IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2408.07653.html

Stylized facts in Web3

Author

Listed:
  • Wei-Ru Chen
  • A. Christian Silva
  • Shen-Ning Tung

Abstract

This paper presents a comprehensive statistical analysis of the Web3 ecosystem, comparing various Web3 tokens with traditional financial assets across multiple time scales. We examine probability distributions, tail behaviors, and other key stylized facts of the returns for a diverse range of tokens, including decentralized exchanges, liquidity pools, and centralized exchanges. Despite functional differences, most tokens exhibit well-established empirical facts, including unconditional probability density of returns with heavy tails gradually becoming Gaussian and volatility clustering. Furthermore, we compare assets traded on centralized (CEX) and decentralized (DEX) exchanges, finding that DEXs exhibit similar stylized facts despite different trading mechanisms and often divergent long-term performance. We propose that this similarity is attributable to arbitrageurs striving to maintain similar centralized and decentralized prices. Our study contributes to a better understanding of the dynamics of Web3 tokens and the relationship between CEX and DEX markets, with important implications for risk management, pricing models, and portfolio construction in the rapidly evolving DeFi landscape. These results add to the growing body of literature on cryptocurrency markets and provide insights that can guide the development of more accurate models for DeFi markets.

Suggested Citation

  • Wei-Ru Chen & A. Christian Silva & Shen-Ning Tung, 2024. "Stylized facts in Web3," Papers 2408.07653, arXiv.org, revised Dec 2024.
    • Handle: RePEc:arx:papers:2408.07653
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2408.07653
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gilles Zumbach, 2009. "Time reversal invariance in finance," Quantitative Finance, Taylor & Francis Journals, vol. 9(5), pages 505-515.
    2. Joseph Najnudel & Shen-Ning Tung & Kazutoshi Yamazaki & Ju-Yi Yen, 2024. "An arbitrage driven price dynamics of Automated Market Makers in the presence of fees," Papers 2401.01526, arXiv.org.
    3. Silva, A. Christian & Prange, Richard E. & Yakovenko, Victor M., 2004. "Exponential distribution of financial returns at mesoscopic time lags: a new stylized fact," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 227-235.
    4. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    5. Heston, Steven L. & Sadka, Ronnie, 2008. "Seasonality in the cross-section of stock returns," Journal of Financial Economics, Elsevier, vol. 87(2), pages 418-445, February.
    6. Zhang, Yuanyuan & Chan, Stephen & Chu, Jeffrey & Nadarajah, Saralees, 2019. "Stylised facts for high frequency cryptocurrency data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 598-612.
    7. Dominique Torre & Sothearath Seang, 2019. "Proof of Work and Proof of Stake consensus protocols: a blockchain application for local complementary currencies," Working Papers halshs-02268391, HAL.
    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. Daniele Maria Di Nosse & Federico Gatta & Fabrizio Lillo & Sebastian Jaimungal, 2025. "Deviations from Tradition: Stylized Facts in the Era of DeFi," Papers 2510.22834, arXiv.org.

    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. Takaishi, Tetsuya, 2025. "Multifractality and sample size influence on Bitcoin volatility patterns," Finance Research Letters, Elsevier, vol. 74(C).
    2. Benjamin R. Auer, 2019. "Does the strength of capital market anomalies exhibit seasonal patterns?," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 43(1), pages 91-103, January.
    3. Guillaume Coqueret, 2016. "Empirical properties of a heterogeneous agent model in large dimensions," Post-Print hal-02088097, HAL.
    4. Michele Vodret & Iacopo Mastromatteo & Bence Tóth & Michael Benzaquen, 2023. "Microfounding GARCH models and beyond: a Kyle-inspired model with adaptive agents," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 18(3), pages 599-625, July.
    5. Eduardo Abi Jaber & Shaun & Li, 2025. "Capturing Smile Dynamics with the Quintic Volatility Model: SPX, Skew-Stickiness Ratio and VIX," Papers 2503.14158, arXiv.org.
    6. Andreas Behr & Ulrich Pötter, 2009. "Alternatives to the normal model of stock returns: Gaussian mixture, generalised logF and generalised hyperbolic models," Annals of Finance, Springer, vol. 5(1), pages 49-68, January.
    7. Guillaume Coqueret, 2017. "Empirical properties of a heterogeneous agent model in large dimensions," Post-Print hal-02000726, HAL.
    8. López Martín, María del Mar & García, Catalina García & García Pérez, José, 2012. "Treatment of kurtosis in financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(5), pages 2032-2045.
    9. Tilfani, Oussama & Kristoufek, Ladislav & Ferreira, Paulo & El Boukfaoui, My Youssef, 2022. "Heterogeneity in economic relationships: Scale dependence through the multivariate fractal regression," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    10. Coqueret, Guillaume, 2017. "Empirical properties of a heterogeneous agent model in large dimensions," Journal of Economic Dynamics and Control, Elsevier, vol. 77(C), pages 180-201.
    11. Seemann, Lars & McCauley, Joseph L. & Gunaratne, Gemunu H., 2011. "Intraday volatility and scaling in high frequency foreign exchange markets," International Review of Financial Analysis, Elsevier, vol. 20(3), pages 121-126, June.
    12. Sosa-Correa, William O. & Ramos, Antônio M.T. & Vasconcelos, Giovani L., 2018. "Investigation of non-Gaussian effects in the Brazilian option market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 525-539.
    13. Chan, Stephen & Chandrashekhar, Durga & Almazloum, Ward & Zhang, Yuanyuan & Lord, Nicholas & Osterrieder, Joerg & Chu, Jeffrey, 2024. "Stylized facts of metaverse non-fungible tokens," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 653(C).
    14. Gianna Figá-Talamanca & Sergio Focardi & Marco Patacca, 2021. "Common dynamic factors for cryptocurrencies and multiple pair-trading statistical arbitrages," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 863-882, December.
    15. Bernardo Spagnolo & Davide Valenti, 2008. "Volatility Effects on the Escape Time in Financial Market Models," Papers 0810.1625, arXiv.org.
    16. Buchbinder, G.L. & Chistilin, K.M., 2007. "Multiple time scales and the empirical models for stochastic volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 379(1), pages 168-178.
    17. Fernando F. Ferreira & A. Christian Silva & Ju-Yi Yen, 2019. "Detailed study of a moving average trading rule," Papers 1907.00212, arXiv.org.
    18. Guillaume Coqueret, 2017. "Empirical properties of a heterogeneous agent model in large dimensions," Post-Print hal-02312186, HAL.
    19. Léo Parent, 2022. "The EWMA Heston model," Post-Print hal-04431111, HAL.
    20. Chen, Ray-Bing & Chen, Ying & Härdle, Wolfgang K., 2014. "TVICA—Time varying independent component analysis and its application to financial data," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 95-109.

    More about this item

    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:arx:papers:2408.07653. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.