Determinants of cryptocurrency returns: A LASSO quantile regression approach

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dc.contributor.authorCiner, Cetin
dc.contributor.authorLucey, Brian
dc.contributor.authorYarovaya, Larisa
dc.date.accessioned2024-06-06T09:11:18Z
dc.date.available2024-06-06T09:11:18Z
dc.date.issued2022-10
dc.descriptionPanagiotidis et al. (2018) use a data set with many predictors from stock, commodity, bond, and exchange rate markets to investigate the determinants of bitcoin (BTC).
dc.description.abstractWe 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. Keywords: COVID-19, Cryptocurrency; LLASSO, Quantile regression
dc.identifier.citationCiner, C., Lucey, B., & Yarovaya, L. (2022). Determinants of cryptocurrency returns: A Lasso quantile regression approach. Finance Research Letters, 49, 102990.
dc.identifier.doihttps://doi.org/10.1016/j.frl.2022.102990
dc.identifier.urihttps://dspace.adu.ac.ae/handle/1/5726
dc.language.isoen
dc.publisherElsevier
dc.titleDeterminants of cryptocurrency returns: A LASSO quantile regression approach
dc.typeArticle

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