A comparative analysis of the predictive power of implied volatility indices and GARCH forecasted volatility
Autor(a) principal: | |
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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/10071/11256 |
Resumo: | This paper examines the accuracy of implied volatility and GARCH forecasted volatility to predict the behavior of realized volatility. The methodology adopted addresses the information content, the bias, the efficiency and the efficiency forecast of the predictor. In previous studies on this topic, efficiency has been analyzed both in terms of the efficiency of the predictor itself and its forecasting efficiency. In this context, implied volatility is the predictor and the efficiency is assessed through the validation of some of the OLS (Ordinary Least Squares) assumptions. However, those studies paid little attention to the heteroskedasticity of the residuals, even though this is an important source of inefficiency. Our study accounts for conditional heteroskedasticity by using a GARCH model to predict the time-dependent variance of the residuals. A GARCH forecasted volatility index was constructed based on these estimates. In addition, we employ out-of-sample forecasting accuracy tests in order to identify the best forecasting model. The results clearly show that GARCH forecasted volatility outperforms implied volatility to produce out-of-sample forecasts based on a subsample of the total sampling period for the four stock markets analyzed. |
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A comparative analysis of the predictive power of implied volatility indices and GARCH forecasted volatilityImplied volatilityGARCH forecasted volatilityInefficiencyOut-of-sample forecasting accuracyThis paper examines the accuracy of implied volatility and GARCH forecasted volatility to predict the behavior of realized volatility. The methodology adopted addresses the information content, the bias, the efficiency and the efficiency forecast of the predictor. In previous studies on this topic, efficiency has been analyzed both in terms of the efficiency of the predictor itself and its forecasting efficiency. In this context, implied volatility is the predictor and the efficiency is assessed through the validation of some of the OLS (Ordinary Least Squares) assumptions. However, those studies paid little attention to the heteroskedasticity of the residuals, even though this is an important source of inefficiency. Our study accounts for conditional heteroskedasticity by using a GARCH model to predict the time-dependent variance of the residuals. A GARCH forecasted volatility index was constructed based on these estimates. In addition, we employ out-of-sample forecasting accuracy tests in order to identify the best forecasting model. The results clearly show that GARCH forecasted volatility outperforms implied volatility to produce out-of-sample forecasts based on a subsample of the total sampling period for the four stock markets analyzed.Elsevier2016-05-05T17:26:40Z2015-01-01T00:00:00Z20152019-05-13T16:19:14Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/11256eng0378-437110.1016/j.physa.2015.01.020Bentes, 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:RCAAP2023-11-09T17:55:52Zoai:repositorio.iscte-iul.pt:10071/11256Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:28:33.840566Repositório 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 |
A comparative analysis of the predictive power of implied volatility indices and GARCH forecasted volatility |
title |
A comparative analysis of the predictive power of implied volatility indices and GARCH forecasted volatility |
spellingShingle |
A comparative analysis of the predictive power of implied volatility indices and GARCH forecasted volatility Bentes, S. R. Implied volatility GARCH forecasted volatility Inefficiency Out-of-sample forecasting accuracy |
title_short |
A comparative analysis of the predictive power of implied volatility indices and GARCH forecasted volatility |
title_full |
A comparative analysis of the predictive power of implied volatility indices and GARCH forecasted volatility |
title_fullStr |
A comparative analysis of the predictive power of implied volatility indices and GARCH forecasted volatility |
title_full_unstemmed |
A comparative analysis of the predictive power of implied volatility indices and GARCH forecasted volatility |
title_sort |
A comparative analysis of the predictive power of implied volatility indices and GARCH forecasted volatility |
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 |
Implied volatility GARCH forecasted volatility Inefficiency Out-of-sample forecasting accuracy |
topic |
Implied volatility GARCH forecasted volatility Inefficiency Out-of-sample forecasting accuracy |
description |
This paper examines the accuracy of implied volatility and GARCH forecasted volatility to predict the behavior of realized volatility. The methodology adopted addresses the information content, the bias, the efficiency and the efficiency forecast of the predictor. In previous studies on this topic, efficiency has been analyzed both in terms of the efficiency of the predictor itself and its forecasting efficiency. In this context, implied volatility is the predictor and the efficiency is assessed through the validation of some of the OLS (Ordinary Least Squares) assumptions. However, those studies paid little attention to the heteroskedasticity of the residuals, even though this is an important source of inefficiency. Our study accounts for conditional heteroskedasticity by using a GARCH model to predict the time-dependent variance of the residuals. A GARCH forecasted volatility index was constructed based on these estimates. In addition, we employ out-of-sample forecasting accuracy tests in order to identify the best forecasting model. The results clearly show that GARCH forecasted volatility outperforms implied volatility to produce out-of-sample forecasts based on a subsample of the total sampling period for the four stock markets analyzed. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-01-01T00:00:00Z 2015 2016-05-05T17:26:40Z 2019-05-13T16:19:14Z |
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 |
http://hdl.handle.net/10071/11256 |
url |
http://hdl.handle.net/10071/11256 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0378-4371 10.1016/j.physa.2015.01.020 |
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 |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
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 |
repository.mail.fl_str_mv |
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1799134848023003136 |