Abstract: Cryptocurrency, also known as crypto, is any digital or virtual currency that uses cryptography to safeguard transactions and circulates without the authority of a central bank. Bitcoin, the first and most widely used decentralized cryptocurrency, was introduced in 2009. After a few years of unrivalled dominance, it lost its monopoly in 2011, when the first competitive alternative currencies arose. As of November 2022, there are almost 21,000 cryptocurrencies in circulation. Because there is no government credit backup, cryptocurrency prices are typically volatile. The cost of one Bitcoin rose from zero at its debut in 2009 to $13 in 2013 and then to $68789 in 2021, with numerous shifts and fluctuations along the way. The accurate forecasting of the Bitcoin price is critical for investors to make decisions and for governments to create regulatory laws. This paper examines the ability of the models - Prophet, Long Short-Term Memory (LSTM), and eXtreme Gradient Boosting (XGBoost) to predict the price of Bitcoin reliably. Using the performance metrics like RMSE, each model was thoroughly trained and tested to discover which one operates more efficiently. After examining the price of Bitcoin from 2012 to 2021, we concluded that the Long Short-Term Memory (LSTM) model proves to be the most efficient when dealing with variable and difficult-topredict data such as Bitcoin values since it portrays promising results in comparison
Many investors include cryptocurrencies as potential investment tools in their portfolios. Previous studies have mostly analyzed Bitcoin regarding its hedge and safe haven features. Although the cryptocurrency market has expanded far beyond Bitcoin, few studies have examined the interaction among all other cryptocurrencies and conventional financial assets. For this purpose, as the dependent variable, we included the cryptocurrency index to represent the cryptocurrency market, whereas international stocks, bonds, United States (US) dollars, gold, and commodities as independent variables in the analysis. The interactions among the variables were analyzed using the Granger causality tests. The analysis results revealed a two-way causality relationship between the cryptocurrency market and the bond markets, indicating that the cryptocurrency index can be used to predict bond prices and vice versa.
Bitcoin prices are highly volatile and have extreme upper tails of the return distributions. One important component of Bitcoin price jumps is that it does not follow a normal distribution. This present study aims to reduce the extreme value data available on Bitcoin into simple clusters based on extreme value returns. The study first measures the excessive volatility and then estimates the extreme value returns of Bitcoin between November 2013 and August 2022 to achieve this objective. For robustness checks, extreme value returns are estimated using both the Rogers and Satchell (RS) and the Variance Ratio (VRatio) estimators that embed jumps in the model. Further, K-means clustering is used to form clusters based on the estimated Bitcoin’s extreme value returns as the probable good days (extreme days), medium days, and bad days. The study observes that K-means clustering can explain 65 percent point return variability. The study findings will be highly useful for crypto investors, policymakers, and future studies in data mining.
Cryptocurrency (e.g., Ethereum and its currency Ether) presents an opportunity to completely change the way money is transacted, which perhaps even redefines the money. On this basis, this paper will investigate and discuss the basic principles and corresponding applications of Ethereum. As a matter of fact, operating on a decentralized platform while also using the global reach of the internet, transactions can be made with little cost, without interference of intermediaries and government intervention. Besides, it will keep the personal information of the user private. In addition, according to the analysis, each user can define how they use ether as they please, e.g., using Ethereum to store assets like art and reselling them through Ethereum. However, Ethereum still has many issues to fix ranging from security risks, privacy risks, and perceived stability due to Ethereum’s notorious volatility and previous illegal activities from other platforms (e.g., Bitcoin). More research is required to distinguish whether Ether can be a viable alternative that could be added to the economy or even completely replacing traditional money altogether and address the current concerns. These results shed light on guiding further exploration of cryptocurrency.
