Table of Links
2. Data and quantitative nature of the events
2.2. Transaction data analysis
3. Methodology
3.1. Network analysis: Triangulated Maximally Filtered Graph (TMFG)
4. Results
4.1. Correlations and network analysis
4.2. Herding analysis: CSAD approach
6. Implications and future research
6.1. Relevance for stakeholders
7. Conclusion, Acknowledgements, and References
4.2. Herding analysis: CSAD approach
Authors:
(1) Antonio Briola, Department of Computer Science, University College London, Gower Street, WC1E 6EA - London, United Kingdom and UCL Centre for Blockchain Technologies, London, United Kingdom;
(2) David Vidal-Tomas (Corresponding author), Department of Computer Science, University College London, Gower Street, WC1E 6EA - London, United Kingdom, Department of Economics, Universitat Jaume I, Campus del Riu Sec, 12071 - Castellon, Spain and UCL Centre for Blockchain Technologies, London, United Kingdom ([email protected]);
(3) Yuanrong Wang, Department of Computer Science, University College London, Gower Street, WC1E 6EA - London, United Kingdom and UCL Centre for Blockchain Technologies, London, United Kingdom;
(4) Tomaso Aste, Department of Computer Science, University College London, Gower Street, WC1E 6EA - London, United Kingdom, Systemic Risk Centre, London School of Economics, London, United Kingdom, and UCL Centre for Blockchain Technologies, London, United Kingdom.
This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.
[15] For robustness purposes, we applied Eq.(4) through rolling windows of 7 days, i.e. 168 hours/observations. However, the absence of herding remains. Moreover, if we remove stablecoins from our sample, whose returns are mainly 0 (e.g. USDT or DAI), we keep obtaining the same result.
