ArticlePloS one2023
Inferring interactions in multispecies communities: The cryptocurrency market case.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
We introduce a general framework for empirically detecting interactions in communities of entities characterized by different features. This approach is inspired by ideas and methods coming from ecology and finance and is applied to a large dataset extracted from the cryptocurrency market. The inter-species interaction network is constructed using a similarity measure based on the log-growth rate of the capitalizations of the cryptocurrency market. The detected relevant interactions are only of the cooperative type, and the network presents a well-defined clustered structure, with two practically disjointed communities. The first one is made up of highly capitalized cryptocurrencies that are tightly connected, and the second one is made up of small-cap cryptocurrencies that are loosely linked. This approach based on the log-growth rate, instead of the conventional price returns, seems to enhance the discriminative potential of the network representation, highlighting a modular structure with compact communities and a rich hierarchy that can be ascribed to different functional groups. In fact, inside the community of the more capitalized coins, we can distinguish between clusters composed of some of the more popular first-generation cryptocurrencies, and clusters made up of second-generation cryptocurrencies. Alternatively, we construct the network of directed interactions by using the partial correlations of the log-growth rate. This network displays the important centrality of Bitcoin, discloses a core cluster containing a branch with the most capitalized first-generation cryptocurrencies, and emphasizes interesting correspondences between the detected direct pair interactions and specific features of the related currencies. As risk strongly depends on the interaction structure of the cryptocurrency system, these results can be useful for assisting in hedging risks. The inferred network topology suggests fewer probable widespread contagions. Moreover, as the riskier coins do not strongly interact with the others, it is more difficult that they can drive the market to more fragile states.
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