IDEAS home Printed from https://ideas.repec.org/a/eee/jebusi/v133y2025ics0148619524000663.html
   My bibliography  Save this article

Pricing efficiency in cryptocurrencies: The case of centralized and decentralized markets

Author

Listed:
  • Almeida, Lucas Mussoi
  • Perlin, Marcelo Scherer
  • Müller, Fernanda Maria

Abstract

This article presents a comparative analysis of the Ethereum (ETH) weak form of market efficiency priced in Bitcoin (BTC), Dai (DAI), and Tether (USDT). The investigation encompasses data from Uniswap-V2, a decentralized app utilizing a Constant Product Market Maker (CFMM) for cryptocurrency pricing, and Binance, a centralized exchange. The study employs different rolling windows to apply the asymmetric MF-DFA. The efficiency of exchange pairs is ranked using the market deficiency measure (MDM). Besides aligning with the literature, revealing an efficiency increase with larger rolling window sizes across centralized and decentralized exchanges and overall, upward and downward trends, our findings reveal that the CFMM employed by Uniswap-V2 leads to a more efficient market for the ETH-BTC pair compared to Binance, making it among the first studies to compare efficiency across these exchanges types. To delve deeper into this phenomenon and explore the dynamics between distinct pricing mechanisms, the Thermal Optimal Path is employed. The analysis highlights a lead-lag relationship between ETH prices in centralized and decentralized exchanges. The results suggest that market efficiency emerges first in the decentralized exchange, particularly when ETH is priced in BTC. The asymmetric MF-DFA was also employed on the pairs datasets before and after the Ethereum 2.0 hard fork. The findings of this analysis revealed significant results indicating that following the fork, Uniswap-V2 exhibited superior market efficiency compared to Binance for the majority of overall and downward trends, a phenomenon that was not observed prior to the merge. These findings contribute to the existing literature on cryptocurrency market efficiency by emphasizing the influence of network upgrades in trading platforms. Notably, this research reveals that, for the ETH-BTC pair, decentralized exchanges exhibit superior a level of weak form efficiency.

