Cryptoassets have experienced dramatic volatility in their prices, especially during the COVID-19 pandemic era. This pilot study explores the volatility asymmetry and correlations among three popular cryptoassets (Bitcoin, Ethereum, and Dogecoin) as well as Gold. Multiple Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models are analyzed. We find that positive shocks have a greater impact on the volatility of these financial assets than negative shocks of the same magnitude, perhaps a manifestation of the fear of missing out (FOMO) effect. Our research is one of the first to use COVID-19-period volatility of financial assets (in-sample data) to forecast their later COVID-19-period volatility (out-of-sample data). This forecast accuracy is compared to that produced by forecasts using the same out-of-sample data and a longer in-sample data. Our results indicate that generally, the larger in-sample dataset gives a higher forecast accuracy though the smaller in-sample dataset is from the same regime as the out-of-sample data. We also evaluate the correlations among the assets using the Dynamic Conditional Correlation (DCC) framework and find that there is an elevated positive correlation between Gold and Bitcoin during the past two years. The Gold-Bitcoin correlation hit its peak during the peak of the COVID-19 pandemic and then fell back to around zero in July 2021 when the pandemic crisis eased. Unsurprisingly, there is a strong positive correlation among the cryptocurrencies. Pairwise correlation among all four assets was stronger during the COVID-19 pandemic. Such continuing analysis can inform portfolio asset allocation as well as general financial policy decisions.
Purpose – This study attempts to establish if the markets for the two most popular cryptocurrencies in the world, Bitcoin and Ethereum, follow weak-form market efficiency across various landmarks in time. Design/Methodology/Approach – Traditional testing for establishing weak-form market efficiency rests on whether the price series exhibits a random walk process, which implies that future prices cannot be predicted. However, not all random walk series automatically imply weak-form market efficiency, since some asset price behaviors may exhibit non-constant variance. In such cases, the GARCH model can be used to test for the presence of market efficiency. Since structural breaks in the prices of both cryptocurrencies are common, tests for market efficiency were carried out using sub-temporal price windows. In both price series, the last time window coincided with the 2020 COVID-19 pandemic period. Findings – Results of the GARCH analyses showed that the volatility and persistence parameters (α and β, respectively) in the Bitcoin and Ethereum models were all statistically significant, implying that prices in their sub-temporal markets were generally weak-form inefficient. The observed market inefficiency in both cryptocurrencies can be attributed to various factors like the price manipulation of crypto whales, security issues, and increased media attention, which led to inflows of information that helped big investors beat and gain from the market by successfully predicting the trend in future prices. During the 2020 COVID-19 pandemic period, both cryptocurrencies’ prices were observed to rise significantly, similar to the case of the 2017 Bitcoin price bubble. A cointegrating regression between Bitcoin and Ethereum prices during this period, however, showed a spurious relationship. Despite the absence of a long run relationship between these two price series, the current price bubbles in the cryptocurrency markets are speculated to be tied together. Research Implications – Players in the cryptocurrency market must always be cautious in making investment decisions regarding this type of asset since the markets are generally price inefficient and risky; any idiosyncratic decision that may be triggered by a price bubble burst in one cryptocurrency market may or may not serve as a signal that the other market will do the same. Since the Bitcoin and Ethereum prices were shown to exhibit volatility spillover and persistence, investors can use this information to make informed decisions as to whether to invest in these cryptocurrencies despite the huge risks that are magnified during the COVID-19 pandemic.
I. S. Ivanchenko, Marina V. Charaeva, Alla A. Lysochenko, Ilya A. Nozhenkov
Since 2009, cryptocurrencies being a modern form of electronic means of payment have become widespread in the global financial market. In this regard, a study aimed to find an answer to the question: “Are cryptocurrencies a modern form of money?” was conducted. An analysis of the scientific works of leading economic schools has led to the conclusion that cryptocurrencies are a modern form of private money that performs the main monetary function being a means of payment, which corresponds to the idea of the Austrian economic school of full-fledged means of payment. The study attempts to predict the market rate of the three most popular cryptocurrencies at present being Bitcoin, Ethereum and Ripple due to the fact that modern cryptocurrencies demonstrate a high level of volatility in their market value, and reliable funds must maintain their purchasing power. The analysis of the cryptocurrency market with regard to the information efficiency has led to the conclusion that cryptocurrencies have been demonstrating instability of qualitative properties over the past five years. The authors proposed to improve the predictive characteristics of the HAR-RV model by additionally calculating the Shannon information entropy of the initial time series to level their insensitivity to unexpected information shocks in the cryptocurrency market being the main drawback of regression models. The study has proved that cryptocurrencies are a promising modern form of electronic money, their market rate is quite predictable, and the popularity of cryptocurrencies and their use in payment transactions will further increase.
