The enormous rise of the cryptocurrencies over the last few years has created one of the largest unregulated markets in the world. In this study, we obtain millisecond data for the five major cryptocurrenciesâbitcoin, ethereum, ripple, litecoin and dashâand two cryptocurrency indicesâCrypto Index (CRIX) and CCI30 Crypto Currencies Indexâto investigate the relationship between cryptocurrency liquidity, herding behaviour and profitability during periods of extreme price movements (EPMs). We demonstrate that cryptocurrency traders (CTs) facilitate EPMs and demand liquidity even during the utmost EPMs. We observe the presence of herding behaviour during up markets across the entire dataset. Our robustness checks indicate that herding behaviour follows a dynamic pattern that varies over time with decreasing magnitude. We also provide novel evidence of CTsâ profitability after transaction costs, and demonstrate their strong profitability-generating record in the future.
The success of bitcoin as a medium of exchange poses a problem for the traditional Austrian view on the emergence of money. In particular, it casts doubt on the regression theorem of Ludwig von Mises, which implies that an item cannot gain acceptance as a medium of exchange without government support if it does not first possess some non-monetary use value. We describe the conflict that cryptocurrencies like bitcoin created for the traditional Austrian understanding of the emergence of money and the more recent steps those working in the field have taken to resolve it.
The primary purpose of this article is to conduct the Fourier Nonlinear Unit Root Test to check Purchasing Power Parity (PPP) for seven cryptocurrencies traded in seventeen countries from 2010 to 2021. The unit root test supports the PPP hypothesis when we use each cryptocurrency separately for all the countries. However, the PPP hypothesis is strongly supported when we pool them together by countries and cryptocurrencies. This finding is in line with the power of the test issue of the PPP for regular PPP testing, which also holds good in cryptocurrencies.
Rarity is known to be a factor in the price of non-fungible tokens (NFTs). Most investors make their purchasing decisions based on the rarity score or rarity rank of NFTs. However, not all rare NFTs are associated with a higher price, especially for play-to-earn gaming NFTs. In this paper, we studied the top-ranked play-to-earn gaming NFTs on Axie Infinity. We found that, in addition to rarity, utility is also a significant factor influencing the price. Furthermore, we use utility as a predictor to predict the price of Axies using the XGBoost regressor. Our results reveal that, compared to using rarity-based predictors only, leveraging utility-based predictors can improve the prediction accuracy, thus highlighting utility as a price determinant for play-to-earn gaming NFTs.
TomĂĄĆĄ Ć Ć„astnĂœ, JiĆĂ Koudelka, Diana BĂlkovĂĄ, LuboĆĄ Marek
Cryptocurrencies are a new field of investment opportunities that has experienced a significant growth in the last decade. The crypto market was capitalized at more than USD 3000 bn, having grown from USD 10 m over the period 2011â2021. Generating high returns, investments in cryptocurrencies have also shown high levels of price volatility. By comparing the performance of cryptocurrencies (measured by the crypto index) and standard equities (included in the S&P 500 index), we found that the former has outperformed the latter 14 times over the last two years. In the present paper, we analyzed the 2012â2022 global crypto market developments and main constituents. With a focus on the top 30 cryptocurrencies and their prices, as of 9 April 2022, covering data of the two major market stress eventsâoutbreaks of the COVID-19 pandemic (February 2020) and the Russian invasion of Ukraine (February 2022). We applied the dynamic time warping method including barycentre averaging and k-Shape clustering of time series. The use of the dynamic time warping has been essential for the preparation of data for subsequent clustering and forecasting. In addition, we compared performance of cryptocurrencies and equities. Cryptocurrency time series are rather short, sometimes involving high levels of volatility and including multiple data gaps, whereas equity time series are much longer and well-established. Identifying similarities between them allows analysts to predict crypto prices by considering the evolution of similar equity instruments and their responses to historical events and stress periods. Moreover, we tested various forecasting methods on the 30 cryptocurrencies to compare traditional econometric methods with machine learning approaches.
Cryptocurrency markets have attracted many interest for global investors because of their novelty, wide on-line availability, increasing capitalization, and potential profits. In the econophysics tradition, we show that many of the most available cryptocurrencies have return statistics that do not follow Gaussian distributions, instead following heavy-tailed distributions. Entropy measures are applied, showing that portfolio diversification is a reasonable practice for decreasing return uncertainty.