Transaction fee markets are essential components of blockchain economies, as they resolve the inherent scarcity in the number of transactions that can be added to each block. In early blockchain protocols, this scarcity was resolved through a first-price auction in which users were forced to guess appropriate bids from recent blockchain data. Ethereum's EIP-1559 fee market reform streamlines this process through the use of a base fee that is increased (or decreased) whenever a block exceeds (or fails to meet) a specified target block size. Previous work has found that the EIP-1559 mechanism may lead to a base fee process that is inherently chaotic, in which case the base fee does not converge to a fixed point even under ideal conditions. However, the impact of this chaotic behavior on the fee market's main design goal -- blocks whose long-term average size equals the target -- has not previously been explored. As our main contribution, we derive near-optimal upper and lower bounds for the time-average block size in the EIP-1559 mechanism despite its possibly chaotic evolution. Our lower bound is equal to the target utilization level whereas our upper bound is approximately 6% higher than optimal. Empirical evidence is shown in great agreement with these theoretical predictions. Specifically, the historical average was approximately 2.9% larger than the target rage under Proof-of-Work and decreased to approximately 2.0% after Ethereum's transition to Proof-of-Stake. We also find that an approximate version of EIP-1559 achieves optimality even in the absence of convergence.
This study estimates the effects of double long memory and structural breaks on the persistence level of six major cryptocurrency markets. We apply the Bai and Perron’s structural break test, Inclán and Tiao’s iterated cumulative sum of squares (ICSS) algorithm, and the fractionally integrated generalized autoregressive conditional heteroscedasticity (FIGARCH) model with different distributions. The results show that long memory and structural breaks characterize the conditional volatility of cryptocurrency markets and confirm our hypothesis that ignoring structural breaks leads to an underestimation of the persistence of volatility modelling. The ARFIMA-FIGARCH model with structural breaks and a skewed Student–t distribution fits the cryptocurrency market’s price dynamics well.
With the rise of Blockchain technology, the cryptocurrency market has been gaining significant interest. In particular, the number of cryptocurrency traders and the market capitalization have grown tremendously. However, predicting cryptocurrency price is very challenging and difficult due to the high price volatility. In this paper, we propose a classification machine learning approach in order to predict the direction of the market (i.e., if the market is going up or down). We identify key features such as Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD) to feed the machine learning model. We illustrate our approach through the analysis of Bitcoin close price. We evaluate the proposed approach via different simulations. Particularly, we provide a backtesting strategy. The evaluation results show that the proposed machine learning approach provides buy and sell signals with more than 86% accuracy.
Economic systems play pivotal roles in the metaverse. However, we have not yet found an overview that systematically introduces economic systems for the metaverse. Therefore, we review the state-of-the-art solutions, architectures, and systems related to economic systems. When investigating those state-of-the-art studies, we keep two questions in our mind: (1) what is the framework of economic systems in the context of the metaverse, and (2) what activities would economic systems engage in the metaverse? This article aims to disclose insights into the economic systems that work for both the current and the future metaverse. To have a clear overview of the economic-system framework, we mainly discuss the connections among three fundamental elements in the metaverse, i.e., digital creation, digital assets, and the digital trading market. After that, we elaborate on each topic of the proposed economic-system framework. Those topics include incentive mechanisms, monetary systems, digital wallets, decentralized finance (DeFi) activities, and cross-platform interoperability for the metaverse. For each topic, we mainly discuss three questions: a) the rationale of this topic, b) why the metaverse needs this topic, and c) how this topic will evolve in the metaverse. Through this overview, we wish readers can better understand what economic systems the metaverse needs, and the insights behind the economic activities in the metaverse.
This paper extends the study of Bourghelle et al. (2022) to check whether collective emotions could help to forecast bitcoin volatility over the period 2018-2021. To this end, we first assess whether consideration of investor sentiment and collective emotions can give us clearer insights into bitcoin dynamics over the period in question and whether they can help to explain the different price fluctuations. Formally, we ran causality tests and, as in Bourghelle et al. (2022), built a two-equation nonlinear vector autoregressive (VAR) model to assess for further lead-lag effects between bitcoin volatility and collective emotions. Second, we proposed in-sample forecasts of bitcoin volatility to test whether our forecasts could be improved by taking investors’emotions and sentiment into account. Our findings show that market sentiment and investors’ emotions provide useful information that can help to explainfluctuations, structural breaks, and changes in bitcoin volatility. Further, collective emotions improve bitcoin volatility forecasting as our nonlinear model, including emotions-related news, supplants the benchmark linear model.
