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• W2980456942 abstract "We employ a time-scale multi-fractal decomposition approach to investigate the properties of Bitcoin prices and volume at different sampling rates using high-frequency data. We provide evidence of multi-fractality at all rates. The big data-driven analysis combined with statistical testing shows evidence of dominant multi-fractal traits within the intervals of 5 mn–90 mn, and 120 mn up to 720 mn. Wavelet leaders comprise a promising algorithmic technique that provides a richer description of the singularity spectrum. In particular, we reveal the distinct heterogeneity of the three log-cumulants for prices and volume between the two distinctive high-frequency sampling intervals. Our findings may assist in devising profitable high-frequency trading strategies in crypto-currency markets." @default.
• W2980456942 created "2019-10-25" @default.
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• W2980456942 date "2020-02-01" @default.
• W2980456942 modified "2023-10-18" @default.
• W2980456942 title "Big data analytics using multi-fractal wavelet leaders in high-frequency Bitcoin markets" @default.
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• W2980456942 doi "https://doi.org/10.1016/j.chaos.2019.109472" @default.
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