Author
Listed:
- Juan Jesús Rico-Peña
(National Distance Education University (UNED))
- Raquel Arguedas-Sanz
(National Distance Education University (UNED))
- Carmen López-Martín
(National Distance Education University (UNED))
Abstract
Blockchain transactions market is expected to gain momentum in the coming years, in a context with gradual transition to fee-regime in many cryptocurrencies, the expansion of blockchain technology to different business areas and the requirement for high transaction throughput in new blockchain-based applications. In this context, the economic assessment of the supply and demand curves defining the transactions market is key to recognize its potential weaknesses, but also to minimize risks and to improve operational efficiency when designing or upgrading blockchain-based applications. The research covers a gap by conducting an empirical analysis of these curves in Bitcoin, based on an extensive dataset spanning two time periods in 2021 and 2023 selected as having different levels of Mempool congestion. The empirical findings support the understanding of why Bitcoin transaction fees are subject to high volatility and how the protocol evolutions made to date have shifted the supply curve. The demand curve, although relatively elastic around the equilibrium price, turns out to be extremely dynamic. On the other hand, the inelasticity of the supply curve, described by an exponential distribution, explains the sharp variations in transaction fees observed under demand shocks. The results bring to light the importance of the transactions market layout as an element of control over the intrinsic problems associated with an imperfect competition, whereas additional sustainability and security aspects beyond pure economic considerations are to be taken into account in the design of this market.
Suggested Citation
Juan Jesús Rico-Peña & Raquel Arguedas-Sanz & Carmen López-Martín, 2025.
"Transactions Market in Bitcoin: Empirical Analysis of the Demand and Supply Block Space Curves,"
Computational Economics, Springer;Society for Computational Economics, vol. 66(4), pages 3327-3357, October.
Handle:
RePEc:kap:compec:v:66:y:2025:i:4:d:10.1007_s10614-024-10775-2
DOI: 10.1007/s10614-024-10775-2
Download full text from publisher
As the access to this document is restricted, you may want to
for a different version of it.
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:kap:compec:v:66:y:2025:i:4:d:10.1007_s10614-024-10775-2. 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.
We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.