Predicting the financial crisis volatility
Autor(a) principal: | |
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Data de Publicação: | 2012 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://ciencia.iscte-iul.pt/public/pub/id/6335 http://hdl.handle.net/10071/10331 |
Resumo: | A volatility model must be able to forecast volatility even in extreme situations. Thus, the main objective of this paper, and due to the most recent increase in international stock markets' volatility, is to check which one of the most popular autoregressive conditional heteroskedasticity models (GARCH, GJR, EGARCH or APARCH) is more able to predict the extreme volatility in 2008 considering the daily returns of eight major international stock market indexes: CAC 40 (France), DAX 30 (Germany), FTSE 100 (UK), NIKKEI 225 (Japan), HANG SENG (Hong Kong), NASDAQ 100, DJIA and S&P 500 (United States). Goodness-of-fit measures demonstrate that EGARCH and APARCH models are able to correctly fit the conditional heteroskedasticity dynamics of the return's series under study. In terms of volatility forecast comparisons, using the Harvey-Newbold test for multiple forecasts encompassing and the ranking of forecasts based on the coefficient of determination (R-2) resulting from the Mincer-Zarnowitz regression, we conclude that EGARCH dominates competing standard asymmetric models. |
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Predicting the financial crisis volatilityForecasting volatilityEGARCHAPARCHGJRA volatility model must be able to forecast volatility even in extreme situations. Thus, the main objective of this paper, and due to the most recent increase in international stock markets' volatility, is to check which one of the most popular autoregressive conditional heteroskedasticity models (GARCH, GJR, EGARCH or APARCH) is more able to predict the extreme volatility in 2008 considering the daily returns of eight major international stock market indexes: CAC 40 (France), DAX 30 (Germany), FTSE 100 (UK), NIKKEI 225 (Japan), HANG SENG (Hong Kong), NASDAQ 100, DJIA and S&P 500 (United States). Goodness-of-fit measures demonstrate that EGARCH and APARCH models are able to correctly fit the conditional heteroskedasticity dynamics of the return's series under study. In terms of volatility forecast comparisons, using the Harvey-Newbold test for multiple forecasts encompassing and the ranking of forecasts based on the coefficient of determination (R-2) resulting from the Mincer-Zarnowitz regression, we conclude that EGARCH dominates competing standard asymmetric models.Editura Academia de studii economice2015-12-09T16:09:22Z2012-01-01T00:00:00Z20122015-12-09T16:08:09Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://ciencia.iscte-iul.pt/public/pub/id/6335http://hdl.handle.net/10071/10331eng0424-267XCurto, J.Pinto, J.info:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-09T17:53:07Zoai:repositorio.iscte-iul.pt:10071/10331Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:26:33.790576Repositório 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 |
Predicting the financial crisis volatility |
title |
Predicting the financial crisis volatility |
spellingShingle |
Predicting the financial crisis volatility Curto, J. Forecasting volatility EGARCH APARCH GJR |
title_short |
Predicting the financial crisis volatility |
title_full |
Predicting the financial crisis volatility |
title_fullStr |
Predicting the financial crisis volatility |
title_full_unstemmed |
Predicting the financial crisis volatility |
title_sort |
Predicting the financial crisis volatility |
author |
Curto, J. |
author_facet |
Curto, J. Pinto, J. |
author_role |
author |
author2 |
Pinto, J. |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Curto, J. Pinto, J. |
dc.subject.por.fl_str_mv |
Forecasting volatility EGARCH APARCH GJR |
topic |
Forecasting volatility EGARCH APARCH GJR |
description |
A volatility model must be able to forecast volatility even in extreme situations. Thus, the main objective of this paper, and due to the most recent increase in international stock markets' volatility, is to check which one of the most popular autoregressive conditional heteroskedasticity models (GARCH, GJR, EGARCH or APARCH) is more able to predict the extreme volatility in 2008 considering the daily returns of eight major international stock market indexes: CAC 40 (France), DAX 30 (Germany), FTSE 100 (UK), NIKKEI 225 (Japan), HANG SENG (Hong Kong), NASDAQ 100, DJIA and S&P 500 (United States). Goodness-of-fit measures demonstrate that EGARCH and APARCH models are able to correctly fit the conditional heteroskedasticity dynamics of the return's series under study. In terms of volatility forecast comparisons, using the Harvey-Newbold test for multiple forecasts encompassing and the ranking of forecasts based on the coefficient of determination (R-2) resulting from the Mincer-Zarnowitz regression, we conclude that EGARCH dominates competing standard asymmetric models. |
publishDate |
2012 |
dc.date.none.fl_str_mv |
2012-01-01T00:00:00Z 2012 2015-12-09T16:09:22Z 2015-12-09T16:08:09Z |
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 |
https://ciencia.iscte-iul.pt/public/pub/id/6335 http://hdl.handle.net/10071/10331 |
url |
https://ciencia.iscte-iul.pt/public/pub/id/6335 http://hdl.handle.net/10071/10331 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0424-267X |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/embargoedAccess |
eu_rights_str_mv |
embargoedAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Editura Academia de studii economice |
publisher.none.fl_str_mv |
Editura Academia de studii economice |
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 |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
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 |
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1799134828709281792 |