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Measuring quantile dependence and testing directional predictability between Bitcoin, altcoins and traditional financial assets

Corbet, Shaen orcid logoORCID: 0000-0001-7430-7417, Katsiampa, Paraskevi orcid logoORCID: 0000-0003-0477-6503 and Lau, Marco Chi Keung orcid logoORCID: 0000-0002-2430-5592 (2020) Measuring quantile dependence and testing directional predictability between Bitcoin, altcoins and traditional financial assets. International Review of Financial Analysis, 71 . ISSN 1057-5219

Abstract
This paper studies causal relationships and the potential of improving conditional quantile forecasting between Bitcoin and seven altcoin markets as well as between Bitcoin and three mainstream assets, namely gold, oil, and the S&P500, by applying the Granger-causality in distribution and in quantiles tests. We find significant bidirectional causality between Bitcoin and all altcoins and assets considered in the two distribution tails. An enhanced forecast of Bitcoin price returns is thus derived by conditioning on altcoins or assets and vice versa during extreme market conditions. However, under normal market conditions the results for the centre of the distribution of the Bitcoin price returns conditional on altcoins depend on both the altcoin considered and quantile under investigation. We also find evidence that Bitcoin is not isolated from financial markets, while this developing financial asset is a strong safe-haven for oil and a weak safe-haven for S&P500, but it cannot be considered as either a weak or strong safe-haven for gold. Our results reveal a more complete relationship between Bitcoin and altcoins as well as financial assets than was previously considered.
Metadata
Item Type:Article (Published)
Refereed:Yes
Additional Information:Article number:101571
Uncontrolled Keywords:Bitcoin; Cryptocurrency; Granger causality in distribution; Quantile dependence; Directional predictability; Cross-quantilogram
Subjects:Business > Economics
Business > Finance
DCU Faculties and Centres:DCU Faculties and Schools > DCU Business School
Publisher:Elsevier
Official URL:https://dx.doi.org/10.1016/j.irfa.2020.101571
Copyright Information:© 2020 The Authors. Open Access (CC-BY-4.0)
ID Code:25899
Deposited On:26 May 2021 13:53 by Thomas Murtagh . Last Modified 09 Jun 2021 15:35
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