Time-frequency forecast of the equity premium

Bibliographic Details
Main Author: Faria, Gonçalo
Publication Date: 2021
Other Authors: Verona, Fabio
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10400.14/31675
Summary: Any time series can be decomposed into cyclical components fluctuating at different frequencies. Accordingly, in this paper, we propose a method to forecast the equity premium which exploits the frequency relationship between the equity premium and several predictor variables. We evaluate a large set of models and find that, by selecting the relevant frequencies for equity premium forecasting purposes, this method significantly improves in a statistical and economic way upon standard time series forecasting methods. This outperformance is robust regardless of the predictor used, the out-of-sample period considered, and the frequency of the data used.
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spelling Time-frequency forecast of the equity premiumTime-frequency forecastEquity premiumMultiresolution analysisAny time series can be decomposed into cyclical components fluctuating at different frequencies. Accordingly, in this paper, we propose a method to forecast the equity premium which exploits the frequency relationship between the equity premium and several predictor variables. We evaluate a large set of models and find that, by selecting the relevant frequencies for equity premium forecasting purposes, this method significantly improves in a statistical and economic way upon standard time series forecasting methods. This outperformance is robust regardless of the predictor used, the out-of-sample period considered, and the frequency of the data used.RoutledgeVeritatiFaria, GonçaloVerona, Fabio2023-06-01T00:30:32Z20212021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.14/31675eng1469-768810.1080/14697688.2020.1820071info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-03-13T13:48:26Zoai:repositorio.ucp.pt:10400.14/31675Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-29T01:59:38.659700Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Time-frequency forecast of the equity premium
title Time-frequency forecast of the equity premium
spellingShingle Time-frequency forecast of the equity premium
Faria, Gonçalo
Time-frequency forecast
Equity premium
Multiresolution analysis
title_short Time-frequency forecast of the equity premium
title_full Time-frequency forecast of the equity premium
title_fullStr Time-frequency forecast of the equity premium
title_full_unstemmed Time-frequency forecast of the equity premium
title_sort Time-frequency forecast of the equity premium
author Faria, Gonçalo
author_facet Faria, Gonçalo
Verona, Fabio
author_role author
author2 Verona, Fabio
author2_role author
dc.contributor.none.fl_str_mv Veritati
dc.contributor.author.fl_str_mv Faria, Gonçalo
Verona, Fabio
dc.subject.por.fl_str_mv Time-frequency forecast
Equity premium
Multiresolution analysis
topic Time-frequency forecast
Equity premium
Multiresolution analysis
description Any time series can be decomposed into cyclical components fluctuating at different frequencies. Accordingly, in this paper, we propose a method to forecast the equity premium which exploits the frequency relationship between the equity premium and several predictor variables. We evaluate a large set of models and find that, by selecting the relevant frequencies for equity premium forecasting purposes, this method significantly improves in a statistical and economic way upon standard time series forecasting methods. This outperformance is robust regardless of the predictor used, the out-of-sample period considered, and the frequency of the data used.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-01-01T00:00:00Z
2023-06-01T00:30:32Z
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/10400.14/31675
url http://hdl.handle.net/10400.14/31675
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1469-7688
10.1080/14697688.2020.1820071
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Routledge
publisher.none.fl_str_mv Routledge
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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