Suggested Citation

  • Almeida, Lucas Mussoi & Perlin, Marcelo Scherer & Müller, Fernanda Maria, 2025. "Pricing efficiency in cryptocurrencies: The case of centralized and decentralized markets," Journal of Economics and Business, Elsevier, vol. 133(C).
    • Handle: RePEc:eee:jebusi:v:133:y:2025:i:c:s0148619524000663
    DOI: 10.1016/j.jeconbus.2024.106224
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148619524000663
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jeconbus.2024.106224?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Al-Yahyaee, Khamis Hamed & Mensi, Walid & Ko, Hee-Un & Yoon, Seong-Min & Kang, Sang Hoon, 2020. "Why cryptocurrency markets are inefficient: The impact of liquidity and volatility," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    2. Chordia, Tarun & Roll, Richard & Subrahmanyam, Avanidhar, 2008. "Liquidity and market efficiency," Journal of Financial Economics, Elsevier, vol. 87(2), pages 249-268, February.
    3. Sinan Krückeberg & Peter Scholz, 2020. "Decentralized Efficiency? Arbitrage in Bitcoin Markets," Financial Analysts Journal, Taylor & Francis Journals, vol. 76(3), pages 135-152, July.
    4. Kakinaka, Shinji & Umeno, Ken, 2022. "Cryptocurrency market efficiency in short- and long-term horizons during COVID-19: An asymmetric multifractal analysis approach," Finance Research Letters, Elsevier, vol. 46(PA).
    5. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," The Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
    6. Lennart Ante & Ingo Fiedler & Jan Marius Willruth & Fred Steinmetz, 2023. "A Systematic Literature Review of Empirical Research on Stablecoins," FinTech, MDPI, vol. 2(1), pages 1-14, January.
    7. Urquhart, Andrew, 2016. "The inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 148(C), pages 80-82.
    8. Shleifer, Andrei & Vishny, Robert W, 1997. "The Limits of Arbitrage," Journal of Finance, American Finance Association, vol. 52(1), pages 35-55, March.
    9. Didier Sornette & Wei-Xing Zhou, 2005. "Non-parametric determination of real-time lag structure between two time series: the 'optimal thermal causal path' method," Quantitative Finance, Taylor & Francis Journals, vol. 5(6), pages 577-591.
    10. Corbet, Shaen & Larkin, Charles & Lucey, Brian, 2020. "The contagion effects of the COVID-19 pandemic: Evidence from gold and cryptocurrencies," Finance Research Letters, Elsevier, vol. 35(C).
    11. Kun Guo & Wei-Xing Zhou & Si-Wei Cheng & Didier Sornette, 2011. "The US Stock Market Leads the Federal Funds Rate and Treasury Bond Yields," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-9, August.
    12. Ali, Sajid & Shahzad, Syed Jawad Hussain & Raza, Naveed & Al-Yahyaee, Khamis Hamed, 2018. "Stock market efficiency: A comparative analysis of Islamic and conventional stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 139-153.
    13. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    14. Jia, Rui-Lin & Wang, Dong-Hua & Tu, Jing-Qing & Li, Sai-Ping, 2016. "Correlation between agricultural markets in dynamic perspective—Evidence from China and the US futures markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 464(C), pages 83-92.
    15. Madhavan, Ananth, 1996. "Security Prices and Market Transparency," Journal of Financial Intermediation, Elsevier, vol. 5(3), pages 255-283, July.
    16. Mensi, Walid & Lee, Yun-Jung & Al-Yahyaee, Khamis Hamed & Sensoy, Ahmet & Yoon, Seong-Min, 2019. "Intraday downward/upward multifractality and long memory in Bitcoin and Ethereum markets: An asymmetric multifractal detrended fluctuation analysis," Finance Research Letters, Elsevier, vol. 31(C), pages 19-25.
    17. Hao Meng & Hai-Chuan Xu & Wei-Xing Zhou & Didier Sornette, 2017. "Symmetric thermal optimal path and time-dependent lead-lag relationship: novel statistical tests and application to UK and US real-estate and monetary policies," Quantitative Finance, Taylor & Francis Journals, vol. 17(6), pages 959-977, June.
    18. Aloui, Chaker & Shahzad, Syed Jawad Hussain & Jammazi, Rania, 2018. "Dynamic efficiency of European credit sectors: A rolling-window multifractal detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 337-349.
