The speculative financial services sector and the systemic failures of financial globalisation have prompted a growing call for delinking from the hegemony of the US dollar. These systemic failures have been illustrated by financial mismanagement and the financial crisis of 2007-8. This event paints a dire picture of the consequences associated with poor regulation of the financial sector. This study seeks to interpret these events in terms of Gramsciâs Prison Notebooks and his concept of the âmorbid symptoms of the interregnumâ. According to Babic (2020), these occur when a hegemony and its institutions are âdyingâ, thereby hampering their power. This notion also highlights that the future remains murky, despite public calls for change. Gramsci calls this the ânew that cannot be bornâ.This study seeks to extend this framework to the current state of the dollar hegemony. The âmorbid symptomsâ we will examine include the call to delink from the speculative high-risk US economy, the emergence of cryptocurrencies, and the politics surrounding fiat money. Some have argued that the emergence of cryptocurrencies could be the ânewâ. However, this study argues that the negative characteristics associated with cryptocurrencies such as cybersecurity concerns and price volatility create ambiguity about whether cryptocurrency is the ideal trajectory. Therefore, it argues that ambiguity and calls for change qualify our current monetary situation to be classified as a Gramscian interregnum.It also examines the current fiat money discourse by highlighting how the dollar hegemony may be succeeded by another fiat currency. However, it argues that the inflationary shortfalls of fiat money make this a fallible option, thereby perpetuating the ambiguities within the Gramscian interregnum.
Purpose : This study focused on evaluating the efficiency of the global cryptocurrency market under the efficient market hypothesis.Methodology : The study explored the presence of calendar effects in the form of the day-of-the-week and the weekend effect in the top 25 global cryptocurrencies and a constructed index using autocorrelation corrected ARCH family models on the log-returns of prices. Non-normality is tackled through the use of a non-parametric bootstrapped approach in addition to parametric estimation. Additionally, rolling window regression is employed as a dynamic framework.Findings : The findings of this study showed the presence of calendar effects in terms of higher returns for certain days over others, thereby concluding that the global cryptocurrency market as a whole was not efficient.Practical Implications : As a result of its informational inadequacies, it was implied that cryptocurrency markets were not even efficient in the weak form. This had practical implications for investors, whose aggressive search and trading tactics appear warranted in the context of market anomalies, as well as other market players, like regulators attempting to comprehend the structure of the cryptocurrency market from an efficiency standpoint and researchers attempting to investigate the efficiency-related behavior and psychology of crypto markets.Originality : This study is exceptional in that it uses a sample of cryptocurrencies that objectively covers the larger global cryptocurrency markets over the most extended amount of time possible, making it unique both for the participants in the crypto markets and for its contribution to the literature on market efficiency. The study employed an evolutionary methodology, taking into account the time-varying behavior of financial assets. As far as we can tell, this is one of the first studies to use this methodology and model formulation.
Xi Deng, Huiming Zhu, S X Li, Zishan Huang · 5 authors
This study measures time-frequency liquidity and investigates the quantile connectedness of cryptocurrencies, decentralized finance, and non-fungible tokens (NFTs). The empirical results reveal that high-liquidity cryptocurrencies and yield-farming tokens are the main net spillover transmitters of low-liquidity cryptocurrenciesâ downside networks in the short term. In addition, the connectedness between high-liquidity cryptocurrencies and yield-farming tokens significantly increases in the long-term upside network. Finally, NFTs exhibit substantial risk-bearing abilities.
An Pham Ngoc Nguyen, Tai Tan, Marija Bezbradica, Martin Crane
We employ graph-based methods to examine the connectedness between cryptocurrencies of different market caps over time. By applying denoising and detrending techniques inherited from Random Matrix Theory and the concept of the so-called Market Component, we are able to extract new insights from historical return and volatility time series. Notably, our analysis reveals that changes in volatility-based network structure can be used to identify major events that have, in turn, impacted the cryptocurrency market. Additionally, we find that these structures reflect investorsâ sentiments, including emotions like fear and greed. Using metrics such as PageRank, we discover that certain minor coins unexpectedly exert a disproportionate influence on the market, while the largest cryptocurrencies such as BTC and ETH seem less influential. We suggest that our findings have practical implications for investors in different ways: Firstly, helping them to avoid major market disruptions such as crashes, to safeguard their investments, and to capitalize on opportunities for high returns; Secondly, sharpening and optimizing the portfolios thanks to the understanding of cryptocurrenciesâ connectedness.
