Predicting the financial crisis volatility

Detalhes bibliográficos
Autor(a) principal: Curto, J.
Data de Publicação: 2012
Outros Autores: Pinto, J.
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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spelling 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
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language eng
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