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Crypto price discovery through correlation networks

Author

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  • Paolo Giudici

    (University of Pavia
    Universitá Politecnica delle Marche)

  • Gloria Polinesi

    (University of Pavia
    Universitá Politecnica delle Marche)

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.

Suggested Citation

  • Paolo Giudici & Gloria Polinesi, 2021. "Crypto price discovery through correlation networks," Annals of Operations Research, Springer, vol. 299(1), pages 443-457, April.
  • Handle: RePEc:spr:annopr:v:299:y:2021:i:1:d:10.1007_s10479-019-03282-3
    DOI: 10.1007/s10479-019-03282-3
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    8. Fulvia Pennoni & Francesco Bartolucci & Gianfranco Forte & Ferdinando Ametrano, 2022. "Exploring the dependencies among main cryptocurrency log‐returns: A hidden Markov model," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 51(1), February.
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    11. Bikramaditya Ghosh & Spyros Papathanasiou & Georgios Pergeris, 2022. "Did cryptocurrencies exhibit log‐periodic power law signature during the second wave of COVID‐19?," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 51(3), November.
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    17. Sasan Barak & Navid Parvini, 2023. "Transfer‐entropy‐based dynamic feature selection for evaluating Bitcoin price drivers," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(12), pages 1695-1726, December.
    18. Fabian Woebbeking, 2021. "Cryptocurrency volatility markets," Digital Finance, Springer, vol. 3(3), pages 273-298, December.
    19. Babaei, Golnoosh & Giudici, Paolo & Raffinetti, Emanuela, 2022. "Explainable artificial intelligence for crypto asset allocation," Finance Research Letters, Elsevier, vol. 47(PB).
    20. Amzallag, Adrien, 2022. "Fund portfolio networks: A climate risk perspective," International Review of Financial Analysis, Elsevier, vol. 84(C).
    21. Wei, Lu & Jing, Haozhe & Huang, Jie & Deng, Yuqi & Jing, Zhongbo, 2023. "Do textual risk disclosures reveal corporate risk? Evidence from U.S. fintech corporations," Economic Modelling, Elsevier, vol. 127(C).
    22. Rasoul Amirzadeh & Asef Nazari & Dhananjay Thiruvady & Mong Shan Ee, 2023. "Modelling Determinants of Cryptocurrency Prices: A Bayesian Network Approach," Papers 2303.16148, arXiv.org.
    23. Timothy King & Dimitrios Koutmos, 2021. "Herding and feedback trading in cryptocurrency markets," Annals of Operations Research, Springer, vol. 300(1), pages 79-96, May.
    24. Agosto, Arianna & Cerchiello, Paola & Pagnottoni, Paolo, 2022. "Sentiment, Google queries and explosivity in the cryptocurrency market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).

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