Wajid Shakeel Ahmed, Ahsan Mehmood, Talha Sheikh, Allah Bachaya
This paper investigated the relationship between cryptocurrencies and emerging stock market indices using fractional integration and co-integration technique. Particularly, fractional integration is applied to examine stochastic properties of individual assets and fractional cointegration to analyse bivariate connectedness. Our findings unveil the absence of mean reversion in majority cases which indicates high persistence in series. Furthermore, bivariate analysis reveals disconnection between cryptocurrencies prices and stock indices. Surprisingly, a different picture emerges on using conditional volatility instead of prices. Like, conditional volatility-based estimation uncovers evidence of mean reversion in univariate analysis as expected. There is some evidence of cointegration on volatility grounds between cryptocurrencies and emerging stock market indices. Our findings implies that investment decision regarding digital currencies should be taken cautiously. As cryptocurrencies are extremely volatile with high degree of persistence which can make them counterproductive.
The global economy receives a catastrophic blow due to the COVID-19 epidemic, with long-term pessimism shown towards the global market and exponential increasing expectation for looking for a reliable safe-haven asset. Now, it seems the possible alternatives to traditional currencies issued and backed by governments have primarily emerged - in the form of Bitcoin and Ethereum, as well as several other cryptocurrencies. Cryptocurrencies have been on the market for a long time and have been controversial in the international financial markets for their unique properties. Since the outbreak of COVID-19, the price of cryptocurrencies has seen an unprecedented increase. Whether the price increase of cryptocurrencies is linked to the COVID-19 outbreak is a mystery. This paper will focus on exploring this question through a linear regression machine learning model. The data in the U.S.A are used here. Our results show that the price of bitcoin is significantly related to the price of Ethereum. There is some correlation between covid-19 new cases incensement and the price of Bitcoin and Ethereum, indicating the legitimacy of predicting cryptocurrencies’ price using covid-19 new cases incensement as a factor.
The goal of our work was to select a neural network architecture that would give the best prediction of the Bitcoin exchange rate using historical data. Our work fits into the very important topic of predicting the value of the cryptocurrency exchange rate, and makes use of recent data which, as a result of the high Bitcoin exchange rate dynamics of the last year, differs significantly from those of previous years. We propose and test a number of neural network-based architectures and conduct a discussion of the results. Unlike previous state-of-the-art works, we conducted a comprehensive comparison of three different neural network-based models: MLP (multilayer perceptron), LSTM (long short-term memory) and CNN (convolutional neural network). We tested them for a wide range of parameters. The results we present are, to the best of our knowledge, the most up to date when it comes to the application of artificial intelligence methods for the prediction of cryptocurrency exchange rates. The best-performing architectures were used for a website that gives real-time predictions of the Bitcoin exchange rate. The website is available at http://stpbtc-ii.up.krakow.pl/. Source codes of our research are available to download in order to make our experiment reproducible.
Cryptocurrency markets have experienced large growth in recent years, with an increase in the number and diversity of traded assets. Previous work has addressed the economic properties of Bitcoin with regards to its hedging or diversification properties. However, the surge of many alternatives, applications, and decentralized finance services on a variety of blockchain networks requires a re-examination of those properties, including indexes from outside the big economies and the inclusion of a variety of cryptocurrencies. In this paper, we report the results of studying the most representative cryptocurrency of each consensus mechanism by trading volume, forming a list of twenty-four cryptocurrencies from the 1st of January 2018 to the 30th of September 2022. Using the Baur and McDermott model, we examine hedge, safe haven, and diversifier properties of all assets for all G7 country’s major indexes as well as all BRICS major indexes breaking it down by two attributes: kind of blockchain technology and pre/during COVID health crisis. Results show that both attributes play an important role in the hedge, safe haven, and diversifier properties associated with the asset. Concretely: stablecoins appear to be the only ones to maintain hedge property in most analyzed markets pre- and during-COVID; Bitcoin investment properties shifted after the COVID crisis started; China and Russia stopped being correlated with the cryptocurrency after the COVID crisis hit.