Abstract This study employs the Bayesian Networks (BN) and the wavelet coherence approaches to invest the relationship between Bitcoin volatility and financial asset classes (MSCI world equity index, S&P Goldman Sachs Commodity Index [GSCI], US index and Investment Grade Corporate Bond Index ETF [PIMCO]) using daily data for the period from August 2011 to October 2021. The results show that the causal relationship between Bitcoin and other financial assets varies depending on the market states. During the low volatility periods, Bitcoin has a stronger impact on the GSCI, while during the stability periods, it has a direct effect on the US index and the MSCI world index. In contrast, during high volatility periods, Bitcoin has a direct impact on both the GSCI and PIMCO indices. The key findings enabled us to provide implications for US investors to promote asset allocation and risk management covering both Bitcoin and traditional financial markets. The results suggest that policymakers should watch Botcoin closely to preserve financial stability.
PaweĆ SzydĆo, Marcin WÄ torek, JarosĆaw KwapieĆ, StanisĆaw DroĆŒdĆŒ
A non-fungible token (NFT) market is a new trading invention based on the blockchain technology, which parallels the cryptocurrency market. In the present work, we study capitalization, floor price, the number of transactions, the inter-transaction times, and the transaction volume value of a few selected popular token collections. The results show that the fluctuations of all these quantities are characterized by heavy-tailed probability distribution functions, in most cases well described by the stretched exponentials, with a trace of power-law scaling at times, long-range memory, persistence, and in several cases even the fractal organization of fluctuations, mostly restricted to the larger fluctuations, however. We conclude that the NFT market-even though young and governed by somewhat different mechanisms of trading-shares several statistical properties with the regular financial markets. However, some differences are visible in the specific quantitative indicators.
Accurate cryptocurrency price prediction is essential to investors and researchers for analyzing trends and advising financial decisions, as price prediction is fundamental to making beneficial investment decisions. Due to the high volatility and unpredictability of the cryptocurrency market, it is difficult to predict these prices based on cryptocurrency time series data accurately. This research paper presents a two-fold analysis of the effectiveness of neural networks and deep learning to predict cryptocurrency prices and proposes a novel approach to cryptocurrency price prediction. This is done by considering Long-Short Term Memory (LSTM) and Transformer neural networks that use historical price features in addition to volatility and momentum technical indicators, along with historical price features, and testing these models on Bitcoin (BTC), Ethereum (ETH) and Litecoin (LTC). Momentum and volatility technical indicators such as Relative Strength Index (RSI), Bollinger Bands %B and Moving Average Convergence/Divergence (MACD) are not commonly used in cryptocurrency machine learning models. Still, the addition of these features can give better insight into the general trend of the price. By adding volatility and momentum features to our LSTM and Transformer models, we see a significant increase in price prediction accuracy, and we also find that Transformers tend to outperform LSTM models in price prediction and trends of cryptocurrency data.
Minority game theory has, traditionally, been used to simulate player behavior under various conditions in a number of stock markets. However, digital markets, such as cryptocurrencies, have been largely ignored by game theory models. Using a comparative approach, with data both from traditional equities markets and from Bitcoin this article presents a model of a dollar game and compares its outcome to real-life data. The paper aims to prove that game theory can be used to predict cryptocurrency markets similarly to how it is used to predict traditional stock markets. By using historical data from the London Stock Exchange and Bitcoin the paper demonstrates that a custom implementation of a dollar game can be used to predict general market trends and the overall impact of short-term investments in Bitcoin.
