Skip to main content
Log in

Crypto price discovery through correlation networks

  • S.I.: Recent Developments in Financial Modeling and Risk Management
  • Published:
Annals of Operations Research Aims and scope Submit manuscript


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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others


  • Billio M., Getmansky, M., Lo, A., & Pelizzon, L. (2012). Econometric measures of connectedness and systemic risk in the finance and insurance sectors. Journal of Financial Economics, 104 (3), 535–559.

  • Bouri, E., Azzi, G., & Dyhrberg, A.H. (2017). On the return-volatility relationship in the Bitcoin market around the price crash of 2013. Economics: The Open-Access, Open-Assessment E-Journal, 11(2017-2).

  • Brandvold, M., Molnr, P., Vagstad, K., Valstad, A., & Ole, C. (2015). Price discovery on Bitcoin exchanges. Journal of International Financial Markets, Institutions and Money, 36(C), 18–35.

    Article  Google Scholar 

  • Calabrese, R., & Giudici, P. (2015). Estimating bank default with generalised extreme value regression models. Journal of the Operational Research Society, 66(11), 1783–1792.

    Article  Google Scholar 

  • Cheah, E. T., & Fry, J. (2015). Speculative bubbles in bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Economics Letters, 130, 32–36.

    Article  Google Scholar 

  • Ciaian, P., & Rajcaniova, M. (2018). Virtual relationships: Short-and long-run evidence from bitcoin and altcoin markets. Journal of International Financial Markets, Institutions and Money, 52, 173–195.

    Article  Google Scholar 

  • Conrad, C., Custovic A., & Ghysels, E. (2018). Long-and short-term cryptocurrency volatility components: A GARCH-MIDAS analysis, Journal of Risk and Financial Management, 11(2), 1–12.

  • Corbet, S., Lucey, B., Urquhart, A., & Yarovaya, L. (2018a). Cryptocurrencies as a financial asset: a systematic analysis. Working Paper.

  • Corbet, S., Meegan, A., Larkin, C., Lucey, B., & Yarovaya, L. (2018b). Exploring the dynamic relationships between cryptocurrencies and other financial assets. Economics Letters, 165, 28–34. (to appear).

    Article  Google Scholar 

  • Dwyer, G. P. (2015). The economics of Bitcoin and similar private digital currencies. Journal of Financial Stability, 17(apr), 81–91.

    Article  Google Scholar 

  • Dyhrberg, A. H. (2016). Bitcoin, gold and the dollar: A GARCH volatility analysis. Finance Research Letters, 16, 85–92.

    Article  Google Scholar 

  • Figini, S., & Giudici, P. (2011). Statistical merging of rating models. Journal of the Operational Research Society, 62, 1067–1074.

    Article  Google Scholar 

  • Giudici, P. (2001) Bayesian data mining, with application to credit scoring and benchmarking. Applied stochastic models in business and industry (vol. 17, pp. 69–81).

  • Giudici, P. (2018). Fintech risk management: A research challenge for artificial intelligence in finance. Frontiers in Artificial Intelligence, 1, 1.

    Article  Google Scholar 

  • Giudici, P., & Abu-Hashish, I. (2018). What determines bitcoin exchange prices? a network VAR approach. Finance Research Letters, 28, 309–318.

    Article  Google Scholar 

  • Gonzalo, J., & Granger, C. (1995). Estimation of common long-memory components in cointegrated systems. Journal of Business & Economic Statistics, 13(1), 27–35.

    Google Scholar 

  • Hafner, C. M. (2018). Testing for bubbles in cryptocurrencies with time-varying volatility. Journal of Financial Econometrics.

  • Hasbrouck, J. (1995). One security, many markets: Determining the contributions to price discovery. The Journal of Finance, 50(4), 1175–1199.

    Article  Google Scholar 

  • Mantegna, R. N. (1999). Hierarchical structure in financial markets. The European Physical Journal B-Condensed Matter and Complex Systems, 11(1), 193–197.

    Article  Google Scholar 

  • Marchenko, V. A., & Pastur, L. A. (1967). Distribution of eigenvalues for some sets of random matrices. Matematicheskii Sbornik, 114, 507–536.

    Google Scholar 

  • Miceli, M.-A., & Susinno, G. (2004). Ultrametricity in fund of funds diversification. Physica A: Statistical Mechanics and its Applications, 344(1–2), 95–99.

    Article  Google Scholar 

  • Nadarajah, S., & Chu, J. (2017). On the inefficiency of Bitcoin. Economics Letters, 150, 6–9.

    Article  Google Scholar 

  • Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. Accessed 2018.

  • Onnela, J.-P., Kaski, K., & Kertész, J. (2004). Clustering and information in correlation based financial networks. The European Physical Journal B, 38(2), 353–362.

    Article  Google Scholar 

  • Pagnottoni, P., Dirk, B., & Dimpfl, T. (2018). Price discovery on Bitcoin markets. Working Paper.

  • Plerou, V., Gopikrishnan, P., Rosenow, B., Amaral, L. A. N., Guhr, T., & Stanley, H. E. (2002). Random matrix approach to cross correlations in financial data. Physical Review E, 65(6), 066126.

    Article  Google Scholar 

  • Spelta, A., & Araújo, T. (2012). The topology of cross-border exposures: Beyond the minimal spanning tree approach. Physica A: Statistical Mechanics and its Applications, 391(22), 5572–5583.

    Article  Google Scholar 

  • Tola, V., Lillo, F., Gallegati, M., & Mantegna, R. N. (2008). Cluster analysis for portfolio optimization. Journal of Economic Dynamics and Control, 32(1), 235–258.

    Article  Google Scholar 

  • Tumminello, M., Aste, T., Di Matteo, T., & Mantegna, R. N. (2005). A tool for filtering information in complex systems. Proceedings of the National Academy of Sciences of the United States of America, 102(30), 10421–10426.

    Article  Google Scholar 

  • Urquhart, A. (2016). The inefficiency of Bitcoin. Economic Letters, 148, 80–82.

    Article  Google Scholar 

Download references


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.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Paolo Giudici.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Giudici, P., Polinesi, G. Crypto price discovery through correlation networks. Ann Oper Res 299, 443–457 (2021).

Download citation

  • Published:

  • Issue Date:

  • DOI: