Tail Conditional Expectations Based on Kumaraswamy Dispersion Models
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/153671 |
Resumo: | Recently, there seems to be an increasing amount of interest in the use of the tail conditional expectation (TCE) as a useful measure of risk associated with a production process, for example, in the measurement of risk associated with stock returns corresponding to the manufacturing industry, such as the production of electric bulbs, investment in housing development, and financial institutions offering loans to small-scale industries. Companies typically face three types of risk (and associated losses from each of these sources): strategic (S); operational (O); and financial (F) (insurance companies additionally face insurance risks) and they come from multiple sources. For asymmetric and bounded losses (properly adjusted as necessary) that are continuous in nature, we conjecture that risk assessment measures via univariate/bivariate Kumaraswamy distribution will be efficient in the sense that the resulting TCE based on bivariate Kumaraswamy type copulas do not depend on the marginals. In fact, almost all classical measures of tail dependence are such, but they investigate the amount of tail dependence along the main diagonal of copulas, which has often little in common with the concentration of extremes in the copula’s domain of definition. In this article, we examined the above risk measure in the case of a univariate and bivariate Kumaraswamy (KW) portfolio risk, and computed TCE based on bivariate KW type copulas. For illustrative purposes, a well-known Stock indices data set was re-analyzed by computing TCE for the bivariate KW type copulas to determine which pairs produce minimum risk in a two-component risk scenario. |
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Tail Conditional Expectations Based on Kumaraswamy Dispersion ModelsAsymmetric lossesBivariate Kumaraswamy distributionBivariate Kumaraswamy type copulasBounded riskCopula-based tail conditional expectationTail conditional expectationsTail value-at-riskRecently, there seems to be an increasing amount of interest in the use of the tail conditional expectation (TCE) as a useful measure of risk associated with a production process, for example, in the measurement of risk associated with stock returns corresponding to the manufacturing industry, such as the production of electric bulbs, investment in housing development, and financial institutions offering loans to small-scale industries. Companies typically face three types of risk (and associated losses from each of these sources): strategic (S); operational (O); and financial (F) (insurance companies additionally face insurance risks) and they come from multiple sources. For asymmetric and bounded losses (properly adjusted as necessary) that are continuous in nature, we conjecture that risk assessment measures via univariate/bivariate Kumaraswamy distribution will be efficient in the sense that the resulting TCE based on bivariate Kumaraswamy type copulas do not depend on the marginals. In fact, almost all classical measures of tail dependence are such, but they investigate the amount of tail dependence along the main diagonal of copulas, which has often little in common with the concentration of extremes in the copula’s domain of definition. In this article, we examined the above risk measure in the case of a univariate and bivariate Kumaraswamy (KW) portfolio risk, and computed TCE based on bivariate KW type copulas. For illustrative purposes, a well-known Stock indices data set was re-analyzed by computing TCE for the bivariate KW type copulas to determine which pairs produce minimum risk in a two-component risk scenario.DM - Departamento de MatemáticaCMA - Centro de Matemática e AplicaçõesRUNGhosh, I.Marques, Filipe J.2023-06-06T22:22:14Z2021-07-242021-07-24T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article17application/pdfhttp://hdl.handle.net/10362/153671eng2227-7390PURE: 62899051https://doi.org/10.3390/math9131478info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:36:14Zoai:run.unl.pt:10362/153671Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:55:22.112576Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Tail Conditional Expectations Based on Kumaraswamy Dispersion Models |
title |
Tail Conditional Expectations Based on Kumaraswamy Dispersion Models |
spellingShingle |
Tail Conditional Expectations Based on Kumaraswamy Dispersion Models Ghosh, I. Asymmetric losses Bivariate Kumaraswamy distribution Bivariate Kumaraswamy type copulas Bounded risk Copula-based tail conditional expectation Tail conditional expectations Tail value-at-risk |
title_short |
Tail Conditional Expectations Based on Kumaraswamy Dispersion Models |
title_full |
Tail Conditional Expectations Based on Kumaraswamy Dispersion Models |
title_fullStr |
Tail Conditional Expectations Based on Kumaraswamy Dispersion Models |
title_full_unstemmed |
Tail Conditional Expectations Based on Kumaraswamy Dispersion Models |
title_sort |
Tail Conditional Expectations Based on Kumaraswamy Dispersion Models |
author |
Ghosh, I. |
author_facet |
Ghosh, I. Marques, Filipe J. |
author_role |
author |
author2 |
Marques, Filipe J. |
author2_role |
author |
dc.contributor.none.fl_str_mv |
DM - Departamento de Matemática CMA - Centro de Matemática e Aplicações RUN |
dc.contributor.author.fl_str_mv |
Ghosh, I. Marques, Filipe J. |
dc.subject.por.fl_str_mv |
Asymmetric losses Bivariate Kumaraswamy distribution Bivariate Kumaraswamy type copulas Bounded risk Copula-based tail conditional expectation Tail conditional expectations Tail value-at-risk |
topic |
Asymmetric losses Bivariate Kumaraswamy distribution Bivariate Kumaraswamy type copulas Bounded risk Copula-based tail conditional expectation Tail conditional expectations Tail value-at-risk |
description |
Recently, there seems to be an increasing amount of interest in the use of the tail conditional expectation (TCE) as a useful measure of risk associated with a production process, for example, in the measurement of risk associated with stock returns corresponding to the manufacturing industry, such as the production of electric bulbs, investment in housing development, and financial institutions offering loans to small-scale industries. Companies typically face three types of risk (and associated losses from each of these sources): strategic (S); operational (O); and financial (F) (insurance companies additionally face insurance risks) and they come from multiple sources. For asymmetric and bounded losses (properly adjusted as necessary) that are continuous in nature, we conjecture that risk assessment measures via univariate/bivariate Kumaraswamy distribution will be efficient in the sense that the resulting TCE based on bivariate Kumaraswamy type copulas do not depend on the marginals. In fact, almost all classical measures of tail dependence are such, but they investigate the amount of tail dependence along the main diagonal of copulas, which has often little in common with the concentration of extremes in the copula’s domain of definition. In this article, we examined the above risk measure in the case of a univariate and bivariate Kumaraswamy (KW) portfolio risk, and computed TCE based on bivariate KW type copulas. For illustrative purposes, a well-known Stock indices data set was re-analyzed by computing TCE for the bivariate KW type copulas to determine which pairs produce minimum risk in a two-component risk scenario. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-07-24 2021-07-24T00:00:00Z 2023-06-06T22:22:14Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/153671 |
url |
http://hdl.handle.net/10362/153671 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2227-7390 PURE: 62899051 https://doi.org/10.3390/math9131478 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
17 application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799138140869361664 |