This paper investigates the persistence in the cryptocurrency market, focusing on five distinct groups categorized by their market capitalization during the sample period from 2020 to 2023. The study aims to test two hypotheses: (H1) The degree of persistence in the cryptocurrency market is contingent on market capitalization, and (H2) The efficiency of the cryptocurrency market has increased in recent years. The methodology employed for this examination is R/S analysis. The results indicate that the cryptocurrency market maintains its inefficiency, and no significant variations in persistence are discerned among different cryptocurrency groups, leading to the rejection of H1. Outcomes related to H2 present a nuanced scenario. Specifically, Litecoin and Ripple exhibit supportive evidence for the Adaptive Market Hypothesis, suggesting an improvement in the efficiency of the cryptocurrency market in recent years. A noteworthy revelation pertains to the anomaly observed in Bitcoin. Despite being the most capitalized and liquid cryptocurrency, it demonstrates inefficiency akin to levels observed five years ago. The implications of this study contribute to the comprehension of cryptocurrency market efficiency. The findings challenge the assumptions of the Efficient Market Hypothesis, favoring instead the Adaptive Market Hypothesis. For practitioners, the results hold significance, providing evidence of price predictability, particularly in the case of Bitcoin. This suggests that trend trading strategies remain viable for generating abnormal profits in the cryptocurrency market. Acknowledgments Alex Plastun gratefully acknowledges financial support from the Ministry of Education and Science of Ukraine (0121U100473).
This paper proposes a new investment strategy in the cryptocurrency market based on a two-step procedure. The first step is the computation of the asset's levels of efficiency in an universe of cryptocurrencies. Price returns efficiency degrees are measured by their corresponding levels of multifractality, obtained by the multifractal detrended fluctuation analysis method. The higher the multifractality, the higher the inefficiency in terms of the weak form of market efficiency. Cryptocurrencies are then ranked in terms of efficiency. The second step is the construction of portfolios under the Markowitz framework composed of the most/least efficient digital coins. Minimum variance, maximum Sharpe ratio, equally weighted and (in)efficient-based portfolios were considered. The former strategy is also proposed, where the weights are computed proportionally to the assets levels of (in)efficiency. The main findings are: cryptocurrency price returns are multifractal and their levels of (in)efficiency change over time; returns exhibit left-sided asymmetry, which implies that subsets of large fluctuations contribute substantially to the multifractal spectrum; in bull markets portfolios with the least efficiency assets provided a better riskâreturn relation; in periods of high volatility and high price depreciation (bear market) a better performance is associated with the portfolios composed by the more efficient cryptocurrencies.
Decentralized Finance (DeFi) aims to use advancements in both computation and cryptography to tackle economic problems. Therefore, it must operate within the intersection of constraints from both the computer science and economic domains. We explore a foundational question at the junction of those fields: Can we synthesize variable market-clearing risk-free yield for native tokens via smart contracts? We use a stylized model representing a large class of decentralized consensus algorithms to show this is impossible. This undecidability result places bounds on what decentralized financial products can be built and constrains the shape of future developments in DeFi. Among other limitations, our results reveal that markets in DeFi are incomplete.
This paper reports our findings on the return dynamics of Bitcoin and Ethereum using high-frequency data (minute-by-minute observations) from 2015 to 2022 for Bitcoin and from 2016 to 2022 for Ethereum. The main objective of modeling these two series was to obtain a dynamic estimation of risk premium with the intention of characterizing its behavior. To this end, we estimated the Generalized Autoregressive Conditional Heteroskedasticity in Mean with Normal-Inverse Gaussian distribution (GARCH-M-NIG) model for the residuals. We also estimated the other parameters of the model and discussed their evolution over time, including the skewness and kurtosis of the Normal-Inverse Gaussian distribution. Similarly, we determined the parameters that define the evolution of the estimated variance, i.e., the parameters related to the fitted past variance, square error and long-term average value. We found that, despite the market uncertainty during the COVID-19 emergency period (2020 and 2021), the selected cryptocurrenciesâ return volatility and kurtosis were even greater for several other subperiods within our sampleâs time frame. Our model represents an analytical tool that estimates the risk premium that should be delivered by Bitcoin and Ethereum and is therefore of interest to risk managers, traders and investors.
