference for the time-varying coefficient double-threshold generalized autoregressive conditional heteroscedastic models. The quasi-maximum exponential likelihood estimators (QMELEs) of the model’s parameters and the asymptotic properties of the estimators are obtained. The simulation study implies that the distribution of the estimators is asymptotically normal. A real data application to stock returns is given. Both the simulations and real data example imply that the model and the QMELE are proper, compatible and accurately fit the financial time series data of the Nikkei 225. time-varying coefficient threshold model QMELE asymptotic properties financial time series explanatory variables Mathematics Lianyan Fu verfasserin aut Dehui Wang verfasserin aut Zhuoxi Yu verfasserin aut In Axioms MDPI AG, 2012 12(2023), 5, p 476 (DE-627)718622030 (DE-600)2661511-3 20751680 nnns volume:12 year:2023 number:5, p 476 https://doi.org/10.3390/axioms12050476 kostenfrei https://doaj.org/article/381af02930874fe48ae08ab2c217e95d kostenfrei https://www.mdpi.com/2075-1680/12/5/476 kostenfrei https://doaj.org/toc/2075-1680 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2023 5, p 476
allfields_unstemmed	10.3390/axioms12050476 doi (DE-627)DOAJ094419795 (DE-599)DOAJ381af02930874fe48ae08ab2c217e95d DE-627 ger DE-627 rakwb eng QA1-939 Tongwei Zhang verfasserin aut A Time-Varying Coefficient Double Threshold GARCH Model with Explanatory Variables 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this article, we consider the nonparametric inference for the time-varying coefficient double-threshold generalized autoregressive conditional heteroscedastic models. The quasi-maximum exponential likelihood estimators (QMELEs) of the model’s parameters and the asymptotic properties of the estimators are obtained. The simulation study implies that the distribution of the estimators is asymptotically normal. A real data application to stock returns is given. Both the simulations and real data example imply that the model and the QMELE are proper, compatible and accurately fit the financial time series data of the Nikkei 225. time-varying coefficient threshold model QMELE asymptotic properties financial time series explanatory variables Mathematics Lianyan Fu verfasserin aut Dehui Wang verfasserin aut Zhuoxi Yu verfasserin aut In Axioms MDPI AG, 2012 12(2023), 5, p 476 (DE-627)718622030 (DE-600)2661511-3 20751680 nnns volume:12 year:2023 number:5, p 476 https://doi.org/10.3390/axioms12050476 kostenfrei https://doaj.org/article/381af02930874fe48ae08ab2c217e95d kostenfrei https://www.mdpi.com/2075-1680/12/5/476 kostenfrei https://doaj.org/toc/2075-1680 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2023 5, p 476
allfieldsGer	10.3390/axioms12050476 doi (DE-627)DOAJ094419795 (DE-599)DOAJ381af02930874fe48ae08ab2c217e95d DE-627 ger DE-627 rakwb eng QA1-939 Tongwei Zhang verfasserin aut A Time-Varying Coefficient Double Threshold GARCH Model with Explanatory Variables 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this article, we consider the nonparametric inference for the time-varying coefficient double-threshold generalized autoregressive conditional heteroscedastic models. The quasi-maximum exponential likelihood estimators (QMELEs) of the model’s parameters and the asymptotic properties of the estimators are obtained. The simulation study implies that the distribution of the estimators is asymptotically normal. A real data application to stock returns is given. Both the simulations and real data example imply that the model and the QMELE are proper, compatible and accurately fit the financial time series data of the Nikkei 225. time-varying coefficient threshold model QMELE asymptotic properties financial time series explanatory variables Mathematics Lianyan Fu verfasserin aut Dehui Wang verfasserin aut Zhuoxi Yu verfasserin aut In Axioms MDPI AG, 2012 12(2023), 5, p 476 (DE-627)718622030 (DE-600)2661511-3 20751680 nnns volume:12 year:2023 number:5, p 476 https://doi.org/10.3390/axioms12050476 kostenfrei https://doaj.org/article/381af02930874fe48ae08ab2c217e95d kostenfrei https://www.mdpi.com/2075-1680/12/5/476 kostenfrei https://doaj.org/toc/2075-1680 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2023 5, p 476
allfieldsSound	10.3390/axioms12050476 doi (DE-627)DOAJ094419795 (DE-599)DOAJ381af02930874fe48ae08ab2c217e95d DE-627 ger DE-627 rakwb eng QA1-939 Tongwei Zhang verfasserin aut A Time-Varying Coefficient Double Threshold GARCH Model with Explanatory Variables 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this article, we consider the nonparametric inference for the time-varying coefficient double-threshold generalized autoregressive conditional heteroscedastic models. The quasi-maximum exponential likelihood estimators (QMELEs) of the model’s parameters and the asymptotic properties of the estimators are obtained. The simulation study implies that the distribution of the estimators is asymptotically normal. A real data application to stock returns is given. Both the simulations and real data example imply that the model and the QMELE are proper, compatible and accurately fit the financial time series data of the Nikkei 225. time-varying coefficient threshold model QMELE asymptotic properties financial time series explanatory variables Mathematics Lianyan Fu verfasserin aut Dehui Wang verfasserin aut Zhuoxi Yu verfasserin aut In Axioms MDPI AG, 2012 12(2023), 5, p 476 (DE-627)718622030 (DE-600)2661511-3 20751680 nnns volume:12 year:2023 number:5, p 476 https://doi.org/10.3390/axioms12050476 kostenfrei https://doaj.org/article/381af02930874fe48ae08ab2c217e95d kostenfrei https://www.mdpi.com/2075-1680/12/5/476 kostenfrei https://doaj.org/toc/2075-1680 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2023 5, p 476
