Estimating copula-based extension of tail value-at-risk and its application in insurance claim

Dependent Tail Value-at-Risk, abbreviated as DTVaR, is a copula-based extension of Tail Value-at-Risk (TVaR). This risk measure is an expectation of a target loss once the loss and its associated loss are above their respective quantiles but bounded above by their respective larger quantiles. In thi...
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Autor*in:	Syuhada, Khreshna [verfasserIn] Neswan, Oki [verfasserIn] Parulian, Josaphat, Bony [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Rechteinformationen:	Open Access Namensnennung 4.0 International ; CC BY 4.0

Schlagwörter:	Dependent TVaR (DTVaR) Dependent Conditional Tail Variance (DCTV) insurance claim nonparametric estimators

Übergeordnetes Werk:	Enthalten in: Risks - Basel : MDPI, 2013, 10(2022), 6 vom: Juni, Artikel-ID 113, Seite 1-26
Übergeordnetes Werk:	volume:10 ; year:2022 ; number:6 ; month:06 ; elocationid:113 ; pages:1-26

Links:	Link aufrufen Link aufrufen

DOI / URN:	10.3390/risks10060113

Katalog-ID:	1816494232

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allfields	10.3390/risks10060113 doi (DE-627)1816494232 (DE-599)KXP1816494232 DE-627 ger DE-627 rda eng Syuhada, Khreshna verfasserin aut Estimating copula-based extension of tail value-at-risk and its application in insurance claim Khreshna Syuhada, Oki Neswan and Bony Parulian Josaphat 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Dependent Tail Value-at-Risk, abbreviated as DTVaR, is a copula-based extension of Tail Value-at-Risk (TVaR). This risk measure is an expectation of a target loss once the loss and its associated loss are above their respective quantiles but bounded above by their respective larger quantiles. In this paper, we propose nonparametric estimators for DTVaR and establish their property of consistency. Moreover, we also propose the variability measure around this expected value truncated by the quantiles, called the Dependent Conditional Tail Variance (DCTV). We use this measure for constructing confidence intervals of the DTVaR. Both parametric and nonparametric approaches for DTVaR estimations are explored. Furthermore, we assess the performance of DTVaR estimations using a proposed backtest based on the DCTV. As for the numerical study, we take an application in the insurance claim amount. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Dependent TVaR (DTVaR) (dpeaa)DE-206 Dependent Conditional Tail Variance (DCTV) (dpeaa)DE-206 insurance claim (dpeaa)DE-206 nonparametric estimators (dpeaa)DE-206 Neswan, Oki verfasserin aut Parulian, Josaphat, Bony verfasserin (DE-588)1269101382 (DE-627)1817777033 aut Enthalten in Risks Basel : MDPI, 2013 10(2022), 6 vom: Juni, Artikel-ID 113, Seite 1-26 Online-Ressource (DE-627)737288485 (DE-600)2704357-5 (DE-576)379467852 2227-9091 nnns volume:10 year:2022 number:6 month:06 elocationid:113 pages:1-26 https://www.mdpi.com/2227-9091/10/6/113/pdf?version=1653904512 Verlag kostenfrei http://doi.org/10.3390/risks10060113 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_152 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 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 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 10 2022 6 6 113 1-26 26 01 0206 4187288775 x1z 13-09-22 2403 01 DE-LFER 4195096073 00 --%%-- --%%-- n --%%-- l01 07-10-22 2403 01 DE-LFER http://doi.org/10.3390/risks10060113 2403 01 DE-LFER https://www.mdpi.com/2227-9091/10/6/113/pdf?version=1653904512
spelling	10.3390/risks10060113 doi (DE-627)1816494232 (DE-599)KXP1816494232 DE-627 ger DE-627 rda eng Syuhada, Khreshna verfasserin aut Estimating copula-based extension of tail value-at-risk and its application in insurance claim Khreshna Syuhada, Oki Neswan and Bony Parulian Josaphat 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Dependent Tail Value-at-Risk, abbreviated as DTVaR, is a copula-based extension of Tail Value-at-Risk (TVaR). This risk measure is an expectation of a target loss once the loss and its associated loss are above their respective quantiles but bounded above by their respective larger quantiles. In this paper, we propose nonparametric estimators for DTVaR and establish their property of consistency. Moreover, we also propose the variability measure around this expected value truncated by the quantiles, called the Dependent Conditional Tail Variance (DCTV). We use this measure for constructing confidence intervals of the DTVaR. Both parametric and nonparametric approaches for DTVaR estimations are explored. Furthermore, we assess the performance of DTVaR estimations using a proposed backtest based on the DCTV. As for the numerical study, we take an application in the insurance claim amount. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Dependent TVaR (DTVaR) (dpeaa)DE-206 Dependent Conditional Tail Variance (DCTV) (dpeaa)DE-206 insurance claim (dpeaa)DE-206 nonparametric estimators (dpeaa)DE-206 Neswan, Oki verfasserin aut Parulian, Josaphat, Bony verfasserin (DE-588)1269101382 (DE-627)1817777033 aut Enthalten in Risks Basel : MDPI, 2013 10(2022), 6 vom: Juni, Artikel-ID 113, Seite 1-26 Online-Ressource (DE-627)737288485 (DE-600)2704357-5 (DE-576)379467852 2227-9091 nnns volume:10 year:2022 number:6 month:06 elocationid:113 pages:1-26 https://www.mdpi.com/2227-9091/10/6/113/pdf?version=1653904512 Verlag kostenfrei http://doi.org/10.3390/risks10060113 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_152 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 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 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 10 2022 6 6 113 1-26 26 01 0206 4187288775 x1z 13-09-22 2403 01 DE-LFER 4195096073 00 --%%-- --%%-- n --%%-- l01 07-10-22 2403 01 DE-LFER http://doi.org/10.3390/risks10060113 2403 01 DE-LFER https://www.mdpi.com/2227-9091/10/6/113/pdf?version=1653904512
allfields_unstemmed	10.3390/risks10060113 doi (DE-627)1816494232 (DE-599)KXP1816494232 DE-627 ger DE-627 rda eng Syuhada, Khreshna verfasserin aut Estimating copula-based extension of tail value-at-risk and its application in insurance claim Khreshna Syuhada, Oki Neswan and Bony Parulian Josaphat 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Dependent Tail Value-at-Risk, abbreviated as DTVaR, is a copula-based extension of Tail Value-at-Risk (TVaR). This risk measure is an expectation of a target loss once the loss and its associated loss are above their respective quantiles but bounded above by their respective larger quantiles. In this paper, we propose nonparametric estimators for DTVaR and establish their property of consistency. Moreover, we also propose the variability measure around this expected value truncated by the quantiles, called the Dependent Conditional Tail Variance (DCTV). We use this measure for constructing confidence intervals of the DTVaR. Both parametric and nonparametric approaches for DTVaR estimations are explored. Furthermore, we assess the performance of DTVaR estimations using a proposed backtest based on the DCTV. As for the numerical study, we take an application in the insurance claim amount. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Dependent TVaR (DTVaR) (dpeaa)DE-206 Dependent Conditional Tail Variance (DCTV) (dpeaa)DE-206 insurance claim (dpeaa)DE-206 nonparametric estimators (dpeaa)DE-206 Neswan, Oki verfasserin aut Parulian, Josaphat, Bony verfasserin (DE-588)1269101382 (DE-627)1817777033 aut Enthalten in Risks Basel : MDPI, 2013 10(2022), 6 vom: Juni, Artikel-ID 113, Seite 1-26 Online-Ressource (DE-627)737288485 (DE-600)2704357-5 (DE-576)379467852 2227-9091 nnns volume:10 year:2022 number:6 month:06 elocationid:113 pages:1-26 https://www.mdpi.com/2227-9091/10/6/113/pdf?version=1653904512 Verlag kostenfrei http://doi.org/10.3390/risks10060113 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_152 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 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 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 10 2022 6 6 113 1-26 26 01 0206 4187288775 x1z 13-09-22 2403 01 DE-LFER 4195096073 00 --%%-- --%%-- n --%%-- l01 07-10-22 2403 01 DE-LFER http://doi.org/10.3390/risks10060113 2403 01 DE-LFER https://www.mdpi.com/2227-9091/10/6/113/pdf?version=1653904512
allfieldsGer	10.3390/risks10060113 doi (DE-627)1816494232 (DE-599)KXP1816494232 DE-627 ger DE-627 rda eng Syuhada, Khreshna verfasserin aut Estimating copula-based extension of tail value-at-risk and its application in insurance claim Khreshna Syuhada, Oki Neswan and Bony Parulian Josaphat 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Dependent Tail Value-at-Risk, abbreviated as DTVaR, is a copula-based extension of Tail Value-at-Risk (TVaR). This risk measure is an expectation of a target loss once the loss and its associated loss are above their respective quantiles but bounded above by their respective larger quantiles. In this paper, we propose nonparametric estimators for DTVaR and establish their property of consistency. Moreover, we also propose the variability measure around this expected value truncated by the quantiles, called the Dependent Conditional Tail Variance (DCTV). We use this measure for constructing confidence intervals of the DTVaR. Both parametric and nonparametric approaches for DTVaR estimations are explored. Furthermore, we assess the performance of DTVaR estimations using a proposed backtest based on the DCTV. As for the numerical study, we take an application in the insurance claim amount. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Dependent TVaR (DTVaR) (dpeaa)DE-206 Dependent Conditional Tail Variance (DCTV) (dpeaa)DE-206 insurance claim (dpeaa)DE-206 nonparametric estimators (dpeaa)DE-206 Neswan, Oki verfasserin aut Parulian, Josaphat, Bony verfasserin (DE-588)1269101382 (DE-627)1817777033 aut Enthalten in Risks Basel : MDPI, 2013 10(2022), 6 vom: Juni, Artikel-ID 113, Seite 1-26 Online-Ressource (DE-627)737288485 (DE-600)2704357-5 (DE-576)379467852 2227-9091 nnns volume:10 year:2022 number:6 month:06 elocationid:113 pages:1-26 https://www.mdpi.com/2227-9091/10/6/113/pdf?version=1653904512 Verlag kostenfrei http://doi.org/10.3390/risks10060113 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_152 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 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 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 10 2022 6 6 113 1-26 26 01 0206 4187288775 x1z 13-09-22 2403 01 DE-LFER 4195096073 00 --%%-- --%%-- n --%%-- l01 07-10-22 2403 01 DE-LFER http://doi.org/10.3390/risks10060113 2403 01 DE-LFER https://www.mdpi.com/2227-9091/10/6/113/pdf?version=1653904512
allfieldsSound	10.3390/risks10060113 doi (DE-627)1816494232 (DE-599)KXP1816494232 DE-627 ger DE-627 rda eng Syuhada, Khreshna verfasserin aut Estimating copula-based extension of tail value-at-risk and its application in insurance claim Khreshna Syuhada, Oki Neswan and Bony Parulian Josaphat 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Dependent Tail Value-at-Risk, abbreviated as DTVaR, is a copula-based extension of Tail Value-at-Risk (TVaR). This risk measure is an expectation of a target loss once the loss and its associated loss are above their respective quantiles but bounded above by their respective larger quantiles. In this paper, we propose nonparametric estimators for DTVaR and establish their property of consistency. Moreover, we also propose the variability measure around this expected value truncated by the quantiles, called the Dependent Conditional Tail Variance (DCTV). We use this measure for constructing confidence intervals of the DTVaR. Both parametric and nonparametric approaches for DTVaR estimations are explored. Furthermore, we assess the performance of DTVaR estimations using a proposed backtest based on the DCTV. As for the numerical study, we take an application in the insurance claim amount. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Dependent TVaR (DTVaR) (dpeaa)DE-206 Dependent Conditional Tail Variance (DCTV) (dpeaa)DE-206 insurance claim (dpeaa)DE-206 nonparametric estimators (dpeaa)DE-206 Neswan, Oki verfasserin aut Parulian, Josaphat, Bony verfasserin (DE-588)1269101382 (DE-627)1817777033 aut Enthalten in Risks Basel : MDPI, 2013 10(2022), 6 vom: Juni, Artikel-ID 113, Seite 1-26 Online-Ressource (DE-627)737288485 (DE-600)2704357-5 (DE-576)379467852 2227-9091 nnns volume:10 year:2022 number:6 month:06 elocationid:113 pages:1-26 https://www.mdpi.com/2227-9091/10/6/113/pdf?version=1653904512 Verlag kostenfrei http://doi.org/10.3390/risks10060113 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_152 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 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 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 10 2022 6 6 113 1-26 26 01 0206 4187288775 x1z 13-09-22 2403 01 DE-LFER 4195096073 00 --%%-- --%%-- n --%%-- l01 07-10-22 2403 01 DE-LFER http://doi.org/10.3390/risks10060113 2403 01 DE-LFER https://www.mdpi.com/2227-9091/10/6/113/pdf?version=1653904512
language	English
source	Enthalten in Risks 10(2022), 6 vom: Juni, Artikel-ID 113, Seite 1-26 volume:10 year:2022 number:6 month:06 elocationid:113 pages:1-26
sourceStr	Enthalten in Risks 10(2022), 6 vom: Juni, Artikel-ID 113, Seite 1-26 volume:10 year:2022 number:6 month:06 elocationid:113 pages:1-26
format_phy_str_mv	Article
building	26:1 2403:0
institution	findex.gbv.de
selectbib_iln_str_mv	26@1z 2403@01
topic_facet	Dependent TVaR (DTVaR) Dependent Conditional Tail Variance (DCTV) insurance claim nonparametric estimators
isfreeaccess_bool	true
container_title	Risks
authorswithroles_txt_mv	Syuhada, Khreshna @@aut@@ Neswan, Oki @@aut@@ Parulian, Josaphat, Bony @@aut@@
publishDateDaySort_date	2022-06-01T00:00:00Z
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author	Syuhada, Khreshna
