Blockchain Papers

Laura Molero González, Roy Cerqueti, Raffaele Mattera, M.A. Sánchez-Granero · 5 authors

No abstract is available for this record.

Yuehan Wang

The research paper will focus on the influence of major cryptocurrencies, especially Bitcoin and Ethereum, on world financial markets and traditional financial systems. It looks at how, because of their decentralized nature, these digital assets have brought new dynamics to financial markets in the price of other assets, their volatility, and their means of investment. The research design is of a mixed-methods nature, combining quantitative data from financial market indices with qualitative insights from expert interviews. Some of the main lessons learnt are declining value with other financial assets, the interdependency between movements in crypto assets and other linked assets and disruptions in banking, payments and investment. Besides, there are regulation decisions that should consider the fluctuations of the market and security requirements, as well as the analysis of many initiatives in order to provide sufficient regulation frameworks on the international level. The concluding advice proposed how not only to accommodate the disturbance of current financial stability through innovations but also to integrate the utilization of cryptocurrencies.

Soufiane Benbachir, Karim Amzile, Mohamed Beraich

The rapid growth of decentralized finance (DeFi) has revolutionized the global financial landscape, providing decentralized alternatives to traditional financial services. This study investigates the asymmetric multifractal behavior of nine DeFi markets—AAVE, Pancake Swap (CAKE), Compound (COMP), Curve Finance (CRV), Maker DAO (MKR), Synthetix (SNX), Sushi Swap (SUSHI), UniSwap (UNis), and Yearn Finance (YFI)—using Asymmetrical Multifractal Detrended Fluctuation Analysis (A-MFDA). The use of generalized Hurst exponents, Rényi exponents, and singularity spectrum functions revealed that DeFi markets exhibit multifractal behaviors. The analysis uncovered clear differences between uptrend and downtrend fluctuation functions, highlighting asymmetric multifractal behavior. The asymmetry intensity was analyzed through excess differences in uptrend and downtrend generalized Hurst exponents. AAVE, COMP, SNX, UNis, SUSHI, and MKR exhibit negative asymmetry, with stronger correlations during negative trends. CAKE shifts from positive to negative asymmetry, showing sensitivity to both trends. CRV is more volatile in negative trends, while YFI consistently displays positive asymmetry across market fluctuations. The results also reveal that long-term correlations and heavy-tailed distributions contribute to the multifractality of DeFi assets. This study highlights the need for dynamic risk management in DeFi markets, urging investors to adopt adaptive strategies for volatile assets and prepare for sudden price fluctuations to safeguard investments.

Ismail Adelopo, Xiaojun Luo

Whilst previous studies have primarily focused on the hedge effects and co-movements between cryptos and traditional assets, cryptos’ features that are associated with hedge effects and co-movements have often been neglected in extant studies. This research aims to investigate how specific cryptocurrency features influence their dynamic volatility and co-movements with stock markets. Using cointegration analysis and Granger causality tests, we explore the hedge effects and co-movement between the top 100 cryptos and eight leading stock markets. Additionally, we use logistic regression models to assess the role of crypto-specific features in driving these dynamics. We find that consensus mechanisms and having limited supply are key features influencing co-movements during and after the Covid-19 pandemic, while acting as a means of payment predominantly affects co-movement after the pandemic. We highlight cryptos underlying characteristics and functionalities that could significantly affect their demand and people’s attitudes toward them. Based on finance theory, these differing characteristics could affect cryptos’ versatility thereby impacting their demand, pricing, hedge effects and co-movement in their returns compared to stock returns. This paper makes significant theoretical contributions by addressing the role of crypto features in their co-movements and hedge effects on representative stock markets.

Yang Zhou, Chi Xie, Gang‐Jin Wang, Jue Gong · 5 authors

Abstract Cryptocurrency is a remarkable financial innovation that has affected the financial system in fundamental ways. Its increasingly complex interactions with the conventional financial market make precisely forecasting its volatility increasingly challenging. To this end, we propose a novel framework based on the evolving multiscale graph neural network (EMGNN). Specifically, we embed a graph that depicts the interactions between the cryptocurrency and conventional financial markets into the predictive process. Furthermore, we employ hierarchical evolving graph structure learners to model the dynamic and scale-specific interactions. We also evaluate our framework’s robustness and discuss its interpretability by extracting the learned graph structure. The empirical results show that (i) cryptocurrency volatility is not isolated from the conventional market, and the embedded graph can provide effective information for prediction; (ii) the EMGNN-based forecasting framework generally yields outstanding and robust performance in terms of multiple volatility estimators, cryptocurrency samples, forecasting horizons, and evaluation criteria; and (iii) the graph structure in the predictive process varies over time and scales and is well captured by our framework. Overall, our work provides new insights into risk management for market participants and into policy formulation for authorities.

