Abstract
We propose a novel modelling approach for the cryptocurrency ecosystem. We model on-chain and off-chain interactions as econophysical systems and employ methods from physical sciences to conduct interpretation of latent parameters describing the cryptocurrency ecosystem as well as to generate predictions. We work with an extracted dataset from the Ethereum blockchain which we combine with off-chain data from exchanges. This allows us to study a large part of the transaction flows related to the cryptocurrency ecosystem. From this aggregate system view we deduct that movements on the blockchain and price and trading action on exchanges are interrelated. The relationship is one directional: On-chain token flows towards exchanges have little effect on prices and trading volume, but changes in price and volume affect the flow of tokens towards the exchange.
This work is supported by Worley Parsons. We are also grateful for contributions and helpful comments from Barthelemy Duthoit, Kai Sun, Ovidiu Serban and cryptocompare.com.
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References
Androulaki, E., Karame, G. O., Roeschlin, M., Scherer, T., & Capkun, S. (2013). Evaluating user privacy in bitcoin. In International Conference on Financial Cryptography and Data Security (pp. 34–51). Springer.
Asch, M., Bocquet, M., & Nodet, M. (2016). Data assimilation: methods, algorithms, and applications (Vol. 11). SIAM.
Bano, S., Sonnino, A., Al-Bassam, M., Azouvi, S., McCorry, P., Meiklejohn, S., & Danezis, G. (2019). Sok: Consensus in the age of blockchains. In Proceedings of the 1st ACM Conference on Advances in Financial Technologies (pp. 183–198).
Barndorff-Nielsen, O. E., & Shephard, N. (2002). Econometric analysis of realized volatility and its use in estimating stochastic volatility models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(2), 253–280.
Campbell, J. Y., & Mankiw, N. G. (1987). Are output fluctuations transitory? The Quarterly Journal of Economics, 102(4), 857–880.
Chen, G.-M., Firth, M., & Rui, O. M. (2001). The dynamic relation between stock returns, trading volume, and volatility. Financial Review, 36(3), 153–174.
Chen, T.,  Zhu, Y., Li, Z.,  Chen, J., Li, X., Luo, X., Lin, X., & Zhange, X. (2018). Understanding ethereum via graph analysis. In IEEE INFOCOM 2018-IEEE Conference on Computer Communications (pp. 1484–1492). IEEE.
Cochrane, J. H. (1988). How big is the random walk in gnp? Journal of political economy, 96(5), 893–920.
Cuomo, S., Galletti, A., Giunta, G., & Marcellino, L. (2017). Numerical effects of the gaussian recursive filters in solving linear systems in the 3dvar case study. Numerical Mathematics: Theory, Methods and Applications, 10(3), 520–540.
Fry, J., & Cheah, E.-T. (2016). Negative bubbles and shocks in cryptocurrency markets. International Review of Financial Analysis, 47, 343–352.
Guegan, D. (2009). Chaos in economics and finance. Annual Reviews in Control, 33(1), 89–93.
Jang, H., & Lee, J. (2017). An empirical study on modeling and prediction of bitcoin prices with bayesian neural networks based on blockchain information. Ieee Access, 6, 5427–5437.
McGinn, D., Birch, D., Akroyd, D., Molina-Solana, M., Guo, Y., & Knottenbelt, W. J. (2016). Visualizing dynamic bitcoin transaction patterns. Big Data, 4(2), 109–119.
Meiklejohn, S., Pomarole, M., Jordan, G., Levchenko, K., McCoy, D., Voelker, G. M., & Savage, S. (2013). A fistful of bitcoins: characterizing payments among men with no names. In Proceedings of the 2013 conference on Internet measurement conference (pp. 127–140). ACM.
Nadler, P., Arcucci, R., & Guo, Y. (2019). A scalable approach to econometric inference. volume 36 of Advances in Parallel Computing (pp. 59–68). IOS Press.
Nadler, P., & Guo, Y. (2020). The fair value of a token: How do markets price cryptocurrencies? Research in International Business and Finance, 52, 101108.
Nakamoto, S., et al. (2008). Bitcoin: A peer-to-peer electronic cash system.
Pereira, E. J. d A. L., da Silva, M. F., & Pereira, H. d B. (2017). Econophysics: Past and present. Physica A: Statistical Mechanics and its Applications, 473, 251–261.
Ranshous, S., Joslyn, C. A., Kreyling, S., Nowak, K., Samatova, N. F., West, C. L., & Winters, S. (2017). Exchange pattern mining in the bitcoin transaction directed hypergraph. In International Conference on Financial Cryptography and Data Security (pp. 248–263). Springer.
Seres, I. A., Nagy, D. A., Buckland, C., & Burcsi, P. (2019). Mixeth: efficient, trustless coin mixing service for ethereum. In International Conference on Blockchain Economics, Security and Protocols (Tokenomics 2019). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik.
Smith, P. J., Dance, S. L., Baines, M. J., Nichols, N. K., & Scott, T. R. (2009). Variational data assimilation for parameter estimation: application to a simple morphodynamic model. Ocean Dynamics, 59(5), 697.
Wood, G., et al. (2014). Ethereum: A secure decentralised generalised transaction ledger. Ethereum project yellow paper, 151(2014), 1–32.
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Nadler, P., Arcucci, R., Guo, Y. (2020). An Econophysical Analysis of the Blockchain Ecosystem. In: Pardalos, P., Kotsireas, I., Guo, Y., Knottenbelt, W. (eds) Mathematical Research for Blockchain Economy. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-53356-4_3
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