spellingShingle	Syuhada, Khreshna misc Dependent TVaR (DTVaR) misc Dependent Conditional Tail Variance (DCTV) misc insurance claim misc nonparametric estimators Estimating copula-based extension of tail value-at-risk and its application in insurance claim
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topic_title	Estimating copula-based extension of tail value-at-risk and its application in insurance claim Khreshna Syuhada, Oki Neswan and Bony Parulian Josaphat Dependent TVaR (DTVaR) (dpeaa)DE-206 Dependent Conditional Tail Variance (DCTV) (dpeaa)DE-206 insurance claim (dpeaa)DE-206 nonparametric estimators (dpeaa)DE-206
topic	misc Dependent TVaR (DTVaR) misc Dependent Conditional Tail Variance (DCTV) misc insurance claim misc nonparametric estimators
topic_unstemmed	misc Dependent TVaR (DTVaR) misc Dependent Conditional Tail Variance (DCTV) misc insurance claim misc nonparametric estimators
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title	Estimating copula-based extension of tail value-at-risk and its application in insurance claim
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title_full	Estimating copula-based extension of tail value-at-risk and its application in insurance claim Khreshna Syuhada, Oki Neswan and Bony Parulian Josaphat
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title_sort	estimating copula-based extension of tail value-at-risk and its application in insurance claim
title_auth	Estimating copula-based extension of tail value-at-risk and its application in insurance claim
abstract	Dependent Tail Value-at-Risk, abbreviated as DTVaR, is a copula-based extension of Tail Value-at-Risk (TVaR). This risk measure is an expectation of a target loss once the loss and its associated loss are above their respective quantiles but bounded above by their respective larger quantiles. In this paper, we propose nonparametric estimators for DTVaR and establish their property of consistency. Moreover, we also propose the variability measure around this expected value truncated by the quantiles, called the Dependent Conditional Tail Variance (DCTV). We use this measure for constructing confidence intervals of the DTVaR. Both parametric and nonparametric approaches for DTVaR estimations are explored. Furthermore, we assess the performance of DTVaR estimations using a proposed backtest based on the DCTV. As for the numerical study, we take an application in the insurance claim amount.
abstractGer	Dependent Tail Value-at-Risk, abbreviated as DTVaR, is a copula-based extension of Tail Value-at-Risk (TVaR). This risk measure is an expectation of a target loss once the loss and its associated loss are above their respective quantiles but bounded above by their respective larger quantiles. In this paper, we propose nonparametric estimators for DTVaR and establish their property of consistency. Moreover, we also propose the variability measure around this expected value truncated by the quantiles, called the Dependent Conditional Tail Variance (DCTV). We use this measure for constructing confidence intervals of the DTVaR. Both parametric and nonparametric approaches for DTVaR estimations are explored. Furthermore, we assess the performance of DTVaR estimations using a proposed backtest based on the DCTV. As for the numerical study, we take an application in the insurance claim amount.
abstract_unstemmed	Dependent Tail Value-at-Risk, abbreviated as DTVaR, is a copula-based extension of Tail Value-at-Risk (TVaR). This risk measure is an expectation of a target loss once the loss and its associated loss are above their respective quantiles but bounded above by their respective larger quantiles. In this paper, we propose nonparametric estimators for DTVaR and establish their property of consistency. Moreover, we also propose the variability measure around this expected value truncated by the quantiles, called the Dependent Conditional Tail Variance (DCTV). We use this measure for constructing confidence intervals of the DTVaR. Both parametric and nonparametric approaches for DTVaR estimations are explored. Furthermore, we assess the performance of DTVaR estimations using a proposed backtest based on the DCTV. As for the numerical study, we take an application in the insurance claim amount.
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title_short	Estimating copula-based extension of tail value-at-risk and its application in insurance claim
url	https://www.mdpi.com/2227-9091/10/6/113/pdf?version=1653904512 http://doi.org/10.3390/risks10060113
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Estimating copula-based extension of tail value-at-risk and its application in insurance claim

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