Steve Springer Laryea, Kofi Agyarko Ababio, Jules Clément, Marno Booyens

This paper aims to investigate investors’ prospects in adding value to their portfolios by considering investors’ behavioural score (Cumulative Prospect Theory (CPT) score) and a clustering technique in the selection of assets. The universe of assets constitutes 63 cryptocurrencies sourced from Bloomberg from Jan 01, 2020, to July 31, 2022. The study period was segmented into two distinct and mutually exclusive periods, namely COVID-19, and post-COVID-19. Nine portfolios were constructed of which six were based on the CPT and the remaining on the K means Clustering technique. Using the copula-based Differential Evolution (DE) algorithm for the optimisation, the results show that portfolios consisting of assets with extremely high CPT scores were preferred during the post-COVID-19 and full sample periods, except for portfolios comprising assets with extremely low CPT scores during the COVID-19 period. The most optimised portfolio was composed of classified assets with extremely high CPT scores in the post-COVID-19 period. These findings provide intuitive and coherent investment strategies to guide investors in the cryptocurrency market.

Shavez Mushtaq Qureshi, Atif Saeed, Farooq Ahmad, Asad Rehman Khattak · 7 authors

Our research investigates the predictive performance and robustness of machine learning classification models and technical indicators for algorithmic trading in the volatile cryptocurrency market. The main aim is to identify reliable approaches for informed decision-making and profitable strategy development. With the increasing global adoption of cryptocurrency, robust trading models are essential for navigating its unique challenges and seizing investment opportunities. This study contributes to the field by offering a novel comparison of models, including logistic regression, random forest, and gradient boosting, under different data configurations and resampling techniques to address class imbalance. Historical data from cryptocurrency exchanges and data aggregators is collected, preprocessed, and used to train and evaluate these models. The impact of class imbalance, resampling techniques, and hyperparameter tuning on model performance is investigated. By analyzing historical cryptocurrency data, the methodology emphasizes hyperparameter tuning and backtesting, ensuring realistic model assessment. Results highlight the importance of addressing class imbalance and identify consistently outperforming models such as random forest, XGBoost, and gradient boosting. Our findings demonstrate that these models outperform others, indicating promising avenues for future research, particularly in sentiment analysis, reinforcement learning, and deep learning. This study provides valuable guidance for navigating the complex landscape of algorithmic trading in cryptocurrencies. By leveraging the findings and recommendations presented, practitioners can develop more robust and profitable trading strategies tailored to the unique characteristics of this emerging market.

Sudip Giri, Dongping Du, Mario G. Beruvides

Non-fungible tokens (NFTs) have gained mainstream attention in the fintech community, but there is little research on their statistical properties. This study investigates the long-memory characteristics of NFT returns and volatility, focusing on their potential for predicting price movements. As NFTs do not conform to traditional models, understanding their unique features is crucial for comprehending complex market dynamics. This study aims to reveal the impact of macroeconomic factors on NFT prices, understand their correlation and develop predictive models using autoregression and artificial intelligence (AI) technology. This research utilized datasets from the Centers for Disease Control and Prevention (CDC), U.S. Bureau of Labor Statistics, Bureau of Economic Analysis, Christie’s, Dune, and Google Trends. Correlation and p value tests revealed strong relationships between NFT prices and variables such as weekly volume, pandemics, inflation and security. The Baseline Model using autoregression with NFT volume, security and technology factors outperformed all other models demonstrating the speculative volatility of NFTs. The Transformer Model using transformers, an architecture used by ChatGPT, Gemini and Stable Diffusion, showed high accuracy with less feature selection and preprocessing efforts. This study provides a novelty using a systematic approach for researchers to perform financial forecasting and contributes to the scarce literature on NFTs. This research offers valuable insights to investors and private agents regarding the right economic conditions for NFT investments by reducing portfolio risks and making informed decisions. To the authors’ best knowledge, this is the first study to utilize time-series transformers for forecasting NFTs based on macroeconomic factors.

