Forecasting volatility in gold returns under the GARCH, IGARCH and FIGARCH frameworks: new evidence

Detalhes bibliográficos
Autor(a) principal: Bentes, S. R.
Data de Publicação: 2015
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10071/11254
Resumo: This study employs three volatility models of the GARCH family to examine the volatility behavior of gold returns. Much of the literature on this topic suggests that gold plays a fundamental role as a hedge and safe haven against adverse market conditions, which is particularly relevant in periods of high volatility. This makes understanding gold volatility important for a number of theoretical and empirical applications, namely investment valuation, portfolio selection, risk management, monetary policy-making, futures and option pricing, hedging strategies and value-at-risk (VaR) policies (e.g. Baur and Lucey (2010)). We use daily data from August 2, 1976 to February 6, 2015 and divide the full sample into two periods: the in-sample period (August 2, 1976-October 24, 2008) is used to estimate model coefficients, while the out-of-sample period (October 27, 2008-February 6, 2015) is for forecasting purposes. Specifically, we employ the GARCH(1,1), IGARCH(1,1) and FIGARCH(1,d,1) specifications. The results show that the FIGARCH(1,d,1) is the best model to capture linear dependence in the conditional variance of the gold returns as given by the information criteria. It is also found to be the best model to forecast the volatility of gold returns.
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spelling Forecasting volatility in gold returns under the GARCH, IGARCH and FIGARCH frameworks: new evidenceGold returnsLong-memoryShock persistenceVolatility forecastsConditional varianceFIGARCHThis study employs three volatility models of the GARCH family to examine the volatility behavior of gold returns. Much of the literature on this topic suggests that gold plays a fundamental role as a hedge and safe haven against adverse market conditions, which is particularly relevant in periods of high volatility. This makes understanding gold volatility important for a number of theoretical and empirical applications, namely investment valuation, portfolio selection, risk management, monetary policy-making, futures and option pricing, hedging strategies and value-at-risk (VaR) policies (e.g. Baur and Lucey (2010)). We use daily data from August 2, 1976 to February 6, 2015 and divide the full sample into two periods: the in-sample period (August 2, 1976-October 24, 2008) is used to estimate model coefficients, while the out-of-sample period (October 27, 2008-February 6, 2015) is for forecasting purposes. Specifically, we employ the GARCH(1,1), IGARCH(1,1) and FIGARCH(1,d,1) specifications. The results show that the FIGARCH(1,d,1) is the best model to capture linear dependence in the conditional variance of the gold returns as given by the information criteria. It is also found to be the best model to forecast the volatility of gold returns.Elsevier2016-05-05T16:50:27Z2015-01-01T00:00:00Z20152019-05-13T15:55:25Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/11254eng0378-437110.1016/j.physa.2015.07.011Bentes, S. R.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:RCAAP2023-11-09T17:54:23Zoai:repositorio.iscte-iul.pt:10071/11254Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:27:24.402495Repositório 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 Forecasting volatility in gold returns under the GARCH, IGARCH and FIGARCH frameworks: new evidence
title Forecasting volatility in gold returns under the GARCH, IGARCH and FIGARCH frameworks: new evidence
spellingShingle Forecasting volatility in gold returns under the GARCH, IGARCH and FIGARCH frameworks: new evidence
Bentes, S. R.
Gold returns
Long-memory
Shock persistence
Volatility forecasts
Conditional variance
FIGARCH
title_short Forecasting volatility in gold returns under the GARCH, IGARCH and FIGARCH frameworks: new evidence
title_full Forecasting volatility in gold returns under the GARCH, IGARCH and FIGARCH frameworks: new evidence
title_fullStr Forecasting volatility in gold returns under the GARCH, IGARCH and FIGARCH frameworks: new evidence
title_full_unstemmed Forecasting volatility in gold returns under the GARCH, IGARCH and FIGARCH frameworks: new evidence
title_sort Forecasting volatility in gold returns under the GARCH, IGARCH and FIGARCH frameworks: new evidence
author Bentes, S. R.
author_facet Bentes, S. R.
author_role author
dc.contributor.author.fl_str_mv Bentes, S. R.
dc.subject.por.fl_str_mv Gold returns
Long-memory
Shock persistence
Volatility forecasts
Conditional variance
FIGARCH
topic Gold returns
Long-memory
Shock persistence
Volatility forecasts
Conditional variance
FIGARCH
description This study employs three volatility models of the GARCH family to examine the volatility behavior of gold returns. Much of the literature on this topic suggests that gold plays a fundamental role as a hedge and safe haven against adverse market conditions, which is particularly relevant in periods of high volatility. This makes understanding gold volatility important for a number of theoretical and empirical applications, namely investment valuation, portfolio selection, risk management, monetary policy-making, futures and option pricing, hedging strategies and value-at-risk (VaR) policies (e.g. Baur and Lucey (2010)). We use daily data from August 2, 1976 to February 6, 2015 and divide the full sample into two periods: the in-sample period (August 2, 1976-October 24, 2008) is used to estimate model coefficients, while the out-of-sample period (October 27, 2008-February 6, 2015) is for forecasting purposes. Specifically, we employ the GARCH(1,1), IGARCH(1,1) and FIGARCH(1,d,1) specifications. The results show that the FIGARCH(1,d,1) is the best model to capture linear dependence in the conditional variance of the gold returns as given by the information criteria. It is also found to be the best model to forecast the volatility of gold returns.
publishDate 2015
dc.date.none.fl_str_mv 2015-01-01T00:00:00Z
2015
2016-05-05T16:50:27Z
2019-05-13T15:55:25Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10071/11254
url http://hdl.handle.net/10071/11254
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0378-4371
10.1016/j.physa.2015.07.011
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dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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