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dc.contributor.authorNguyen, Linh H.
dc.contributor.authorChevapatrakul, Thanaset
dc.contributor.authorYao, Kai
dc.date.accessioned2020-07-21T11:00:09Z
dc.date.available2020-07-21T11:00:09Z
dc.date.issued2020-07-03
dc.identifier.citationNguyen, L.H., Chevapatrakul, T., Yao, K. (2020) Investigating tail-risk dependence in the cryptocurrency markets: A LASSO quantile regression approach. Journal of Empirical Finance, 58, pp.333-355.en
dc.identifier.issn0927-5398
dc.identifier.urihttps://dora.dmu.ac.uk/handle/2086/20006
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
dc.description.abstractWe construct the complete network of tail risk spillovers among major cryptocurrencies using the Least Absolute Shrinkage and Selection Operator (LASSO) quantile regression. We capture important features of the network, including major risk-driving and major risk-receiving currencies, and the evolution of the tail dependence among the currencies over time. Importantly, we reveal a striking finding that the right tail dependence among the cryptocurrencies is significantly stronger than the left tail counterpart. This unique characteristic may have contributed to the rise in popularity of cryptocurrencies over the last few years. Our portfolio analysis reveals that diversification in cryptocurrency investment can be accomplished simply by employing the naïve equal-weighted scheme even when transaction costs are taken into account.en
dc.language.isoenen
dc.publisherElsevieren
dc.subjectTail risken
dc.subjectSpilloveren
dc.subjectCryptocurrencyen
dc.subjectNetworken
dc.titleInvestigating tail-risk dependence in the cryptocurrency markets: A LASSO quantile regression approachen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1016/j.jempfin.2020.06.006
dc.peerreviewedYesen
dc.funderNo external funderen
dc.cclicenceCC BYen
dc.date.acceptance2020-06-29
dc.researchinstituteFinance and Banking Research Group (FiBRe)en


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