In recent years a new type of tradable assets appeared, generically
known as cryptocurrencies. Among them, the most widespread is Bitcoin. Given its novelty, this paper investigates some statistical properties of the Bitcoin market. This study compares Bitcoin
and standard currencies dynamics and focuses on the analysis of returns
at different time scales. We test the presence of long memory in return
time series from 2011 to 2017, using transaction data from one Bitcoin
platform. We compute the Hurst exponent by means of the Detrended
Fluctuation Analysis method, using a sliding window in order to measure
long range dependence. We detect that Hurst exponents changes
significantly during the first years of existence of Bitcoin,
tending to stabilize in recent times. Additionally, multiscale analysis
shows a similar behavior of the Hurst exponent, implying a self-similar
process.
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3017968
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