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

Nguyen, Linh Hoang; Chevapatrakul, Thanaset; Yao, Kai

Abstract

© 2020 Elsevier B.V. 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.

Citation

Nguyen, 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, 333-355. https://doi.org/10.1016/j.jempfin.2020.06.006

• Journal Article Type: Article
• Acceptance Date: Jun 29, 2020
• Online Publication Date: Jul 3, 2020
• Publication Date: Sep 1, 2020
• Deposit Date: Jun 30, 2020
• Publicly Available Date: Jan 4, 2022
• Journal: Journal of Empirical Finance
• Print ISSN: 0927-5398
• Electronic ISSN: 1879-1727
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 58
• Pages: 333-355
• DOI: https://doi.org/10.1016/j.jempfin.2020.06.006
• Keywords: Tail risk; Spillovers; Cryptocurrency; Network
• Public URL: https://nottingham-repository.worktribe.com/output/4739517
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0927539820300372
• Additional Information: This article is maintained by: Elsevier; Article Title: Investigating tail-risk dependence in the cryptocurrency markets: A LASSO quantile regression approach; Journal Title: Journal of Empirical Finance; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.jempfin.2020.06.006; Content Type: article; Copyright: © 2020 Elsevier B.V. All rights reserved.