Stamatis Papangelou, Klitos Christodoulou, Antonios Inglezakis

Decentralization is a core principle of blockchain technology and Decentralized Autonomous Organizations (DAOs), enhancing security and resilience by distributing control across a network. Traditional metrics like the Gini coefficient and Nakamoto coefficient often fall short in capturing the complex dynamics of decentralization. This paper introduces the Apokedro decentralization index, a metric that evaluates decentralization by considering the probabilities of all possible subsets of nodes that could collectively centralize control. These concepts from game theory, such as the Nash equilibrium, and the Apokedro index, when incorporated, provide a nuanced assessment of centralization risks. Key contributions include the mathematical formulation of the index, an efficient computational algorithm utilizing pruning techniques, and benchmarking experiments that compare the index performance against traditional metrics across various statistical distributions. The Apokedro index offers a comprehensive tool for measuring decentralization in blockchain networks and DAOs.

Ahmed Bouteska, Taimur Sharif, Layal Isskandarani, Mohammad Zoynul Abedin

This research investigates how market-wide conditions (macro aspects) and individual cryptocurrency-specific characteristics (micro aspects) influence the efficiency of cryptocurrency markets. Macro aspects encompass the impacts of overall market liquidity, volatility, and global uncertainty events (e.g., the COVID-19 pandemic and geopolitical conflicts) on market efficiency. Micro aspects focus on cryptocurrency-specific attributes, such as liquidity and volatility levels, and their effects on price delays. Our findings reveal that rising liquidity and declining volatility enhance market efficiency at both macro and micro levels. Furthermore, we observe that during the periods of uncertainty, inefficiencies are exacerbated among less liquid and more volatile cryptocurrencies. We propose that the perceived uncertainties and substantial transaction costs associated with cryptocurrencies that lack liquidity and exhibit high volatility act as deterrents, diminishing the eagerness of active traders to participate in arbitrage trading. Consequently, this leads to inefficiencies in the market. The results of this study offer valuable insights for financial market regulators and authorities as well as investors associated with the crypto market, particularly during the times of financial turmoils.

Sana Gaied Chortane, Kamel Naoui

Has the mean-variance framework become obsolete? In this paper, we replace traditional variance–covariance methods of portfolio optimisation with relative Tsallis entropy and mutual information measures. Its goal is to enhance risk management and diversification in complicated finance ecosystems. We utilize the S&P 500 and Bitwise 10 cryptocurrency indices’ daily returns (2019–2024 data) and conduct our analysis to the year 2020 under extreme shocks. Many models were trained with different configurations, like mean-variance (MV), mean-entropy (ME), and mean-mutual information (MI) traders and their corresponding variants, using Sharpe’s ratio, Jensen’s alpha, and entropy value of risk (EVAR). The findings indicate that entropic models outperform conventional models in terms of diversification and, especially, extreme risk management. Because the appropriate normalization conditions often fail to be satisfied, we can informally see that after a recalibration of the effective frontier, we obtain from EVAR an accumulated resilience aspect to these rare events while also observing the great potential of entropy-based models to replicate non-linear dependencies between assets. The results show that models combining entropy and mutual information optimise the gain–loss ratio (GLR), providing stable diversification and improved risk management, while maximising returns in complex and volatile market environments.

Cristian Bucur, Bogdan-George Tudorică, Adela Bârã, Simona‐Vasilica Oprea

This research employs Multifractal Detrended Fluctuation Analysis (MFDFA) to investigate multifractal properties in financial variables, including Bitcoin prices and economic indicators. Spanning 2019–2022, the analysis reveals multifractal scaling not only in Bitcoin prices, but also in economic indicators such as inflation rates and energy commodity prices. The non-linear singularity spectra unveil the multifaceted nature of scaling properties. Temporal analysis exposes intriguing trends in multifractality with implications for market efficiency. Furthermore, correlation analysis unveils connections among multifractal properties. For instance, a positive correlation between oil prices and Bitcoin suggests similar market forces. The log-log plot of fluctuation function Fq versus lag size demonstrates a power-law relationship, characteristic of multifractal systems. The empirical data’s alignment in log-log space suggests self-similarity in the Bitcoin time series, supporting multifractality. The calculated Hurst exponents values suggest varying degrees of multifractality across the years, with 2021 exhibiting the highest degree and 2022 the lowest. Furthermore, an asymmetry index (0.5767) deviating from 0.5 indicates that the multifractal nature of the Bitcoin market is not symmetric. This research enhances risk assessment and portfolio optimization in finance. It challenges the Efficient Market Hypothesis (EMH), emphasizing the significance of MFDFA in comprehending financial market and economic factor’s relationships.

