Volatility modeling based on GARCH-skewed-t-type models for Chinese stock market

Detalhes bibliográficos
Autor(a) principal: Fei Lin
Data de Publicação: 2019
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10071/18940
Resumo: As an emerging stock market with enormous potential, Chinese stock market has apparent volatility clustering appearance along with typical feature of leptokurtic, negative skewness and fat tail in its index yield series. The model based on traditional normal distribution often underestimate the risk, which would lead to profound loss for the investors and financial institution when the extreme events happened. VaR(Value at Risk), which measures risk as a certain value, is widely used in financial industry for its intuitive and concise characteristics. Since parameter method of the VaR calculation is the mostly implementation in practice, the choice of appropriate probability distribution function and variance can quite improve its accuracy. Therefore, the conditional variance is estimated by GARCH-type models and the assumption of normal distribution is replaced by skewed-t distribution. Compared with the common RiskMetrics based on normal distribution, the ARMA-GJR-GARCH-skewed-t model has better adaptability and precision for the VaR estimation of indices of Chinese stock markets.
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spelling Volatility modeling based on GARCH-skewed-t-type models for Chinese stock marketValue at RiskVolatilityGARCHMercado de açõesRisco financeiroModelos VARVolatilidadeModelos GARCHChinaAs an emerging stock market with enormous potential, Chinese stock market has apparent volatility clustering appearance along with typical feature of leptokurtic, negative skewness and fat tail in its index yield series. The model based on traditional normal distribution often underestimate the risk, which would lead to profound loss for the investors and financial institution when the extreme events happened. VaR(Value at Risk), which measures risk as a certain value, is widely used in financial industry for its intuitive and concise characteristics. Since parameter method of the VaR calculation is the mostly implementation in practice, the choice of appropriate probability distribution function and variance can quite improve its accuracy. Therefore, the conditional variance is estimated by GARCH-type models and the assumption of normal distribution is replaced by skewed-t distribution. Compared with the common RiskMetrics based on normal distribution, the ARMA-GJR-GARCH-skewed-t model has better adaptability and precision for the VaR estimation of indices of Chinese stock markets.Como um mercado emergente de ações com enorme potencial, o mercado acionário chinês tem uma aparente aparência de agregação de volatilidade, juntamente com uma característica típica de leptocurtice, assimetria negativa e cauda gorda em sua série de índices de rendimento. O modelo baseado na distribuição normal tradicional freqüentemente subestima o risco, o que levaria a perdas profundas para os investidores e instituições financeiras quando os eventos extremos acontecessem. O VaR (Value at Risk), que mede o risco como um determinado valor, é amplamente utilizado no setor financeiro por suas características intuitivas e concisas. Como o método de parâmetro do cálculo do VaR é a maior parte da implementação na prática, a escolha da função de distribuição de probabilidade apropriada e da variância pode melhorar bastante sua precisão. Portanto, a variância condicional é estimada pelo modelo GARCH-types e a suposição de distribuição normal é substituída pela distribuição skewed-t. Comparado com o comum RiskMetrics baseado na distribuição normal e outros modelos do tipo GARCHskewed- t, o modelo ARMA-GJR-GARCH-skewed-t tem melhor adaptabilidade e precisão para a estimativa de VaR de índices dos mercados de ações chineses.2019-12-05T12:25:38Z2019-09-30T00:00:00Z2019-09-302019-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfapplication/octet-streamhttp://hdl.handle.net/10071/18940TID:202295052engFei Lininfo: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:RCAAP2023-11-09T17:42:42Zoai:repositorio.iscte-iul.pt:10071/18940Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:20:01.607087Repositório 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 Volatility modeling based on GARCH-skewed-t-type models for Chinese stock market
title Volatility modeling based on GARCH-skewed-t-type models for Chinese stock market
spellingShingle Volatility modeling based on GARCH-skewed-t-type models for Chinese stock market
Fei Lin
Value at Risk
Volatility
GARCH
Mercado de ações
Risco financeiro
Modelos VAR
Volatilidade
Modelos GARCH
China
title_short Volatility modeling based on GARCH-skewed-t-type models for Chinese stock market
title_full Volatility modeling based on GARCH-skewed-t-type models for Chinese stock market
title_fullStr Volatility modeling based on GARCH-skewed-t-type models for Chinese stock market
title_full_unstemmed Volatility modeling based on GARCH-skewed-t-type models for Chinese stock market
title_sort Volatility modeling based on GARCH-skewed-t-type models for Chinese stock market
author Fei Lin
author_facet Fei Lin
author_role author
dc.contributor.author.fl_str_mv Fei Lin
dc.subject.por.fl_str_mv Value at Risk
Volatility
GARCH
Mercado de ações
Risco financeiro
Modelos VAR
Volatilidade
Modelos GARCH
China
topic Value at Risk
Volatility
GARCH
Mercado de ações
Risco financeiro
Modelos VAR
Volatilidade
Modelos GARCH
China
description As an emerging stock market with enormous potential, Chinese stock market has apparent volatility clustering appearance along with typical feature of leptokurtic, negative skewness and fat tail in its index yield series. The model based on traditional normal distribution often underestimate the risk, which would lead to profound loss for the investors and financial institution when the extreme events happened. VaR(Value at Risk), which measures risk as a certain value, is widely used in financial industry for its intuitive and concise characteristics. Since parameter method of the VaR calculation is the mostly implementation in practice, the choice of appropriate probability distribution function and variance can quite improve its accuracy. Therefore, the conditional variance is estimated by GARCH-type models and the assumption of normal distribution is replaced by skewed-t distribution. Compared with the common RiskMetrics based on normal distribution, the ARMA-GJR-GARCH-skewed-t model has better adaptability and precision for the VaR estimation of indices of Chinese stock markets.
publishDate 2019
dc.date.none.fl_str_mv 2019-12-05T12:25:38Z
2019-09-30T00:00:00Z
2019-09-30
2019-01
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10071/18940
TID:202295052
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identifier_str_mv TID:202295052
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instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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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
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