This study analyses and compares the behavior of the gold-backed, conventional cryptocurrency, and gold markets capable of detecting the existence of herding and deducing the efficiency degree. In addition, this empirical work tried to examine the COVID-19 pandemic's influence on both cryptocurrency performances. This work developed a new method that discloses herding biases using persistence and efficiency metrics. Besides, this paper investigated the nonlinear dynamic properties of the gold-backed, conventional cryptocurrencies and Gold by estimating the Multifractal Detrended Fluctuation Analysis (MFDFA). It also assessed the inefficiency of these markets through an efficiency index (IEI) and tested the effect of COVID-19 on their dynamics. The findings of this investigation indicate that the gold-backed cryptocurrency (X8X) is the most efficient market in the long-term trading market. However, the conventional cryptocurrency market (Bitcoin) is the most efficient on the short trade horizon. Besides, gold-backed cryptocurrency markets present a smaller level of herding behavior than conventional cryptocurrencies on tall scales. Nevertheless, we noted the positive and negative effects of the pandemic on each cryptocurrency market dynamics. To the best of the authors' knowledge, this study is the first investigation that uses multifractal analysis to quantify the impact of the COVID-19 spread on gold-backed cryptocurrencies and detects the presence of herding behavior.
Essais sur la dynamique du prix des crypto-monnaies Cet essai, qui comprend trois recherches empiriques originales, met en lumière les caractéristiques du comportement du marché des crypto-monnaies en fonction des effets macroéconomiques exogènes, du réseau interne et de leurs “matières premières" - les marchés de l'énergie. Le premier chapitre étudie les réponses des rendements et de la volatilité des crypto-monnaies aux annonces de nouvelles macroéconomiques américaines. En utilisant les données \textit{intraday} de 5 minutes sur les prix des crypto-monnaies, nous trouvons des preuves de la réaction des rendements et de la volatilité des crypto-monnaies aux nouvelles macroéconomiques et les possibilités d'utiliser les crypto-monnaies comme un outil de refuge en raison de la différence de réponse aux nouvelles macroéconomiques américaines entre les crypto-monnaies et les autres actifs financiers conventionnels. Dans le deuxième chapitre, nous étudions la causalité dynamique sur le marché des crypto-monnaies du point de vue du rendement et de la liquidité en utilisant une méthode de réseau de causalité stable. Les résultats mettent en évidence la spéculation du marché en montrant que les principales crypto-monnaies ne sont pas les acteurs les plus influents du réseau. Le chapitre 3 traite du sujet controversé de la relation entre les crypto-monnaies et les marchés de l'énergie. En outre, la consommation d'énergie des crypto-monnaies augmente considérablement suite à la difficulté croissante du minage et à une gamme plus complète d'application de la technologie blockchain telle que NFT et DeFi. En utilisant le modèle VAR à paramètres variables dans le temps, ce chapitre complète la littérature existante sur la liaison entre les crypto-monnaies et les marchés de l'énergie. Contrairement à la plupart des chercheurs existants, notre étude aborde non seulement les principales crypto-monnaies consommatrices d'énergie, le Bitcoin et l'Ethereum, mais aussi d'autres actifs basés sur la blockchain.
We applied the SVAR-LiNGAM to illustrate the causal relationships between the spot exchange rate, and three crypto-asset exchange rates, Bitcoin, Ethereum, and Ripple. It was notable that the causal order, the EUR_USD spot rate->Bitcoin->Ethereum->Ripple, was obtained by this approach. All the instantaneous effects were strongly positive. Moreover, it was notable that Bitcoin can influence the EUR_USD spot rate positively with a one-day time lag.
This paper examines the trading performances of several technical oscillators created using crypto-asset pricing methods for short-term bitcoin trading. Seven pricing models proposed in the professional and academic literature were transformed into oscillators, and two thresholds were introduced to create buy and sell signals. The empirical back-testing analysis showed that some of these methods proved to be profitable with good Sharpe ratios and limited max drawdowns. However, the trading performances of almost all methods significantly worsened after 2017, thus indirectly confirming an increasing financial literature that showed that the introduction of bitcoin futures in 2017 improved the efficiency of bitcoin markets.