Jyothi Chittineni

The study aims to understand the interconnectedness and interdependence of cryptocurrency with global uncertainties. The study employs quantile regression and Markov regime-switching models to understand the time-varying connectedness between the cryptocurrency market and uncertainties. The study findings reveal that geopolitical risk positively influences cryptocurrency returns at all quantiles, highlighting the significance of understanding geopolitical risk before considering the investments in cryptocurrency market. On the other hand, economic policy uncertainty negatively affects with the returns during economic expansions and at higher quantiles. Cryptocurrency market is independent of gold price volatility and oil price volatility significantly reduces cryptocurrency returns. The results suggest that cryptocurrency investments are attractive during geopolitical uncertainties, they are unfavourably affected by economic policy uncertainty and oil price volatility, reflecting complex investor behaviours.Copyright© 2025 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.

Ecem Arık

The aim of this research is to investigate the long-term relationships among the dollar exchange rate (TRY/USD), gold (GAU/USD), the Borsa Istanbul 100 Index (BIST 100) and the prices of Bitcoin (BTC/USD), Ethereum (ETH/USD), and Binance Coin (BNB/USD). Since the series contain structural breaks, Fourier unit root tests were used to model the structural breaks. As the method of this study, the relationships between variables in the long term were examined by using Fourier Shin (FSHIN) and Shin (1994) (SHIN) cointegration tests. The findings of this study showed that cryptocurrencies are cointegrated among themselves under structural breaks in the long term; investment instruments are cointegrated among themselves. In addition, as a result of this study, it was determined financial instruments and cryptocurrencies do not move in along over time under structural breaks.

Ehsan Mohammadian Amiri, Akbar Esfahanipour

This study aims to develop a dynamic portfolio trading system for high-risk profiles of cryptocurrencies in two phases: 1) portfolio selection and 2) portfolio construction. In the first phase, we propose a novel algorithmic trading model applying a Convolutional Neural Network (CNN) using a 2-D convolution layer with eight kernels of 3×3 sizes based on the prediction of selected technical indicators to predict buy/sell trading signals. To effectively increase the accuracy of the CNN model, first, the H-step ahead predictions of the selected technical indicators based on Long-short-term-memory (LSTM) along with the indicators themselves have been used to construct input matrices of the CNN model. A new price labeling approach was proposed to determine buying or selling points using the zigzag indicator (ZZ) in our CNN model. Assets with buy signals have been selected to construct the proposed portfolio. In the second phase, we propose a novel robust approach based on Holt-Winters-Multiplicative (HWM) to determine the realized crypto portfolio weights robustly by considering the seasonal effects. The experimental results show that our developed system outperforms the competing models for 30 cryptocurrencies with a high-risk profile in the two phases.

Mohammad Abdullah, Mohammad Ashraful Ferdous Chowdhury, G. M. Wali Ullah

This study inspects the asymmetric tail risk dynamics, efficiency, and interconnectedness among FinTech stocks, cryptocurrencies, and traditional assets. Firstly, we employ the Multifractal-Asymmetric Detrended Cross-Correlation Analysis to examine the cross-correlation patterns and efficiency dynamics of the analyzed assets. The findings reveal asymmetries in cross-correlations and the presence of multifractality, highlighting the nonlinear relationships among these assets and find FinTech assets are the most efficient. Secondly, we utilize the time domain quantile connectedness method to investigate tail risk connectedness, offering insights into the network's shock transmission and spillover effects. Our analysis identifies the major risk transmitters (FinTech stocks) and receivers (bond), emphasizing the interconnectedness of the assets. Additionally, the study conducts bivariate portfolio analysis, considering short and long investment horizons, to guide asset allocation and hedging strategies. Our findings have significant implications for facilitating informed investment strategies and improving the stability and resilience of financial markets.

Giuseppe Pernagallo

Abstract Market efficiency assumes that prices in financial markets are perfectly informative and, therefore, it is not possible to design trading strategies that outperform the market. The concept of efficiency has important implications for financial stability and, consequently, for financial policies. If asset returns exhibit persistent or anti-persistent behavior, then predictability based on past returns might be possible, which would be a clear violation of the weak form of efficiency. Many studies rely on the Hurst exponent to evaluate the level of memory of financial returns, and the purpose of this paper is to show that long memory or anti-persistence of financial returns is not incompatible with the random walk model or the efficient market hypothesis (EMH). The use of the Hurst exponent to demonstrate the inefficiency of financial markets using common estimators is troublesome, especially when applied to financial returns, since values of $$\hat{H} \ne 0.5$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mover> <mml:mi>H</mml:mi> <mml:mo>^</mml:mo> </mml:mover> <mml:mo>≠</mml:mo> <mml:mn>0.5</mml:mn> </mml:mrow> </mml:math> are not evidence against the random walk model or the EMH. Moreover, the high variability of Hurst exponent estimates and their dependence on the chosen algorithm should motivate careful use of this tool. This study proposes a simple theoretical explanation and an extensive simulation study to show that $$\hat{H} \ne 0.5$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mover> <mml:mi>H</mml:mi> <mml:mo>^</mml:mo> </mml:mover> <mml:mo>≠</mml:mo> <mml:mn>0.5</mml:mn> </mml:mrow> </mml:math> for financial returns is perfectly compatible with the random walk model. As a robustness check, both the traditional rescaled range and the wavelet lifting algorithms are used. Applications to real data are also discussed to show that the empirical values of the Hurst exponent are in the range suggested by the simulations, providing evidence that over-reliance on the Hurst exponent could lead to erroneous rejection of the random walk model. Specifically, the paper presents an application to the daily returns of stock market indices (DJIA and S&amp;P 500) over a period of more than 30 years and cryptocurrencies (Bitcoin and Ethereum) over a period of more than 5 years.

Saralees Nadarajah, Jules Clément, Patrick Rakotomarolahy, Henri T. J. E. Ratolojanahary

The purpose of this study is to conduct an empirical comparative study of volatility models for three of the most popular cryptocurrencies. We study the volatility of the following cryptocurrencies: Bitcoin, Ethereum, and Litecoin. We consider the GARCH-type, boosting-family-tree-based ensemble learning, and ANFIS volatility models for these financial crypto-assets, which some have claimed capture stylized facts about cryptocurrency volatility well. We conduct comparative studies on in-sample and out-of-sample empirical analyses. The results show that tree-based ensemble learning delivers better forecast accuracy. Nevertheless, the performance of some GARCH-type volatility models is relatively close to that of the best model on both training and evaluation samples.

M. Venturini, Daniel García-Costa, Elena Álvarez-García, Francisco Grimaldo · 5 authors

Cryptocurrencies have recently been in the spotlight of public debate due to their embrace by the new US President, with crypto fans expecting a 'bull run'. The global cryptocurrency market capitalisation is more than \$3.50 trillion, with 1 Bitcoin exchanging for more than \$97,000 at the end of November 2024. Monitoring the evolution of these systems is key to understanding whether the popular perception of cryptocurrencies as a new, sustainable economic infrastructure is well-founded. In this paper, we have reconstructed the network structures and dynamics of Bitcoin from its launch in January 2009 to December 2023 and identified its key evolutionary phases. Our results show that network centralisation and wealth concentration increased from the very early years, following a richer-get-richer mechanism. This trend was endogenous to the system, beyond any subsequent institutional or exogenous influence. The evolution of Bitcoin is characterised by three periods, Exploration, Adaptation and Maturity, with substantial coherent network patterns. Our findings suggest that Bitcoin is a highly centralised structure, with high levels of wealth inequality and internally crystallised power dynamics, which may have negative implications for its long-term sustainability.

Viktor Manahov, Mingnan Li

We explore volatility spillover effects between mainstream cryptocurrencies and energy token markets in the 120 days following three notable Blockchain bridge heists in 2022. Using the DCC-GARCH model, we find significant spillover effects between Bitcoin, Ethereum, and energy tokens like Power Ledger Token and Energy Web Token post-heists. This indicates heightened investor concern and panic trading impacting cryptocurrencies and energy token markets. Our analysis also reveals a herding behaviour in energy tokens under market stress and increased liquidity issues, leading to broader market quality deterioration. Based on these findings, we propose regulatory enhancements and the ‘Energy Future Fund’ to support the stability and growth of energy token markets.

