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Determinants of cryptocurrency returns: A LASSO quantile regression approach

Determinants of cryptocurrency returns: A LASSO quantile regression approach
Determinants of cryptocurrency returns: A LASSO quantile regression approach
We consider a relatively large set of predictors and investigate the determinants of cryptocurrency returns at different quantiles. Our analysis exclusively focuses on the highly volatile period of COVID-19. The innovation in the paper stems from the fact that we employ the LASSO penalty in a quantile regression framework to select informative variables. We find that US government bond indices and small company stock returns, a new predictor introduce in this study, significantly impact the tail behavior of the cryptocurrency returns.
LASSO, quantile regression, Cryptocurrency, covid-19
1544-6123
Ciner, Cetin
cf7b4a40-8c6b-4105-ab75-cfd1547e79c0
Lucey, Brian
ea62416d-8886-4acd-b160-f653aea6c319
Yarovaya, Larisa
2bd189e8-3bad-48b0-9d09-5d96a4132889
Ciner, Cetin
cf7b4a40-8c6b-4105-ab75-cfd1547e79c0
Lucey, Brian
ea62416d-8886-4acd-b160-f653aea6c319
Yarovaya, Larisa
2bd189e8-3bad-48b0-9d09-5d96a4132889

Ciner, Cetin, Lucey, Brian and Yarovaya, Larisa (2022) Determinants of cryptocurrency returns: A LASSO quantile regression approach. Finance Research Letters, 49, [102990].

Record type: Article

Abstract

We consider a relatively large set of predictors and investigate the determinants of cryptocurrency returns at different quantiles. Our analysis exclusively focuses on the highly volatile period of COVID-19. The innovation in the paper stems from the fact that we employ the LASSO penalty in a quantile regression framework to select informative variables. We find that US government bond indices and small company stock returns, a new predictor introduce in this study, significantly impact the tail behavior of the cryptocurrency returns.

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Ciner_et_al.FRL.QuantileLasso (1) - Accepted Manuscript
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More information

Accepted/In Press date: 17 May 2022
e-pub ahead of print date: 19 May 2022
Published date: 8 July 2022
Keywords: LASSO, quantile regression, Cryptocurrency, covid-19

Identifiers

Local EPrints ID: 470631
URI: http://eprints.soton.ac.uk/id/eprint/470631
ISSN: 1544-6123
PURE UUID: 95e1299c-c366-47b4-ba32-4753ba8d4e6d
ORCID for Larisa Yarovaya: ORCID iD orcid.org/0000-0002-9638-2917

Catalogue record

Date deposited: 17 Oct 2022 16:31
Last modified: 18 Oct 2022 01:55

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Contributors

Author: Cetin Ciner
Author: Brian Lucey
Author: Larisa Yarovaya ORCID iD

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