Automated Market Makers (AMMs) have cemented themselves as an integral part of the decentralized finance (DeFi) space. AMMs are a type of exchange that allows users to trade assets without the need for a centralized exchange. They form the foundation for numerous decentralized exchanges (DEXs), which help facilitate the quick and efficient exchange of on-chain tokens. All present-day popular DEXs are static protocols, with fixed parameters controlling the fee and the curvature - they suffer from invariance and cannot adapt to quickly changing market conditions. This characteristic may cause traders to stay away during high slippage conditions brought about by intractable market movements. We propose a Reinforcement Learning (RL) framework to optimize the fees collected on an AMM protocol. In particular, we develop a Q-Learning Agent for Market Making Protocols (QLAMMP) that learns the optimal fee rates and leverage coefficients for a given AMM protocol and maximizes the expected fee collected under a range of different market conditions. We show that QLAMMP is consistently able to outperform its static counterparts under all the simulated test conditions.
The existing studies rarely reveal the reasons for the digital currency price fluctuation from the perspective of internal interaction and contagion. Therefore, to fill this research gap, this paper comprehensively adopts the dynamic conditional correlation (DCC-) GARCH model and wavelet coherence analysis (WTC) to reveal the internal correlation and formation reasons of digital currency price fluctuations. Our research has the following findings: (1) the price fluctuations of digital currency are highly related. Through the observation of the dynamic conditional correlation coefficient graph, it is found that the price fluctuations have a strong time-varying trend, manifested as a ‘contagious’ characteristic. (2) During the outbreak of COVID-19, most digital currencies have shown positive resonance in the short, medium, and long term, suggesting that the COVID-19 pandemic has increased the correlation and contagion of digital currency price fluctuations. (3) In the short term, Bitcoin is the main ‘contagious source’ of digital currency price fluctuation. But in the medium and long term, Ethereum and Ripple, which are closely related to the real economy, have a greater impact and become the new ‘contagious source’. Generally speaking, Bitcoin, Ethereum, and Ripple are the internal causes of instability in the digital currency market. Finally, based on the empirical conclusion, this paper proposes that the digital currency portfolio should be optimized to meet the investment demand; strengthen digital currency regulatory cooperation, and improve regulatory efficiency. Let the digital currency return to the ‘currency’ attribute and serve the real economy.
This paper investigates how changes in investor base is related to idiosyncratic volatility in cryptocurrency markets. For each cryptocurrency, we set change in its subreddit followers as a proxy for the change in its investor base, and find out that the latter can significantly increase cryptocurrencies idiosyncratic volatility. This finding is not subsumed by effects of size, momentum, liquidity and volume and is robust to various measures of idiosyncratic volatility.
In 2022, with the implementation of tightening monetary policies by FOMC, US dollar is experiencing a dramatic appreciation in a very short period. Though numerous studies have demonstrated the connection between the traditional currency market, cryptocurrency market, and precious metal market, rare studies are exploring the relationships between the three markets under a special political environment. This paper selects USDCNY exchange rate, gold and silver, and bitcoin as the representatives of three markets and then tests the volatility response of return on gold & silver and return on bitcoin to the change of return on USDCNY exchange rate. By employing impulse response function and ARMA-GARCHX model, the paper verifies the change of exchange rate will exacerbate the volatility of returns on gold & silver and bitcoin significantly, which suggests high risk and uncertainty of the cryptocurrency market and precious metal market in a complex and extreme political environment. Investors and speculators should take prudent investment strategies in such environment.
Being archetypal complex systems, financial markets exhibit rich set of dynamics in their interactions. In this paper, we focus on the recently evolved cryptocurrency market as an example of a complex system and analyse the evolution of cross correlation structure of cryptocurrencies in the 5 year period from 2017 to 2022. We observe characteristic correlation structures in the observation time window duration and use these specific structures to cluster the cryptocurrency market in 4 market states.
Abstract Can currency competition affect central banks’ control of interest rates and prices? Yes, it can. In a two-currency world with competing cash (material or digital), the growth rate of the cryptocurrency sets an upper bound on the nominal interest rate and the attainable inflation rate, if the government currency is to retain its role as medium of exchange. In any case, the government has full control of the inflation rate. With an interest-bearing digital currency, equilibria in which government currency loses medium-of-exchange property are ruled out. This benefit comes at the cost of relinquishing control over the inflation rate.