Bitcoin is one of many crypto currencies used for peer - to - peer transactions accessible to anyone with internet access. It is a decentralized digital currency not backed by any government or other legal entity, making it an attractive alternative to the traditional fiat money system. There is no doubt that crypto currencies are the future of money. However, not all crypto projects will succeed in the long run and some might even turn out to be scams. It?s a jungle out there! How do you know which crypt currency project is going to survive? Our paper can help you identify which projects have a good chance of survival by analyzing their market capitalization trends over time using equicorrelation analysis. This paper examines whether or not Bitcoin returns are dependent on common factors, investigates whether or not Bitcoin returns are i. i. d., tests the efficiency of crypt currency markets, and provides an answer to the following question: are crypto currencies efficient? We'll be looking at crypto currencies and their impact on financial markets. We'll also discuss the challenges of using crypto currencies as a predictor for later price movements and look at equicorrelation and its effect on the crypt currency market. We'll also discuss some of the challenges of using equicorrelation as a predictor for future price movements. Finally, we'll explore some potential applications for equicorrelation within the business world.
The three largest cryptocurrencies by market value are examined for overreaction to positive and negative outlier return events. Bitcoin and Ethereum show significant reversals in value following outlier negative events suggesting overreaction. For positive events, significant cumulative gains (not reversals) followed outlier positive events for Ethereum and Tether showed a significant reversal in value after the positive events. Evidence is, therefore, mixed among the three main cryptocurrencies when it comes to how revaluations occur after outlier positive events.
We study the information dynamics between the largest Bitcoin exchange markets during the bubble in 2017-2018. By analysing high-frequency market-microstructure observables with different information theoretic measures for dynamical systems, we find temporal changes in information sharing across markets. In particular, we study the time-varying components of predictability, memory, and synchronous coupling, measured by transfer entropy, active information storage, and multi-information. By comparing these empirical findings with several models we argue that some results could relate to intra-market and inter-market regime shifts, and changes in direction of information flow between different market observables.
This research is the first attempt to customize a trading system that is based on second order stochastic dominance (SSD) to five known cryptocurrencies’ daily data: Bitcoin, Ethereum, XRP, Binance Coin, and Cardano. Results show that our system can predict price trends of cryptocurrencies, trade them profitably, and in most cases outperform the buy and hold (B&H) simple strategy. Our system’s best performance was achieved trading XRP, Binance Coin, Ethereum, and Bitcoin. Although our system has also generated a positive net profit (NP) for Cardano, it failed to outperform the B&H strategy. For all currencies, the system better predicted long trends than short trends.
The results of empirical analyses confirm that analysed unsystematic factors, the Stock-to-Flow index (S2F), and information on the Bitcoin (BTC) are directly correlated with BTC values. These results are expected and in line with the economic theory; however, this research paper aimed to investigate the impact of unsystematic factors on the value of decentralised virtual cryptocurrency BTC. Its aim was also to analyse the reasons for significant oscillations of market values in relation to the S2F and S2FX model and thus confirm the reliability of these models in the estimation of BTC value. The research further confirms the strong influence of non-technical information directly linked with the BTC. The limitations of this paper are the lack of possibilities for examining the impact of non-technical information affecting the Bitcoin price deviation regarding the S2F model. In addition to all mentioned limitations, the research results indicate the relevance of the S2F and S2FX models and show a strong impact of (half) the information on the value of cryptocurrencies.