This paper investigates the price and risk dynamics of Bitcoin. Applying SVAR to study Bitcoin, gold and U.S. dollar in one system, we find that neither the gold nor U.S. dollar can explain Bitcoin pricing dynamics in the short-run. We further apply the DCC-MGARCH model to study the risk correlations. The results show that there exists volatility spillover effect and dynamic correlation between three markets, which is magnified with the advent of COVID-19. We can thus draw a conclusion that the boom of Bitcoin is just a hype and speculative bubble.
Seyram Pearl Kumah, Jones OdeiâMensah, Richmell Baaba Amanamah
This paper investigates the co-movement between cryptocurrencies and African stock returns to uncover their degree of association and global portfolio diversification benefits implementing the three-dimensional continuous Morlet wavelet transform technique. Data span 10 August 2015 to 10 December 2021 at daily frequency. The results suggest high degrees of co-movement between the asset markets at medium and lower frequencies implying that stock markets in Africa are highly exposed to cryptocurrency market disruptions from the medium term and that international investors seeking to hedge their price risk in African stock markets using cryptocurrencies may have to look at the short term. The phase difference arrow vectors implying lead (lag) effects are time-varying and heterogeneous showing no particular cryptocurrency or stock market as leader or follower. Different markets have the potential to lead or lag other markets at varying scales which may induce arbitrage opportunities for international and local investors. Our findings provide insights for policymakers, regulators and international investors as an economyâs monetary policy can be affected by the connections between the domestic capital market and other markets globally.
Within this work we consider an axiomatic framework for Automated Market Makers (AMMs). AMMs are smart contracts that set prices for swaps on a pool of assets. By imposing reasonable axioms on the underlying utility function, we are able to characterize the properties of the swap size of the assets and of the resulting pricing oracle. In providing these general axioms, we define a novel measure of price impacts that can be used to quantify those costs between different AMM constructions. We have analyzed many existing AMMs and shown that the vast majority of them satisfy our axioms. We have also considered the question of fees and divergence loss. In doing so, we have proposed a new fee structure so as to make the AMM indifferent to transaction splitting. Finally, we have proposed a novel AMM that has nice analytical properties and provides a large range over which there is no divergence loss.
Yizhi Wang, Florian Horky, Lennart John Baals, Brian M. Lucey · 5 authors
Amid surging market values and widespread regulatory discussion, NFT and DeFi markets are widely perceived as being simply speculative in nature. This paper detects the existence and dates of price bubbles in the NFT and DeFi markets by applying SADF and GSADF tests. We document that NFT and DeFi markets both exhibit speculative bubbles, with NFT bubbles being more recurrent and having higher average explosive magnitudes than DeFi bubbles. The price bubbles in the NFT and DeFi markets are highly correlated with market hype and with more general cryptocurrency market uncertainty. We do find periods where bubbles are not detected, suggesting that these markets do have some intrinsic value and should not be dismissed as simply bubbles.
Ăder Johnson de Area LeĂŁo Pereira, Paulo Ferreira, Derick Quintino
Non-fungible tokens (NFTs) are a type of digital record of ownership used in a unique way: ensuring authenticity and uniqueness. Due to these characteristics, NFTs have been used in several markets: games, arts, and sports, among others. In 2020, the volume of negotiations of the NFTs was about USD 200 million. Despite the strong interest of economic agents in operating with NFTs, there are still gaps in the literature, regarding their dynamics and price interrelation with other potentially related assets, which deserve to be studied. In this sense, the main purpose in this paper is to analyze the cross-correlation between NFTs and larger cryptocurrencies. To this end, our methodological approach is based on a Detrended Cross-Correlation Analysis correlation coefficient, with a sliding windows approach. Our main finding is that the cross-correlations are not significant, except for a few cryptocurrencies, with weak significance at some moments of time. We also carried out an analysis of the long-term memory of NFTs, which demonstrated the antipersistence of these assets, with results seemingly corroborating the market inefficiency hypothesis. Our results are particularly important for different classes of investors, due to the analysis on different time scales.
