# Signatures of the Crypto-Currency Market Decoupling from the Forex
## 7 Summary  
As reported in a recent study [48], in spite of its virtual nature and novelty, the Bitcoin (BTC) market over the years 2016-2017 developed the statistical hallmarks which are empirically observed in all'mature' markets like stocks, commodities or the Forex. The present study not only confirms this observation for the year 2018 but also documents that it can be extended to another crypto-currency, the Ethereum (ETH), which, in terms of the capitalization involved can be considered the second most important one among the hundreds of crypto-currencies that nowadays circulate and are traded.  
Indeed, the fluctuations of the ETH exchange rates, with respect to both the EUR and the USD, to a good approximation obey the inverse cubic power-law. The autocorrelation functions for the cryptocurrencies have developed dynamics which resemble the EUR/USD rate just one decade ago. Moreover, their Hurst exponent has been systematically increasing and approached the'mature' value of 0.5 in early 2018. There is, furthermore, even evidence of a well-developed multifractality. Recently, the directly-traded exchange rates between the Bitcoin and Ethereum (BTC/ETH) appear to obey analogous fluctuation characteristics, with the multifractal spectrum being even broader and more symmetric compared to those of BTC or ETH in relation to fiat currencies like EUR and USD. There are also significant cross-correlations between the considered crypto/fiat exchange rates, similar to those observed in the mature financial markets [50]. What, however, appears particularly thought-provoking is that, meanwhile, the cross-correlations between the BTC/ETH and the EUR/USD exchange rates entirely disappear.  
Altogether, these facts, and especially the last one, provide strong support for the hypothesis [48] of the gradual emergence of a new and at least partially independent market, analogous to the global foreign exchange (Forex) market, wherein cryptocurrencies are traded in a self-contained manner  
Figure 11: (Color online) Family of the \(q\)th-order fluctuation cross-covariance functions \(F_{XY}(q,s)\), for different values of \(q\) in steps of 0.2 The upper-most lines correspond to \(q=4\), whereas the bottom ones to those for which \(F_{XY}(q,s)\) are still positive (upper panel) and the \(\rho_{q}(s)\) coefficients as functions of the temporal scale \(s\) for \(q=1\) and \(q=4\), calculated for the cross-correlation between BTC/ETH and EUR/USD exchange rates (lower panel).  
In more practical terms, this means that not only the Bitcoin but even the whole emerging crypto-market may, eventually, offer 'a hedge or safe haven' for currencies [59], gold and commodities [60].  
Analysis of the cross-correlations between cryptocurrencies also reveals time-scale and fluctuation-size dependence, which could be relevant in the design of trading strategies and portfolio management. In the present study, the cross-correlations involving only two highest-capitalization cryptocurrencies are systematically explored on a fully quantitative level. Future work should explicitly address the larger set of most liquid cryptocurrencies, also relevant to triangular arbitrage. Despite already featuring statistical characteristics close to the mature markets, the cryptocurrency market is still continuously evolving its nature; hence, future work should also address other periods and time-scales of its development.  
Conceptualization, S.D.; L.M.; P.O. and M.S.; methodology, S.D.; L.M.; P.O.; M.S. and M.W. software, P.O. and M.W.; validation, S.D.; L.M.; P.O.; M.S.; formal analysis, P.O. and M.W.; investigation, S.D.; L.M.; P.O.; M.S. and M.W.; data curation, MW; writing--original draft preparation, S.D.; L.M.; P.O.; M.S. and M.W.; writing--review and editing, S.D.; L.M.; P.O.; M.S. and M.W.; supervision, S.D.  
This research received no external funding.  
The authors declare no conflict of interest.