Publicação
Predicting the financial crisis volatility
| 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. |
|---|---|
| Autores principais: | Curto, J. |
| Outros Autores: | Pinto, J. |
| Assunto: | Forecasting volatility EGARCH APARCH GJR |
| Ano: | 2012 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso embargado |
| Instituição associada: | ISCTE |
| Idioma: | inglês |
| Origem: | Repositório ISCTE |
| _version_ | 1868443704895733760 |
|---|---|
| author | Curto, J. |
| author2 | Pinto, J. |
| author2_role | author |
| author_facet | Curto, J. Pinto, J. |
| author_role | author |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Curto, J.\"},{\"Person.name\":\"Pinto, J.\"}] |
| datacite.creators.creator.creatorName.fl_str_mv | Curto, J. Pinto, J. |
| datacite.date.Accepted.fl_str_mv | 2012-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2015-12-09T16:09:22Z |
| datacite.date.embargoed.fl_str_mv | 2015-12-09T16:09:22Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_f1cf |
| datacite.subjects.subject.fl_str_mv | Forecasting volatility EGARCH APARCH GJR |
| datacite.titles.title.fl_str_mv | Predicting the financial crisis volatility |
| dc.creator.none.fl_str_mv | Curto, J. Pinto, J. |
| dc.date.Accepted.fl_str_mv | 2012-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2015-12-09T16:09:22Z |
| dc.date.embargoed.fl_str_mv | 2015-12-09T16:09:22Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | https://ciencia.iscte-iul.pt/public/pub/id/6335 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Editura Academia de studii economice |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_f1cf |
| dc.subject.none.fl_str_mv | Forecasting volatility EGARCH APARCH GJR |
| dc.title.fl_str_mv | Predicting the financial crisis volatility |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| 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. |
| dirty | 0 |
| eu_rights_str_mv | embargoedAccess |
| format | article |
| id | iscte_63a100b41dcc7eef4dbab5dcd48dee98 |
| identifier.url.fl_str_mv | https://ciencia.iscte-iul.pt/public/pub/id/6335 |
| instacron_str | iscte |
| institution | ISCTE |
| instname_str | ISCTE |
| language | eng |
| network_acronym_str | iscte |
| network_name_str | Repositório ISCTE |
| oai_identifier_str | oai:repositorio.iscte-iul.pt:10071/10331 |
| organization_str_mv | urn:organizationAcronym:iscte |
| person_str_mv | Curto, J. Pinto, J. |
| publishDate | 2012 |
| publisher.none.fl_str_mv | Editura Academia de studii economice |
| reponame_str | Repositório ISCTE |
| repository_id_str | urn:repositoryAcronym:iscte |
| service_str_mv | urn:repositoryAcronym:iscte |
| spelling | porA 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.application/pdfengEditura Academia de studii economiceporPredicting the financial crisis volatilityCurto, J.Pinto, J.URLhttps://ciencia.iscte-iul.pt/public/pub/id/6335ISSNIsPartOf0424-267XDOIhttp://hdl.handle.net/10071/103312015-12-09T16:09:22Z2012-01-01T00:00:00Z20122015-12-09T16:08:09Zhttp://purl.org/coar/access_right/c_f1cfembargoed accessporForecasting volatilityporEGARCHporAPARCHporGJR8019475 byteshttp://purl.org/coar/access_right/c_f1cfapplication/pdffulltexthttps://repositorio.iscte-iul.pt/bitstreams/11006e8e-d203-4713-ba42-a4674321399d/downloadliteraturehttp://purl.org/coar/resource_type/c_6501journal article |
| spellingShingle | Predicting the financial crisis volatility Curto, J. Forecasting volatility EGARCH APARCH GJR |
| status | SINGLETON |
| subject.fl_str_mv | Forecasting volatility EGARCH APARCH GJR |
| title | 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_short | Predicting the financial crisis volatility |
| title_sort | Predicting the financial crisis volatility |
| topic | Forecasting volatility EGARCH APARCH GJR |
| topic_facet | Forecasting volatility EGARCH APARCH GJR |
| url | https://ciencia.iscte-iul.pt/public/pub/id/6335 |
| visible | 1 |