Ana Todorovska, Eva Spirovska, Gorast Angelovski, Hristijan Peshov · 9 authors
In a world where no country, market, or economy is an island, interconnectivity is becoming a fundamental feature of almost all social and economic systems. In the case of digital assets like cryptocurrencies, the impact of interconnectivity on their performance and price trajectory is amplified. Studying these phenomena is essential for understanding the processes driving the crypto-markets. In this paper, we propose seven different approaches to create a network of eighteen most important cryptocurrencies. The first three approaches discover correlations between cryptocurrencies based on their daily prices, daily returns, and sentiment extracted from Reddit data. The following two approaches offer insights from the frequency of joint appearance of cryptocurrencies in Google news and Reddit data. The remaining two approaches determine each cryptocurrency’s impact over the others when forecasting prices and returns. Furthermore, we explore the networks’ interdependencies to explore the similarities of the cryptocurrency networks generated by different approaches. The proposed methodology allows us to understand the dynamics in the cryptocurrency markets and the different processes that influence their performance.
Using the asymmetric stochastic volatility model, this study investigates the day-of-the-week and holiday effects on the returns and volatility of Bitcoin from January 1, 2013 to August 31, 2019; in this context, we also discuss the characteristics of Bitcoin as a financial asset. The results of the estimation are threefold. First, the finding shows a small day-of-the week effect in volatility on Saturday and Sunday than in the rest of the week. Second, although the holiday effects are examined in active trading countries, namely Japan, China, Germany, and the United States, the positive post-holiday effect on the returns and weak positive pre-holiday effect on the volatility are only observed in the United States. Finally, the asymmetry effect is not observed. A comparison of Bitcoin to several assets such as stock, currency, and gold shows Bitcoin's positioning between stock, currency, and gold in relation to the week and holiday effects, its reaction to federal funds and medium of exchange characteristics, and the lack of asymmetry effect.
Fulvia Pennoni, Francesco Bartolucci, Gianfranco Forte, Ferdinando M. Ametrano
Abstract A hidden Markov model is proposed for the analysis of time‐series of daily log‐returns of the last 4 years of Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash. These log‐returns are assumed to have a multivariate Gaussian distribution conditionally on a latent Markov process having a finite number of regimes or states. The hidden regimes represent different market phases identified through distinct vectors of expected values and variance–covariance matrices of the log‐returns, so that they also differ in terms of volatility. Maximum‐likelihood estimation of the model parameters is carried out by the expectation–maximisation algorithm, and regimes are singularly predicted for every time occasion according to the maximum‐a‐posteriori rule. Results show three positive and three negative phases of the market. In the most recent period, an increasing tendency towards positive regimes is also predicted. A rather heterogeneous correlation structure is estimated, and evidence of structural medium term trend in the correlation of Bitcoin with the other cryptocurrencies is detected.
This paper initially presents a brief overview of the cryptocurrency and its history. We discuss the novel nature of literature attempting to create hybrid artificial neural network models to predict prices of cryptocurrency. For the remaining majority of the paper, we present the details of various hybrid artificial neural networks that have successfully been implemented to predict cryptocurrency prices in the form of a survey. Comparison of methods and results follow in the results section.
We develop an analysis of the cryptocurrency market borrowing methods and concepts from ecology. This approach makes it possible to identify specific diversity patterns and their variation, in close analogy with ecological systems, and to characterize the cryptocurrency market in an effective way. At the same time, it shows how non-biological systems can have an important role in contrasting different ecological theories and in testing the use of neutral models. The study of the cryptocurrencies abundance distribution and the evolution of the community structure strongly indicates that these statistical patterns are not consistent with neutrality. In particular, the necessity to increase the temporal change in community composition when the number of cryptocurrencies grows, suggests that their interactions are not necessarily weak. The analysis of the intraspecific and interspecific interdependency supports this fact and demonstrates the presence of a market sector influenced by mutualistic relations. These latest findings challenge the hypothesis of weakly interacting symmetric species, the postulate at the heart of neutral models.
The machine learning method has been used in stock price prediction for a long time, and the price of cryptocurrencies such as bitcoin has attracted more and more attention in recent years. This paper aims to improve the method applicable to the stock market and try to use it in cryptocurrency price prediction. A simple three-layered feedforward artificial neural networks (ANN) model was applied in this paper to predict the daily directions of cryptocurrency prices. The historical trading data of Bitcoin, Ethereum, and Cardano were used in the experiments. Nine selected technical indicators were preprocessed into discrete trend data, and they were input into the model together with three additional indicators for training. This study has preliminarily obtained an effective result with price prediction accuracy of the three cryptocurrencies between 61% and 65%.
