IDEAS home Printed from https://ideas.repec.org/p/zbw/irtgdp/2020027.html
   My bibliography  Save this paper

Blockchain mechanism and distributional characteristics of cryptos

Author

Listed:
  • Lin, Min-Bin
  • Khowaja, Kainat
  • Chen, Cathy Yi-Hsuan
  • Härdle, Wolfgang Karl

Abstract

We investigate the relationship between underlying blockchain mechanism of cryptocurrencies and its distributional characteristics. In addition to price, we emphasise on using actual block size and block time as the operational features of cryptos. We use distributional characteristics such as fourier power spectrum, moments, quantiles, global we optimums, as well as the measures for long term dependencies, risk and noise to summarise the information from crypto time series. With the hypothesis that the blockchain structure explains the distributional characteristics of cryptos, we use characteristic based spectral clustering to cluster the selected cryptos into five groups. We scrutinise these clusters and find that indeed, the clusters of cryptos share similar mechanism such as origin of fork, difficulty adjustment frequency, and the nature of block size. This paper provides crypto creators and users with a better understanding toward the connection between the blockchain protocol design and distributional characteristics of cryptos.

Suggested Citation

  • Lin, Min-Bin & Khowaja, Kainat & Chen, Cathy Yi-Hsuan & Härdle, Wolfgang Karl, 2020. "Blockchain mechanism and distributional characteristics of cryptos," IRTG 1792 Discussion Papers 2020-027, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    • Handle: RePEc:zbw:irtgdp:2020027
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/230833/1/irtg1792dp2020-027.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Guo, Li & Tao, Yubo & Härdle, Wolfgang Karl, 2018. "Understanding Latent Group Structure of Cryptocurrencies Market: A Dynamic Network Perspective," IRTG 1792 Discussion Papers 2018-032, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    2. Yhlas Sovbetov, 2018. "Factors Influencing Cryptocurrency Prices: Evidence from Bitcoin, Ethereum, Dash, Litcoin, and Monero," Journal of Economics and Financial Analysis, Tripal Publishing House, vol. 2(2), pages 1-27.
    3. Iwamura, Mitsuru & Kitamura, Yukinobu & 北村, 行伸 & Matsumoto, Tsutomu & Saito, Kenji, 2019. "Can We Stabilize the Price of a Cryptocurrency?: Understanding the Design of Bitcoin and Its Potential to Compete with Central Bank Money," Hitotsubashi Journal of Economics, Hitotsubashi University, vol. 60(1), pages 41-60, June.
    4. Zimmerman, Peter, 2020. "Blockchain structure and cryptocurrency prices," Bank of England working papers 855, Bank of England.
    5. Trimborn, Simon & Härdle, Wolfgang Karl, 2018. "CRIX an Index for cryptocurrencies," Journal of Empirical Finance, Elsevier, vol. 49(C), pages 107-122.
    6. Wolfgang Karl Härdle & Campbell R Harvey & Raphael C G Reule, 2020. "Understanding Cryptocurrencies," Journal of Financial Econometrics, Oxford University Press, vol. 18(2), pages 181-208.
    7. Ai Jun Hou & Weining Wang & Cathy Y H Chen & Wolfgang Karl Härdle, 2020. "Pricing Cryptocurrency Options," Journal of Financial Econometrics, Oxford University Press, vol. 18(2), pages 250-279.
    8. Katsiampa, Paraskevi & Corbet, Shaen & Lucey, Brian, 2019. "High frequency volatility co-movements in cryptocurrency markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 62(C), pages 35-52.
    9. Wolfgang Karl Hardle & Campbell R. Harvey & Raphael C. G. Reule, 2020. "Editorial: Understanding Cryptocurrencies," Papers 2007.14702, arXiv.org.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zinovyev, Elizaveta & Reule, Raphael C. G. & Härdle, Wolfgang, 2021. "Understanding Smart Contracts: Hype or hope?," IRTG 1792 Discussion Papers 2021-004, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    2. Stefan Craß & Alexander Eisl & Nedim Begic & Romana Polt, 2022. "Die Rolle moderner Technologien, insbesondere Blockchain, in der Lieferkettenverantwortung," FIW Research Reports series VIII-006, FIW.