    19. Guillermo Angeris & Tarun Chitra, 2020. "Improved Price Oracles: Constant Function Market Makers," Papers 2003.10001, arXiv.org, revised Jun 2020.
    20. Keshab Shrestha, 2021. "Multifractal Detrended Fluctuation Analysis of Return on Bitcoin," International Review of Finance, International Review of Finance Ltd., vol. 21(1), pages 312-323, March.
    21. Khuntia, Sashikanta & Pattanayak, J.K., 2020. "Adaptive long memory in volatility of intra-day bitcoin returns and the impact of trading volume," Finance Research Letters, Elsevier, vol. 32(C).
    22. Zargar, Faisal Nazir & Kumar, Dilip, 2019. "Informational inefficiency of Bitcoin: A study based on high-frequency data," Research in International Business and Finance, Elsevier, vol. 47(C), pages 344-353.
    23. Al-Yahyaee, Khamis Hamed & Mensi, Walid & Yoon, Seong-Min, 2018. "Efficiency, multifractality, and the long-memory property of the Bitcoin market: A comparative analysis with stock, currency, and gold markets," Finance Research Letters, Elsevier, vol. 27(C), pages 228-234.
    24. Emna Mnif & Anis Jarboui, 2021. "COVID-19, bitcoin market efficiency, herd behaviour," Review of Behavioral Finance, Emerald Group Publishing Limited, vol. 13(1), pages 69-84, March.
    25. Nan, Zheng & Kaizoji, Taisei, 2019. "Market efficiency of the bitcoin exchange rate: Weak and semi-strong form tests with the spot, futures and forward foreign exchange rates," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 273-281.
    26. Shahzad, Syed Jawad Hussain & Bouri, Elie & Kayani, Ghulam Mujtaba & Nasir, Rana Muhammad & Kristoufek, Ladislav, 2020. "Are clean energy stocks efficient? Asymmetric multifractal scaling behaviour," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    27. Sudhanshu Pani, 2021. "Liquidity in high resolution in limit order markets," International Journal of Financial Markets and Derivatives, Inderscience Enterprises Ltd, vol. 8(1), pages 23-49.
    28. Yousra Trichilli & Mouna Abdelhédi & Mouna Boujelbène Abbes, 2020. "The thermal optimal path model: Does Google search queries help to predict dynamic relationship between investor’s sentiment and indexes returns?," Journal of Asset Management, Palgrave Macmillan, vol. 21(3), pages 261-279, May.
    29. Mnif, Emna & Jarboui, Anis & Mouakhar, Khaireddine, 2020. "How the cryptocurrency market has performed during COVID 19? A multifractal analysis," Finance Research Letters, Elsevier, vol. 36(C).
    30. Robin Hanson, 2003. "Combinatorial Information Market Design," Information Systems Frontiers, Springer, vol. 5(1), pages 107-119, January.
    31. Yao, Can-Zhong & Li, Hong-Yu, 2020. "Time-varying lead–lag structure between investor sentiment and stock market," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    32. Xu, Hai-Chuan & Zhou, Wei-Xing & Sornette, Didier, 2017. "Time-dependent lead-lag relationship between the onshore and offshore Renminbi exchange rates," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 49(C), pages 173-183.
    33. Nadarajah, Saralees & Chu, Jeffrey, 2017. "On the inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 150(C), pages 6-9.
    34. Vidal-Tomás, David & Ibañez, Ana, 2018. "Semi-strong efficiency of Bitcoin," Finance Research Letters, Elsevier, vol. 27(C), pages 259-265.
    35. Laib, Mohamed & Golay, Jean & Telesca, Luciano & Kanevski, Mikhail, 2018. "Multifractal analysis of the time series of daily means of wind speed in complex regions," Chaos, Solitons & Fractals, Elsevier, vol. 109(C), pages 118-127.
    36. Telli, Şahin & Chen, Hongzhuan, 2020. "Multifractal behavior in return and volatility series of Bitcoin and gold in comparison," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    37. Naeem, Muhammad Abubakr & Bouri, Elie & Peng, Zhe & Shahzad, Syed Jawad Hussain & Vo, Xuan Vinh, 2021. "Asymmetric efficiency of cryptocurrencies during COVID19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    38. Riccardo Blasis & Luca Galati & Alexander Webb & Robert I. Webb, 2023. "Intelligent design: stablecoins (in)stability and collateral during market turbulence," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.
