A Survey on Empirical Findings about Spillovers in Cryptocurrency Markets

Abstract

This paper provides a systematic survey on return and volatility spillovers of cryptocurrencies based on the empirical results of relevant academic literature. Evidence reveals that Bitcoin is the most influential among digital coins mainly as a transmitter toward digital currencies but also as a receiver of spillovers from virtual currencies and alternative assets. Ethereum, Litecoin, and Ripple present the most significant interlinkages with Bitcoin. Return spillovers are more pronounced but volatility spillovers often present a bi-directional character. Volatility shock transmission is detected among Bitcoin and national currencies, while economic policy uncertainty is not influential. This survey provides useful guidance in the hotly-debated issue of reform and decentralization of financial systems.

When the blockchain does not block: on hackings and uncertainty in the cryptocurrency market

“…‡ See https://www.pwc.com/gx/en/financial-services/pdf/pwc-el wood-annual-crypto-hedge-fund-report-may-2020.pdf. § Other recent cryptocurrency-related surveys are provided Kyriazis (2019b) and Corbet et al (2019).…”

Portfolio diversification benefits of alternative currency investment in Bitcoin and foreign exchange markets

“…Meanwhile, Kyriazis (2019b) found Bitcoin to be the most significant transmitter and receiver of volatility spillover to and from other cryptocurrencies. The author found that volatility spillover transmission was present among Bitcoin and global national currencies.…”

“…Bitcoin has also been found to provide value-added diversification for portfolios of traditional assets (Platanakis and Urquhart 2019), as indicated by the presence of detached price behavior of Bitcoin from these traditional assets. Bitcoin price movement has also been found to affect national currency markets (Kyriazis 2019b). Thus, it is important to study the varying nature of volatility spillover and portfolio diversification benefits that alternative currency Bitcoin trading can offer investors.…”

Bitcoin Analysis and Forecasting Through Fuzzy Transform

“…F-transform is thus used to model Bitcoin as a function of GT100. The L 1 -norm and L 2 -norm based inverse iF-transforms of the data-set (GT100 t , BitCoin t ), t = 1, 2, ..., M are computed; taking into account the sparsity of the values GT100 t (in particular above the threshold 40), we use a non-uniform 1-partition (P, A) of the range [0, 100] of the observed GT100 t , namely the set of 25 nodes {0, 2,3,4,5,6,7,8,10,12,14,16,18,20,22,24,26,28,30,35,40,45,50, 60, 100}, as pictured in Figure 5. The two curves give the predominant relationship between BitCoin and GT100.…”

“…Many other authors approach general cryptocurrencies properties. For example, in [28] there is evidence that Bitcoin is the most influential among digital coins both as a transmitter toward digital currencies and as a receiver of spillovers from virtual and traditional instruments. An extended analysis is also presented in [29] where the four crypto-currencies Bitcoin, Ethereum, Ripple and Litecoin are predicted through a combination of eight models revealing that a combination of stochastic volatility and a student-t distribution gives the best results.…”