Dr. Carlo Campajola
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Dr. Carlo Campajola Senior Research Associate |
Short biography
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Dr Carlo Campajola holds a PhD cum laude in applied mathematics (quantitative finance) from the Scuola Normale Superiore (Pisa, Italy), which he earned working on models of discrete autoregressive processes intersecting methods from statistical physics and financial econometrics. He previously graduated from the International Master in Physics of Complex Systems organised by the Polytechnic of Torino, the International School for Advanced Studies in Trieste and the Campus Paris Saclay in Paris. His current research activity, together with Prof. Dr Claudio J. Tessone, focuses on the analysis of cryptocurrencies from a complex systems perspective, with particular attention to the characterisation of individual properties of economic agents which affect the collective functioning of the system. Specific topics include analysis of transaction networks, modelling of the velocity of tokens, statistical methods for blockchain deanonymisation and fraud detection, and financial properties of crypto assets. |
Research Interests
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Publications
ZORA Publication List
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Publications
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The microvelocity of money in Ethereum. EPJ Data Science, 14:11.
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Statistical detection of selfish mining in proof-of-work blockchain systems. Scientific Reports, 14(1):6251.
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The Evolving Liaisons between the Transaction Networks of Bitcoin and Its Price Dynamics. In: Proceedings of Blockchain Kaigi 2022 (BCK22), Sendai, Japan, 4 August 2022 - 5 August 2022, Physical Society of Japan.
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On the equivalence between the kinetic Ising model and discrete autoregressive processes. Journal of Statistical Mechanics: Theory and Experiment, 2021(3):033412.
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Tail Granger causalities and where to find them: Extreme risk spillovers vs spurious linkages. Journal of Economic Dynamics and Control, 121:104022.
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Unveiling the relation between herding and liquidity with trader lead-lag networks. Quantitative Finance, 20(11):1765-1778.
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Inference of the kinetic Ising model with heterogeneous missing data. Physical review. E, 99(6):062138.