    39. Yang, Yan-Hong & Shao, Ying-Hui, 2020. "Time-dependent lead-lag relationships between the VIX and VIX futures markets," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
    40. Akbas, Ferhat & Armstrong, Will J. & Sorescu, Sorin & Subrahmanyam, Avanidhar, 2016. "Capital Market Efficiency and Arbitrage Efficacy," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 51(2), pages 387-413, April.
    41. Kim, Jae H., 2009. "Automatic variance ratio test under conditional heteroskedasticity," Finance Research Letters, Elsevier, vol. 6(3), pages 179-185, September.
    42. Stefan Loesch & Nate Hindman & Mark B Richardson & Nicholas Welch, 2021. "Impermanent Loss in Uniswap v3," Papers 2111.09192, arXiv.org.
    43. Cajueiro, Daniel O. & Tabak, Benjamin M., 2007. "Long-range dependence and multifractality in the term structure of LIBOR interest rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 373(C), pages 603-614.
    44. Wen, Fenghua & Tong, Xi & Ren, Xiaohang, 2022. "Gold or Bitcoin, which is the safe haven during the COVID-19 pandemic?," International Review of Financial Analysis, Elsevier, vol. 81(C).
    45. Cao, Guangxi & Cao, Jie & Xu, Longbing, 2013. "Asymmetric multifractal scaling behavior in the Chinese stock market: Based on asymmetric MF-DFA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(4), pages 797-807.
    46. Kantelhardt, Jan W. & Zschiegner, Stephan A. & Koscielny-Bunde, Eva & Havlin, Shlomo & Bunde, Armin & Stanley, H.Eugene, 2002. "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 87-114.
    47. Ante, Lennart & Fiedler, Ingo & Strehle, Elias, 2021. "The influence of stablecoin issuances on cryptocurrency markets," Finance Research Letters, Elsevier, vol. 41(C).
    48. Wang, Yudong & Liu, Li & Gu, Rongbao, 2009. "Analysis of efficiency for Shenzhen stock market based on multifractal detrended fluctuation analysis," International Review of Financial Analysis, Elsevier, vol. 18(5), pages 271-276, December.
    49. Lioba Heimbach & Eric Schertenleib & Roger Wattenhofer, 2022. "Risks and Returns of Uniswap V3 Liquidity Providers," Papers 2205.08904, arXiv.org, revised Sep 2022.
    50. Gong, Chen-Chen & Ji, Shen-Dan & Su, Li-Ling & Li, Sai-Ping & Ren, Fei, 2016. "The lead–lag relationship between stock index and stock index futures: A thermal optimal path method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 63-72.
    51. Parameswaran Gopikrishnan & Martin Meyer & Luis A Nunes Amaral & H Eugene Stanley, 1998. "Inverse Cubic Law for the Probability Distribution of Stock Price Variations," Papers cond-mat/9803374, arXiv.org, revised May 1998.
    52. Guo, Kun & Sun, Yi & Qian, Xin, 2017. "Can investor sentiment be used to predict the stock price? Dynamic analysis based on China stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 390-396.
    53. Bhaskar Krishnamachari & Qi Feng & Eugenio Grippo, 2021. "Dynamic Curves for Decentralized Autonomous Cryptocurrency Exchanges," Papers 2101.02778, arXiv.org.
    54. Ye Wang & Yan Chen & Haotian Wu & Liyi Zhou & Shuiguang Deng & Roger Wattenhofer, 2021. "Cyclic Arbitrage in Decentralized Exchanges," Papers 2105.02784, arXiv.org, revised Jan 2022.
    55. E. Mnif & A. Jarboui & K. Mouakhar, 2020. "How the Cryptocurrency Market Has Performed during COVID 19? A Multifractal Analysis," Post-Print hal-04457136, HAL.
    56. P. Gopikrishnan & M. Meyer & L.A.N. Amaral & H.E. Stanley, 1998. "Inverse cubic law for the distribution of stock price variations," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 3(2), pages 139-140, July.
    57. Ibikunle, Gbenga & McGroarty, Frank & Rzayev, Khaladdin, 2020. "More heat than light: Investor attention and bitcoin price discovery," International Review of Financial Analysis, Elsevier, vol. 69(C).
    58. Cheng, Qing & Liu, Xinyuan & Zhu, Xiaowu, 2019. "Cryptocurrency momentum effect: DFA and MF-DFA analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    59. Andreas A. Aigner & Gurvinder Dhaliwal, 2021. "UNISWAP: Impermanent Loss and Risk Profile of a Liquidity Provider," Papers 2106.14404, arXiv.org.
    Full references (including those not matched with items on IDEAS)