    3. Ingo Weber & Mark Staples, 2022. "Programmable money: next-generation blockchain-based conditional payments," Digital Finance, Springer, vol. 4(2), pages 109-125, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Konstantin Häusler & Hongyu Xia, 2022. "Indices on cryptocurrencies: an evaluation," Digital Finance, Springer, vol. 4(2), pages 149-167, September.
    2. Georg Keilbar & Yanfen Zhang, 2021. "On cointegration and cryptocurrency dynamics," Digital Finance, Springer, vol. 3(1), pages 1-23, March.
    3. Konstantin Hausler & Wolfgang Karl Hardle, 2021. "Cryptocurrency Dynamics: Rodeo or Ascot?," Papers 2103.12461, arXiv.org, revised Jan 2022.
    4. Häusler, Konstantin & Härdle, Wolfgang, 2021. "Rodeo or ascot: Which hat to wear at the crypto race?," IRTG 1792 Discussion Papers 2021-007, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    5. Liebi, Luca J., 2022. "Is there a value premium in cryptoasset markets?," Economic Modelling, Elsevier, vol. 109(C).
    6. Charfeddine, Lanouar & Benlagha, Noureddine & Khediri, Karim Ben, 2022. "An intra-cryptocurrency analysis of volatility connectedness and its determinants: Evidence from mining coins, non-mining coins and tokens," Research in International Business and Finance, Elsevier, vol. 62(C).
    7. Danial Saef & Yuanrong Wang & Tomaso Aste, 2022. "Regime-based Implied Stochastic Volatility Model for Crypto Option Pricing," Papers 2208.12614, arXiv.org, revised Sep 2022.
    8. Kim, Alisa & Trimborn, Simon & Härdle, Wolfgang Karl, 2021. "VCRIX — A volatility index for crypto-currencies," International Review of Financial Analysis, Elsevier, vol. 78(C).
    9. Abel Brodeur & David Gray & Anik Islam & Suraiya Bhuiyan, 2021. "A literature review of the economics of COVID‐19," Journal of Economic Surveys, Wiley Blackwell, vol. 35(4), pages 1007-1044, September.
    10. Elizaveta Zinovyeva & Raphael C. G. Reule & Wolfgang Karl Hardle, 2021. "Understanding Smart Contracts: Hype or Hope?," Papers 2103.08447, arXiv.org.
    11. Yaya, OlaOluwa S. & Lukman, Adewale F. & Vo, Xuan Vinh, 2022. "Persistence and volatility spillovers of bitcoin price to gold and silver prices," Resources Policy, Elsevier, vol. 79(C).
    12. Nikolaos A. Kyriazis, 2021. "A Survey on Volatility Fluctuations in the Decentralized Cryptocurrency Financial Assets," JRFM, MDPI, vol. 14(7), pages 1-46, June.
    13. Matic, Jovanka Lili & Packham, Natalie & Härdle, Wolfgang Karl, 2021. "Hedging Cryptocurrency Options," MPRA Paper 110985, University Library of Munich, Germany.
    14. Lee A. Smales, 2021. "Volatility Spillovers among Cryptocurrencies," JRFM, MDPI, vol. 14(10), pages 1-12, October.
    15. Yousaf, Imran & Yarovaya, Larisa, 2022. "Herding behavior in conventional cryptocurrency market, non-fungible tokens, and DeFi assets," Finance Research Letters, Elsevier, vol. 50(C).
    16. Almeida, José & Gonçalves, Tiago Cruz, 2023. "A systematic literature review of investor behavior in the cryptocurrency markets," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    17. Ciner, Cetin & Lucey, Brian & Yarovaya, Larisa, 2022. "Determinants of cryptocurrency returns: A LASSO quantile regression approach," Finance Research Letters, Elsevier, vol. 49(C).
    18. Figà-Talamanca, Gianna & Focardi, Sergio & Patacca, Marco, 2021. "Regime switches and commonalities of the cryptocurrencies asset class," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    19. Leonardo Ieracitano Vieira & Márcio Poletti Laurini, 2023. "Time-varying higher moments in Bitcoin," Digital Finance, Springer, vol. 5(2), pages 231-260, June.
    20. Mingzhe Wei & Georgios Sermpinis & Charalampos Stasinakis, 2023. "Forecasting and trading Bitcoin with machine learning techniques and a hybrid volatility/sentiment leverage," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 852-871, July.

    More about this item

    Keywords

    Cryptocurrency; price; blockchain mechanism; distributional characteristics; clustering;
    All these keywords.

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:irtgdp:2020027. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/wfhubde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.