A comparative analysis of the predictive power of implied volatility indices and GARCH forecasted volatility

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/10071/11256
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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spelling 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
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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
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dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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