Abstract Recent studies about cryptocurrency returns show that their distribution can be highly-peaked, skewed, and heavy-tailed, with a large excess kurtosis. To accommodate all these peculiarities, we propose the asymmetric Laplace scale mixture (ALSM) family of distributions. Each member of the family is obtained by dividing the scale parameter of the conditional asymmetric Laplace (AL) distribution by a convenient mixing random variable taking values on all or part of the positive real line and whose distribution depends on a parameter vector $$\varvec{\theta }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>Ξ</mml:mi> </mml:mrow> </mml:math> providing greater flexibility to the resulting ALSM. Advantageously concerning the AL distribution, our family members allow for a wider range of values for skewness and kurtosis. For illustrative purposes, we consider different mixing distributions; they give rise to ALSMs having a closed-form probability density function where the AL distribution is obtained as a special case under a convenient choice of $$\varvec{\theta }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>Ξ</mml:mi> </mml:mrow> </mml:math> . We examine some properties of our ALSMs such as hierarchical and stochastic representations and moments of practical interest. We describe an EM algorithm to obtain maximum likelihood estimates of the parameters for all the considered ALSMs. We fit these models to the returns of two cryptocurrencies, considering several classical distributions for comparison. The analysis shows how our models represent a valid alternative to the considered competitors in terms of AIC, BIC, and likelihood-ratio tests.
Abstract Many types of cryptocurrencies, which predominantly utilize blockchain technology, have emerged worldwide. Several issuers plan to circulate their original cryptocurrencies for monetary use. This study investigates whether issuers can stimulate cryptocurrencies to attain a monetary function. We use a multi-agent model, referred to as the Yasutomi model, which simulates the emergence of money. We analyze two scenarios that may result from the actions taken by the issuer. These scenarios focus on increases in the number of stores that accept cryptocurrency payments and situations whereby the cryptocurrency issuer designs the cryptocurrency to be attractive to people and conducts an airdrop. We find that a cryptocurrency can attain a monetary function in two cases. One such case occurs when 20% of all agents accept the cryptocurrency for payment and 50% of the agents are aware of this fact. The second case occurs when the issuer continuously airdrops a cryptocurrency to a specific person while maintaining the total volume of the cryptocurrency within a range that prevents it from losing its attractiveness.
Cryptocurrency has gained its popularity in recent years. Due to enormous profitability potential, many investors and researchers alike have taken an interest in this domain. There is a lot of data in the cryptocurrency market that has to be analysed to make the right choices quickly when trading. Many have tried to automate the trading process by utilizing various prediction models and reinforcement learning to further streamline the trading process. It is therefore important to collect and summarize current state of the art technologies that investors and researchers use to predict and automate the cryptocurrency trading process. This paper provides a repository of knowledge to find out what other researchers have done by covering more than 13 different machine learning methods and several hybrid methods. This paper is the initial research step to try to come up with a new state-of-the-art approach to programmatic trading by determining a method that can be researched further.
Ensemble learning is a methodology that entails integrating a number of inefficient entities to achieve significantly improved performance. Boosting is a significant category of ensemble learning that involves the consecutive aggregate input of weak learners. The benefits of boosting approaches in processing tabular data with a significant quantity of information and resistance to overfitting can be very useful in estimating the market value of digital currency or cryptocurrency. The goal of this work is to examine and comprehend the capabilities of major boosting techniques such as XGBoost, AdaBoost, and CatBoost in cryptocurrency forecasting. The work examines the long-term forecasts of two major cryptocurrencies, Bitcoin and Ripple, for this purpose. The results indicate that AdaBoost and XGBoost have comparable predicting efficiency, followed by CatBoost. This implies that AdaBoostâs simpler boosting strategy is effective at achieving outcomes that are comparable to those of more recent boosting algorithms like XGBoost and CatBoost. The study has emphasized the similarities in achieving the best cryptocurrency prediction outcomes from each model. According to the research, a more straightforward boosting tactic is just as effective as or even more effective than the other most recent boosting strategies.
This study aims to forecast extreme fluctuations of Bitcoin returns. Bitcoin is the first decentralized and the largest, in terms of capitalization, cryptocurrency. A well-timed and precise forecast of extreme changes in Bitcoin returns is key to market participants since they may trigger large-scale selling or buying strategies that may crucially impact the cryptocurrency markets. We term the instances of extreme Bitcoin movement as âspikesâ. In this paper, spikes are defined as the returns instances that outreach a two-standard deviations band around the mean value. Instead of the unconditional historic standard deviation that is usually used, in this paper, we utilized a GARCH(p,q) model to derive the conditional standard deviation. We claim that the conditional standard deviation is a more suitable measure of on-the-spot risk than the overall standard deviation. The forecasting operation was performed using the support vector machines (SVM) methodology from machine learning. The most accurate forecasting model that we created reached 79.17% out-of-sample forecasting accuracy regarding the spikes cases and 87.43% regarding the non-spikes ones.