Big data analytics using multi-fractal wavelet leaders in high-frequency Bitcoin markets
Chaos, Solitons & Fractals, ISSN: 0960-0779, Vol: 131, Page: 109472
2020
- 29Citations
- 42Captures
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Article Description
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.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0960077919304187; http://dx.doi.org/10.1016/j.chaos.2019.109472; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85073720646&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0960077919304187; https://api.elsevier.com/content/article/PII:S0960077919304187?httpAccept=text/xml; https://api.elsevier.com/content/article/PII:S0960077919304187?httpAccept=text/plain; https://dul.usage.elsevier.com/doi/; https://dx.doi.org/10.1016/j.chaos.2019.109472
Elsevier BV
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