Given that technical trading charts are publicly available on popular financial websites such as Bloomberg and MarketWatch, it stands to reason that the same technical trading approaches may be applied to cryptocurrency markets. One of these trading strategies is the variable length moving average (VMA), whose flexibility benefit has not been fully explored in prior research. To fill this gap, we evaluate Bitcoin futures using VMA trading rules and provide the results in a heatmap diagram. This approach allows investors to choose the most effective VMA rules, potentially leading to profits. Furthermore, our approach may shed new light on previously unexplored investment thinking and practices that have the potential to improve investment outcomes.
In an age of rapidly changing technological revolutions, where cryptocurrencies and blockchain play key roles, studying the dynamics of cryptocurrency markets at the government level is becoming an urgent need, which is not just a step into the future, but also an opportunity for countries to act forward, based on data analysis and forecasting global economic trends. Every aspect of cryptocurrency - from financial stability to technological innovation - has the potential to transform the global landscape. Studying the interaction of cryptocurrencies with national interests will not only help to determine the positions of countries in this context, but also formulate effective strategies for managing this rapidly developing economic segment. It is important to realize that those states that integrate cryptocurrency market analysis into their strategies can best adapt to the challenges of the modern world and promote their economic prosperity. The purpose of the research is to study how the introduction of digital money into the economy affects the interest of various countries in participating in trading in the cryptocurrency market. To identify the relationship between the integration of such assets into the economy and the desire of host countries to participate in cryptocurrency markets. Consequently, there is a need to analyze the mechanisms of interaction of large economic entities - states - with cryptocurrencies, as well as predict the likely responses in this context of research. Using panel data analysis, to conduct a study of the dynamics of the cryptocurrency market in the digital finance market using the example of 50 countries around the world. To identify the relationship between the attitudes of countries and the dynamics of the cryptocurrency market in order to suggest possible directions for the future development of the studied evolutionary economic sphere. Materials and methods. As a basis for the study, a balanced and informative set of indexes (17 indexes) was identified, which represents the key variables necessary for a more in-depth analysis of the dynamics of cryptocurrency markets in the context of various countries over a period of ten years (2013-2022). The âCryptocurrency trading volumeâ index was chosen as the effective index. The set of indexes was selected based on their ability to reflect cryptocurrency trading volumes, investor activity, and each countryâs level of involvement in cryptocurrency transactions. The impact of various factors on the volume of transactions with electronic money and digital financial assets was assessed using panel data analysis methods in the Gretl statistical analysis program. Results. As a result of the analysis using the panel data tool, three models were created: a pooled regression model, a fixed-effects model, and a random-effects model. The choice of the best model is made through testing special hypotheses - the Brisch-Pagan test and the Hausman test. The fixed effects model was preferable to the random effects model in this study. The reason is the fixed effects modelâs ability to take into account the individual characteristics of each country in the sample, leading to more accurate results. Based on the study of individual fixed effects, three groups of countries were identified: those that have a positive impact on the volume of cryptocurrency trading (for example, the United States and Japan), countries with a neutral impact (for example, Germany), and countries where individual effects have a negative impact (for example, China and Russia). Conclusion. Overall results indicate that countries with advanced digital infrastructure and ease of use of electronic payments, as well as inflationary and cultural influences, may exhibit higher activity in cryptocurrency markets. Based on the fixed effects model and taking into account assumptions about the dynamics in different countries, general conclusions were formulated regarding the index analyzed in this study - the volume of cryptocurrency trading.
