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Crypto price discovery through correlation networks

  • S.I.: Recent Developments in Financial Modeling and Risk Management
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Abstract

We aim to understand the dynamics of crypto asset prices and, specifically, how price information is transmitted among different bitcoin market exchanges, and between bitcoin markets and traditional ones. To this aim, we hierarchically cluster bitcoin prices from different exchanges, as well as classic assets, by enriching the correlation based minimum spanning tree method with a preliminary filtering method based on the random matrix approach. Our main empirical findings are that: (i) bitcoin exchange prices are positively related with each other and, among them, the largest exchanges, such as Bitstamp, drive the prices; (ii) bitcoin exchange prices are not affected by classic asset prices, but their volatilities are, with a negative and lagged effect.

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Acknowledgements

We acknowledge support from the European Union’s Horizon 2020 research and innovation programme, under grant agreement No 825215 (Topic: ICT-35-2018 Type of action: CSA), and from the Universitá Politecnica delle Marche. We also thank two anonymous referees, for the provided suggestions which have helped improving the paper.

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Authors and Affiliations

  1. Department of Economics, University of Pavia, Via S. Felice 5, 27100, Pavia, PV, Italy

    Paolo Giudici & Gloria Polinesi

  2. Department of Economic and Social Sciences, Universitá Politecnica delle Marche, Ancona, Italy

    Paolo Giudici & Gloria Polinesi

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  1. Paolo Giudici
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  2. Gloria Polinesi
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Correspondence to Paolo Giudici.

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Giudici, P., Polinesi, G. Crypto price discovery through correlation networks. Ann Oper Res 299, 443–457 (2021). https://doi.org/10.1007/s10479-019-03282-3

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  • DOI: https://doi.org/10.1007/s10479-019-03282-3

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