Investigating tail-risk dependence in the cryptocurrency markets: A LASSO quantile regression approach

Linh Hoang Nguyen, Thanaset Chevapatrakul and Kai Yao

Journal of Empirical Finance, 2020, vol. 58, issue C, 333-355

Abstract: We 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.

Keywords: Tail risk; Spillovers; Cryptocurrency; Network (search for similar items in EconPapers)
JEL-codes: C20 C51 C53 G11 G12 G17 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (38)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0927539820300372
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:58:y:2020:i:c:p:333-355

DOI: 10.1016/j.jempfin.2020.06.006

Access Statistics for this article

Journal of Empirical Finance is currently edited by R. T. Baillie, F. C. Palm, Th. J. Vermaelen and C. C. P. Wolff

More articles in Journal of Empirical Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().