As cryptocurrency is widely used in the financial field, detecting anomalous trading behavior of blockchain-based cryptocurrencies has become increasingly important. Researchers have utilized graph convolutional neural networks (GCN) for detecting anomalous cryptocurrency transactions. However, GCN fails to fully capture the spatial information correlation between neighboring nodes when processing graph data, which limits the utilization of structural features in the model. Therefore, the performance of GCN may be constrained when dealing with complex, high-dimensional graph data. In this paper, we propose a cosine similarity-based graph convolutional neural network for detecting anomalous cryptocurrency transactions. Compared to traditional GCN models, our method can better utilize both the network structure features and spatial information correlation, and it performs better in processing infinite-dimensional graph data. Experimental results show that our model can effectively detect anomalous cryptocurrency transactions, thereby improving the security and reliability of cryptocurrency, and it has good application prospects.
This research paper presents a thorough economic analysis of Bitcoin and its impact. We delve into fundamental principles, and technological evolution into a prominent decentralized digital currency. Analysing Bitcoin's economic dynamics, we explore aspects such as transaction volume, market capitalization, mining activities, and macro trends. Moreover, we investigate Bitcoin's role in economy ecosystem, considering its implications on traditional financial systems, monetary policies, and financial inclusivity. We utilize statistical and analytical tools to assess equilibrium , market behaviour, and economic . Insights from this analysis provide a comprehensive understanding of Bitcoin's economic significance and its transformative potential in shaping the future of global finance. This research contributes to informed decision-making for individuals, institutions, and policymakers navigating the evolving landscape of decentralized finance.
Abstract Cryptocurrencies and Bitcoin, in particular, are prone to wild swings resulting in frequent jumps in prices, making them historically popular for traders to speculate. It is claimed in recent literature that Bitcoin price is influenced by sentiment about the Bitcoin system. Transaction, as well as the popularity, have shown positive evidence as potential drivers of Bitcoin price. This study introduces a bivariate jump-diffusion model to capture the dynamics of Bitcoin prices and the Bitcoin sentiment indicator, integrating trading volumes or Google search trends with Bitcoin price movements. We derive a closed-form solution for the Bitcoin price and the associated BlackâScholes equation for Bitcoin option valuation. The resulting partial differential equation for Bitcoin options is solved using an artificial neural network, and the model is validated with data from highly volatile stocks. We further test the modelâs robustness across a broad spectrum of parameters, comparing the results to those obtained through Monte Carlo simulations. Our findings demonstrate the modelâs practical significance in accurately predicting Bitcoin price movements and option values, providing a reliable tool for traders, analysts, and risk managers in the cryptocurrency market.
Automated Market Makers (AMMs) are major centers of matching liquidity supply and demand in Decentralized Finance. Their functioning relies primarily on the presence of liquidity providers (LPs) incentivized to invest their assets into a liquidity pool. However, the prices at which a pooled asset is traded is often more stale than the prices on centralized and more liquid exchanges. This leads to the LPs suffering losses to arbitrage. This problem is addressed by adapting market prices to trader behavior, captured via the classical market microstructure model of Glosten and Milgrom. In this paper, we propose the first optimal Bayesian and the first model-free data-driven algorithm to optimally track the external price of the asset. The notion of optimality that we use enforces a zero-profit condition on the prices of the market maker, hence the name ZeroSwap. This ensures that the market maker balances losses to informed traders with profits from noise traders. The key property of our approach is the ability to estimate the external market price without the need for price oracles or loss oracles. Our theoretical guarantees on the performance of both these algorithms, ensuring the stability and convergence of their price recommendations, are of independent interest in the theory of reinforcement learning. We empirically demonstrate the robustness of our algorithms to changing market conditions.
In this article, we consider DAG-based distributed ledger technologies (DLTs), i.e., DLTs where each block can reference several previous blocks hence forming a directed acyclic graph of blocks (BDAG). Each block has a weight (usually a constant normalized to one) and our goal is to compute the heaviest sub-BDAG that does not contain conflicting blocks. First, we prove that computing such a sub-BDAG is NP-complete. Then, we show that the difficulty comes from concurrent conflicts and we present an optimal algorithm that is polynomial if the number of concurrent conflicts is bounded. We also give an efficient incremental version of our algorithm. Finally, we evaluate the performance of our algorithm on random BDAGs against an existing algorithm called GHOSTDAG and show that, in addition to being optimal, our algorithm is also more efficient in practice.