Cryptocurrencies are increasingly popular digital assets/cashes programmed to work as a medium of exchange that are "secure" by design (e.g., through block-chains and cryptography). The year 2017 saw the rise and fall of the cryptocurrency market, followed by high volatility in the price of each cryptocurrency . In this work, we study critical transitions in cryptocurrency residualsthrough the phenomenon of critical slowing down  . We find that, regardless of the specific cryptocurrency or rolling window size, the autocorrelation always fluctuates around a high value and the standard deviation increases monotonically.  In particular, we have detected two sudden jumps in the standard deviation, in the second quarter of 2017 and at the beginning of 2018, suggesting early warning signals of two majors price collapse that have happened in those periods. Our findings represent a first step towards a better diagnostic of the risk of critical transition in the price and/or volume of cryptocurrencies .
 The year 2017 saw the rise and fall of the crypto-currency market, followed by high variability in the price of all crypto-currencies . In this work, we study the abrupt transition in crypto-currency residuals, which is associated with the critical transition (the phenomenon of critical slowing down ) or the stochastic transition phenomena . We find that, regardless of the specific crypto-currency or rolling window size, the autocorrelation always fluctuates around a high value , while the standard deviation increases monotonically. Therefore, while the autocorrelation does not display signals of critical slowing down, the standard deviation can be used to anticipate critical or stochastic transitions. In particular, we have detected two sudden jumps in the standard deviation, in the second quarter of 2017 and at the beginning of 2018, which could have served as early warning signals of two majors price collapses that have happened in the following periods. We finally propose a mean-field phenomenological model for the price of crypto-currency to show how the use of the standard deviation of the residuals is a better leading indicator of the collapse in price than the time series' autocorrelation. Our findings represent a first step towards a better diagnostic of the risk of critical transition in the price and/or volume of crypto-currencies .
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Cryptocurrencies are increasingly popular digital assets/cashes programmed to work as a medium of exchange that are "secure" by design (e.g., through block-chains and cryptography).
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cryptocurrency
crypto-currency
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volatility
variability
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each cryptocurrency
all crypto-currencies
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critical transitions in cryptocurrency residualsthrough the
the abrupt transition in crypto-currency residuals, which is associated with the critical transition (the
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) or the stochastic transition phenomena
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cryptocurrency
crypto-currency
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and
, while
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Therefore, while the autocorrelation does not display signals of critical slowing down, the standard deviation can be used to anticipate critical or stochastic transitions.
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suggesting
which could have served as
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collapse
collapses
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those periods.
the following periods. We finally propose a mean-field phenomenological model for the price of crypto-currency to show how the use of the standard deviation of the residuals is a better leading indicator of the collapse in price than the time series' autocorrelation.
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cryptocurrencies
crypto-currencies