Essa Al-Mansouri, Ahmet Faruk Aysan, Ruslan Nagayev

This paper examines Bitcoin’s viability as money through the lens of its risk profile, with a particular focus on its store of value function. We employ a suite of wavelet techniques, including Wavelet Transform (WT), Wavelet Transform Coherence (WTC), Multiple Wavelet Coherence (MWC), and Partial Wavelet Coherence (PWC), to decompose the risk structure of Bitcoin and analyze its relationship with various systematic risk factors. Our dataset spans from 13 August 2015 to 29 June 2024, and includes Bitcoin, major commodities, global and US equities, Shari’ah-compliant equities, Ethereum, and the Secured Overnight Financing Rate (SOFR). We find that Bitcoin’s risk profile is increasingly aligned with traditional financial assets, indicating growing market integration. While Bitcoin exhibits high volatility, a significant portion of this volatility can be attributed to systematic rather than idiosyncratic factors. This suggests that Bitcoin’s risk may be more diversifiable than previously thought. Our findings have important implications for monetary policy and financial regulation, challenging the notion that Bitcoin’s volatility precludes its use as money and suggesting that regulatory approaches should consider Bitcoin’s evolving risk characteristics and increasing integration with broader financial markets.

Bentzion Szrajber, Ilan Alon, Shalom Levy

The purpose of this article is to study analysis the Decentralized Finance (DeFi) literature. By synthesizing the themes and theorical frameworks, we aim to identify knowledge gaps and potential areas for future research in the DeFi landscape. We conduct bibliometric and content analysis on a corpus of 275 articles extracted from the Web of Science and Scopus databases. We use the Bibliometrix package in R software to apply co-citation and bibliographic coupling. We find three research clusters (a) socioeconomic (b) technology and (c) financial with their conceptual structure, interactions and transformations. Applying both co-citation and bibliographic coupling network analysis yields a dynamic view of the field tracing thematic evolution from its inception to the present day, revealing a decline in academic interest in DeFi security vulnerabilities in contrast to the growing emphasis on social media's influence on DeFi prices.

Walid Mensi, Ramzi Nekhili, Xuan Vinh Vo, Sang Hoon Kang

ABSTRACT This paper examines the hourly downward/upward multifractality and dynamic efficiency of four cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Litecoin (LTC)— before and during the COVID‐19 pandemic, and during the Russia–Ukraine tension. Using the asymmetric multifractal detrended fluctuation analysis method, the results show significant asymmetric multifractality in all series, which intensifies for BTC only throughout the COVID‐19 crisis and narrows for ETH, XRP, and LTC. Moreover, we show that cryptocurrency markets are more inefficient during the upward (downward) trend and before (during) the COVID‐19 crisis. LTC is the least inefficient market pre COVID‐19, whereas XRP is the least inefficient during the pandemic crisis. The results show evidence of excessive asymmetric multifractality for all four crypto markets. Before the COVID‐19 crisis, positive values of excess asymmetry in multifractality have been identified for BTC and LTC markets, whereas the excess asymmetry values were negative for ETH and XRP markets. BTC and ETH markets showed wider multifractality fluctuations compared to LTC and XRP, indicating a stronger reaction to the war's impact.

Luís Pedro Freitas, Jorge Cerdeira, Diogo Lourenço

The rise of cryptocurrencies over the past decade has promised to challenge the dominance of fiat money systems and reshape monetary policy. However, recent developments, including market volatility and the collapse of key exchanges like FTX, have eroded public trust, raising skepticism of a feasible transition to a crypto-based monetary system. This paper explores why cryptocurrencies have not met the expectations of their proponents, particularly those who saw them as a step towards Friedrich Hayek’s vision for competitive currency issuance. While cryptocurrencies reflect some aspects of Hayek’s model, their instability—especially in Bitcoin-like assets—undermines their role as a reliable alternative to fiat money. The paper also considers how central bank independence and regulatory gaps further hinder the development of a robust cryptocurrency framework. Despite the continued relevance of Hayek’s ideas in today’s monetary landscape, the entrenched structures of modern central banks and the rise of Central Bank Digital Currencies suggest that a decentralised currency order remains unlikely in the near future.