language	English
source	In Axioms 12(2023), 5, p 476 volume:12 year:2023 number:5, p 476
sourceStr	In Axioms 12(2023), 5, p 476 volume:12 year:2023 number:5, p 476
format_phy_str_mv	Article
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topic_facet	time-varying coefficient threshold model QMELE asymptotic properties financial time series explanatory variables Mathematics
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authorswithroles_txt_mv	Tongwei Zhang @@aut@@ Lianyan Fu @@aut@@ Dehui Wang @@aut@@ Zhuoxi Yu @@aut@@
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callnumber-first	Q - Science
author	Tongwei Zhang
spellingShingle	Tongwei Zhang misc QA1-939 misc time-varying coefficient misc threshold model misc QMELE misc asymptotic properties misc financial time series misc explanatory variables misc Mathematics A Time-Varying Coefficient Double Threshold GARCH Model with Explanatory Variables
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topic_title	QA1-939 A Time-Varying Coefficient Double Threshold GARCH Model with Explanatory Variables time-varying coefficient threshold model QMELE asymptotic properties financial time series explanatory variables
topic	misc QA1-939 misc time-varying coefficient misc threshold model misc QMELE misc asymptotic properties misc financial time series misc explanatory variables misc Mathematics
topic_unstemmed	misc QA1-939 misc time-varying coefficient misc threshold model misc QMELE misc asymptotic properties misc financial time series misc explanatory variables misc Mathematics
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title	A Time-Varying Coefficient Double Threshold GARCH Model with Explanatory Variables
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title_sort	time-varying coefficient double threshold garch model with explanatory variables
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title_auth	A Time-Varying Coefficient Double Threshold GARCH Model with Explanatory Variables
abstract	In this article, we consider the nonparametric inference for the time-varying coefficient double-threshold generalized autoregressive conditional heteroscedastic models. The quasi-maximum exponential likelihood estimators (QMELEs) of the model’s parameters and the asymptotic properties of the estimators are obtained. The simulation study implies that the distribution of the estimators is asymptotically normal. A real data application to stock returns is given. Both the simulations and real data example imply that the model and the QMELE are proper, compatible and accurately fit the financial time series data of the Nikkei 225.
abstractGer	In this article, we consider the nonparametric inference for the time-varying coefficient double-threshold generalized autoregressive conditional heteroscedastic models. The quasi-maximum exponential likelihood estimators (QMELEs) of the model’s parameters and the asymptotic properties of the estimators are obtained. The simulation study implies that the distribution of the estimators is asymptotically normal. A real data application to stock returns is given. Both the simulations and real data example imply that the model and the QMELE are proper, compatible and accurately fit the financial time series data of the Nikkei 225.
abstract_unstemmed	In this article, we consider the nonparametric inference for the time-varying coefficient double-threshold generalized autoregressive conditional heteroscedastic models. The quasi-maximum exponential likelihood estimators (QMELEs) of the model’s parameters and the asymptotic properties of the estimators are obtained. The simulation study implies that the distribution of the estimators is asymptotically normal. A real data application to stock returns is given. Both the simulations and real data example imply that the model and the QMELE are proper, compatible and accurately fit the financial time series data of the Nikkei 225.
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container_issue	5, p 476
title_short	A Time-Varying Coefficient Double Threshold GARCH Model with Explanatory Variables
url	https://doi.org/10.3390/axioms12050476 https://doaj.org/article/381af02930874fe48ae08ab2c217e95d https://www.mdpi.com/2075-1680/12/5/476 https://doaj.org/toc/2075-1680
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A Time-Varying Coefficient Double Threshold GARCH Model with Explanatory Variables

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