Stefan Kitzler, Friedhelm Victor, Pietro Saggese, Bernhard Haslhofer
We present a measurement study on compositions of Decentralized Finance (DeFi) protocols, which aim to disrupt traditional finance and offer services on top of distributed ledgers, such as Ethereum. Understanding DeFi compositions is of great importance, as they may impact the development of ecosystem interoperability, are increasingly integrated with web technologies, and may introduce risks through complexity. Starting from a dataset of 23 labeled DeFi protocols and 10,663,881 associated Ethereum accounts, we study the interactions of protocols and associated smart contracts. From a network perspective, we find that decentralized exchange (DEX) and lending protocol account nodes have high degree and centrality values, that interactions among protocol nodes primarily occur in a strongly connected component, and that known community detection methods cannot disentangle DeFi protocols. Therefore, we propose an algorithm to decompose a protocol call into a nested set of building blocks that may be part of other DeFi protocols. This allows us to untangle and study protocol compositions. With a ground truth dataset that we have collected, we can demonstrate the algorithm’s capability by finding that swaps are the most frequently used building blocks. As building blocks can be nested, that is, contained in each other, we provide visualizations of composition trees for deeper inspections. We also present a broad picture of DeFi compositions by extracting and flattening the entire nested building block structure across multiple DeFi protocols. Finally, to demonstrate the practicality of our approach, we present a case study that is inspired by the recent collapse of the UST stablecoin in the Terra ecosystem. Under the hypothetical assumption that the stablecoin USD Tether would experience a similar fate, we study which building blocks — and, thereby, DeFi protocols — would be affected. Overall, our results and methods contribute to a better understanding of a new family of financial products.
The cryptocurrency market debate resumed in 2020 with renewed vigour as the price of Bitcoin surpassed late 2017 highs. This study aims to analyse possible factors of Bitcoin’s pricing at various cryptocurrency market development stages — before the 2017 price bubble, after and during the COVID-19 pandemic. The main method of analysis is a generalized autoregressive conditional heteroskedasticity model with conditional generalized error distribution (GARCHGED). Two groups of indicators are used as possible factors related to the Bitcoin dynamics. The first group consists of various quantitative indicators directly related to Bitcoin (the so-called internal factors) — the volume of exchange trade, the volume of transactions in the Bitcoin blockchain, the number of new and active wallets, hash rate, the sum of fees paid in the blockchain, as well as the dynamics of Google Trends search queries. The second group is the return on various financial assets — stock and bond indexes, commodities, and currency markets. The results of the analysis demonstrate the absence of a stable correlation between any of the factors under consideration and Bitcoin returns in all the periods that we focus on. In the period before the 2017 price bubble, the internal factors and Bitcoin returns showed generally co-directional dynamics, but the situation changed in 2018. In early 2021, the correlation between Bitcoin and traditional financial assets returns has increased significantly. We can conclude that Bitcoin is becoming a popular means of diversification as a high-risk asset, which, however, follows the pattern of a speculative bubble at the beginning of 2021. The increased demand for the need to invest in Bitcoin using various exchange-traded instruments (ETFs for cryptocurrencies) may soon lead to a further increase in the price of this cryptocurrency if such instruments are registered on the exchange.
Herding behaviour is an interesting phenomenon that has a serious impact on the market, leading to inefficient asset prices and high volatility in periods of market turmoil. We analysed the existence of herding behaviour in the cryptocurrency market using hourly price data of eight major cryptocurrencies and the cross-sectional standard absolute deviation (CSAD) approach. Our findings showed anti-herding behaviour at shorter time intervals and herding behaviour during longer periods. The trading decisions of cryptocurrency investors mimic the behaviour of other traders over time. We further found that herding behaviour is stronger over longer time intervals in a down market. When a market is declining, it suggests that fear increases and investors are forced to act quickly in response to market movements rather than using their information. Thus, investors need to fix the situation as quickly as possible to avoid making losses, hence they need to make rational choices based on knowledge rather than emotion or fear.