Time-Varying Expected Returns, Conditional Skewness and Bitcoin Return Predictability

We employ a GARCH-type model to jointly estimate returns, conditional variance and skewness and show that conditional skewness outperforms sample skewness and conditional and sample variance in predicting future Bitcoin returns. Interestingly, the results show that the relationship between condition...

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Authors: Atance del Olmo, David|||0000-0001-5860-0584, Serna Calvo, Gregorio Manuel|||0000-0002-4106-7063
Format: article
Publication Date:2024
Country:España
Institution:Universidad de Alcalá (UAH)
Repository:e_Buah Biblioteca Digital Universidad de Alcalá
Language:English
OAI Identifier:oai:ebuah.uah.es:10017/63078
Online Access:http://hdl.handle.net/10017/63078
https://dx.doi.org/10.1016/j.qref.2024.101868
Access Level:Open access
Keyword:Bitcoin return predictions
GARCHS models
Conditional skewness
Sample skewness
G11
G14
G15
G17
Economía
Empresas
Economics
Management science
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spelling Time-Varying Expected Returns, Conditional Skewness and Bitcoin Return PredictabilityAtance del Olmo, David|||0000-0001-5860-0584Serna Calvo, Gregorio Manuel|||0000-0002-4106-7063Bitcoin return predictionsGARCHS modelsConditional skewnessSample skewnessG11G14G15G17EconomíaEmpresasEconomicsManagement scienceWe employ a GARCH-type model to jointly estimate returns, conditional variance and skewness and show that conditional skewness outperforms sample skewness and conditional and sample variance in predicting future Bitcoin returns. Interestingly, the results show that the relationship between conditional skewness and future Bitcoin returns is different depending on the sample period. In the first subsample (2018?2020), a period of relative calm in the Bitcoin market, the relationship is negative, which is in line with that found in the literature. However, in the second subsample (2021?2022), a period of major turmoil in the Bitcoin market, the relationship is positive, which is consistent with that found in previous papers on the relationship between conditional market skewness and future index returns during crisis periods. Based on these results, a dynamic buy and sell strategy of buying or selling Bitcoin based on the estimated conditional skewness is proposed. This dynamic strategy outperforms a static buy-and-hold strategy. The profitability of this strategy can be viewed as the reward that investors demand for bearing the risk associated with the changing conditions in the cryptocurrency market that generate time-varying expected returns20242024-05-28journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10017/63078https://dx.doi.org/10.1016/j.qref.2024.101868reponame:e_Buah Biblioteca Digital Universidad de Alcaláinstname:Universidad de Alcalá (UAH)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:ebuah.uah.es:10017/630782026-06-18T11:13:07Z
dc.title.none.fl_str_mv Time-Varying Expected Returns, Conditional Skewness and Bitcoin Return Predictability
title Time-Varying Expected Returns, Conditional Skewness and Bitcoin Return Predictability
spellingShingle Time-Varying Expected Returns, Conditional Skewness and Bitcoin Return Predictability
Atance del Olmo, David|||0000-0001-5860-0584
Bitcoin return predictions
GARCHS models
Conditional skewness
Sample skewness
G11
G14
G15
G17
Economía
Empresas
Economics
Management science
title_short Time-Varying Expected Returns, Conditional Skewness and Bitcoin Return Predictability
title_full Time-Varying Expected Returns, Conditional Skewness and Bitcoin Return Predictability
title_fullStr Time-Varying Expected Returns, Conditional Skewness and Bitcoin Return Predictability
title_full_unstemmed Time-Varying Expected Returns, Conditional Skewness and Bitcoin Return Predictability
title_sort Time-Varying Expected Returns, Conditional Skewness and Bitcoin Return Predictability
dc.creator.none.fl_str_mv Atance del Olmo, David|||0000-0001-5860-0584
Serna Calvo, Gregorio Manuel|||0000-0002-4106-7063
author Atance del Olmo, David|||0000-0001-5860-0584
author_facet Atance del Olmo, David|||0000-0001-5860-0584
Serna Calvo, Gregorio Manuel|||0000-0002-4106-7063
author_role author
author2 Serna Calvo, Gregorio Manuel|||0000-0002-4106-7063
author2_role author
dc.subject.none.fl_str_mv Bitcoin return predictions
GARCHS models
Conditional skewness
Sample skewness
G11
G14
G15
G17
Economía
Empresas
Economics
Management science
topic Bitcoin return predictions
GARCHS models
Conditional skewness
Sample skewness
G11
G14
G15
G17
Economía
Empresas
Economics
Management science
description We employ a GARCH-type model to jointly estimate returns, conditional variance and skewness and show that conditional skewness outperforms sample skewness and conditional and sample variance in predicting future Bitcoin returns. Interestingly, the results show that the relationship between conditional skewness and future Bitcoin returns is different depending on the sample period. In the first subsample (2018?2020), a period of relative calm in the Bitcoin market, the relationship is negative, which is in line with that found in the literature. However, in the second subsample (2021?2022), a period of major turmoil in the Bitcoin market, the relationship is positive, which is consistent with that found in previous papers on the relationship between conditional market skewness and future index returns during crisis periods. Based on these results, a dynamic buy and sell strategy of buying or selling Bitcoin based on the estimated conditional skewness is proposed. This dynamic strategy outperforms a static buy-and-hold strategy. The profitability of this strategy can be viewed as the reward that investors demand for bearing the risk associated with the changing conditions in the cryptocurrency market that generate time-varying expected returns
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-05-28
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10017/63078
https://dx.doi.org/10.1016/j.qref.2024.101868
url http://hdl.handle.net/10017/63078
https://dx.doi.org/10.1016/j.qref.2024.101868
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:e_Buah Biblioteca Digital Universidad de Alcalá
instname:Universidad de Alcalá (UAH)
instname_str Universidad de Alcalá (UAH)
reponame_str e_Buah Biblioteca Digital Universidad de Alcalá
collection e_Buah Biblioteca Digital Universidad de Alcalá
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