    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. Aslam, Faheem & Memon, Bilal Ahmed & Hunjra, Ahmed Imran & Bouri, Elie, 2023. "The dynamics of market efficiency of major cryptocurrencies," Global Finance Journal, Elsevier, vol. 58(C).
    2. Naeem, Muhammad Abubakr & Bouri, Elie & Peng, Zhe & Shahzad, Syed Jawad Hussain & Vo, Xuan Vinh, 2021. "Asymmetric efficiency of cryptocurrencies during COVID19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    3. Naeem, Muhammad Abubakr & Farid, Saqib & Ferrer, Román & Shahzad, Syed Jawad Hussain, 2021. "Comparative efficiency of green and conventional bonds pre- and during COVID-19: An asymmetric multifractal detrended fluctuation analysis," Energy Policy, Elsevier, vol. 153(C).
    4. Nikolaos A. Kyriazis, 2019. "A Survey on Efficiency and Profitable Trading Opportunities in Cryptocurrency Markets," JRFM, MDPI, vol. 12(2), pages 1-17, April.
    5. Assaf, Ata & Mokni, Khaled & Yousaf, Imran & Bhandari, Avishek, 2023. "Long memory in the high frequency cryptocurrency markets using fractal connectivity analysis: The impact of COVID-19," Research in International Business and Finance, Elsevier, vol. 64(C).
    6. Yao, Can-Zhong & Mo, Yi-Na & Zhang, Ze-Kun, 2021. "A study of the efficiency of the Chinese clean energy stock market and its correlation with the crude oil market based on an asymmetric multifractal scaling behavior analysis," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    7. Abdullah, Mohammad & Chowdhury, Mohammad Ashraful Ferdous & Sulong, Zunaidah, 2023. "Asymmetric efficiency and connectedness among green stocks, halal tourism stocks, cryptocurrencies, and commodities: Portfolio hedging implications," Resources Policy, Elsevier, vol. 81(C).
    8. Cristiana Vaz & Rui Pascoal & Helder Sebastião, 2021. "Price Appreciation and Roughness Duality in Bitcoin: A Multifractal Analysis," Mathematics, MDPI, vol. 9(17), pages 1-18, August.
    9. Carmen López-Martín & Sonia Benito Muela & Raquel Arguedas, 2021. "Efficiency in cryptocurrency markets: new evidence," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(3), pages 403-431, September.
    10. Flori, Andrea, 2019. "News and subjective beliefs: A Bayesian approach to Bitcoin investments," Research in International Business and Finance, Elsevier, vol. 50(C), pages 336-356.
    11. Manahov, Viktor & Urquhart, Andrew, 2021. "The efficiency of Bitcoin: A strongly typed genetic programming approach to smart electronic Bitcoin markets," International Review of Financial Analysis, Elsevier, vol. 73(C).
    12. Kakinaka, Shinji & Umeno, Ken, 2022. "Cryptocurrency market efficiency in short- and long-term horizons during COVID-19: An asymmetric multifractal analysis approach," Finance Research Letters, Elsevier, vol. 46(PA).
    13. Shrestha, Keshab & Naysary, Babak & Philip, Sheena Sara Suresh, 2023. "Fintech market efficiency: A multifractal detrended fluctuation analysis," Finance Research Letters, Elsevier, vol. 54(C).
    14. Yao, Can-Zhong & Li, Hong-Yu, 2020. "Time-varying lead–lag structure between investor sentiment and stock market," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    15. Assaf, Ata & Bhandari, Avishek & Charif, Husni & Demir, Ender, 2022. "Multivariate long memory structure in the cryptocurrency market: The impact of COVID-19," International Review of Financial Analysis, Elsevier, vol. 82(C).
    16. Naseem Al Rahahleh & Ahmed Al Qurashi, 2024. "The impact of COVID-19 on Ethereum returns and Ethereum market efficiency," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 14(3), pages 729-755, September.
    17. Haider Ali & Faheem Aslam & Paulo Ferreira, 2021. "Modeling Dynamic Multifractal Efficiency of US Electricity Market," Energies, MDPI, vol. 14(19), pages 1-16, September.
    18. Chowdhury, Mohammad Ashraful Ferdous & Abdullah, Mohammad & Alam, Masud & Abedin, Mohammad Zoynul & Shi, Baofeng, 2023. "NFTs, DeFi, and other assets efficiency and volatility dynamics: An asymmetric multifractality analysis," International Review of Financial Analysis, Elsevier, vol. 87(C).
    19. Mensi, Walid & Sensoy, Ahmet & Vo, Xuan Vinh & Kang, Sang Hoon, 2022. "Pricing efficiency and asymmetric multifractality of major asset classes before and during COVID-19 crisis," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    20. Andrea Flori, 2019. "Cryptocurrencies In Finance: Review And Applications," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(05), pages 1-22, August.

    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:eee:jebusi:v:133:y:2025:i:c:s0148619524000663. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/journal-of-economics-and-business .

    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.