This study tests for the weak-form market efficiency of 15 cryptocurrency prices. The conventional unit root tests and stationary test results reveal that most cryptocurrency markets are efficient markets. However, the non-linear quantile unit root test proposed by Li and Park (2018) rejects the unit root null hypothesis over the whole quantile level. To derive more informative ideas, we split the whole quantile interval to several sub-intervals and find asymmetric behaviour of the market efficiency across the lower and upper sub-intervals in several cryptocurrency markets. Moreover, non-linear quantile unit root tests for Chainlink, Bitcoin Cash, Binance Coin, EOS, Tron, and Stellar indicate that markets for these cryptocurrencies are efficient at the upper sub-intervals.
Purpose The COVID-19 pandemic is known to have affected the logistics and supply chains; however, there is no adequate empirical evidence to prove in which way it has affected the relationship between the stocks related to this field with the corresponding cryptocurrencies. This paper aims to test the dynamic relationship of cryptocurrencies with supply chain and logistics stocks. Design/methodology/approach In this paper, the author tests the causal and long-run relationship between logistics and supply chain stocks with the corresponding cryptocurrencies related to these fields, or those that are known to exhibit characteristics that can be utilized by these fields, testing also whether the COVID-19 pandemic affected this relationship. To do so, the author performs the variable-lag causality to test the causal relationship, and examines if this relationship changed due to COVID-19. The author then implements the multifractal detrended cross-correlation analysis to investigate the characteristics of a possible long-run relationship, testing also whether they changed due to COVID-19. Findings The results indicate that there is a positive long-run relationship between each logistics and supply chain stocks and the corresponding cryptocurrencies, before and also during COVID-19, but during COVID-19 this relationship becomes weaker, in most cases. Moreover, before COVID-19, the majority of the cases indicate a causal direction from cryptocurrencies to the stocks, while during COVID-19, the causal relationships decrease in multitude, and most cases unveil a causal direction from the stocks to cryptocurrencies. Originality/value The causal pattern changed during COVID-19, and the long-run relationship became weaker, showing a change in the dynamics in the relationship between logistics and supply chain stocks with cryptocurrencies.
This paper tests the random walk hypothesis in the cryptocurrency market. Based on the well-known Meese–Rogoff puzzle, we evaluate whether cryptocurrency returns are predictable or not. For this purpose, we conduct in-sample and out-of-sample analyses to examine the forecasting power of our model built with autoregressive components and lagged returns of BITCOIN, compared with the random walk benchmark. To this end, we considered the 13 major cryptocurrencies between 2018 and 2022. Our results indicate that our models significantly outperform the random walk benchmark. In particular, cryptocurrencies tend to be far more persistent than regular exchange rates, and BITCOIN (BTC) seems to improve the predictive accuracy of our models for some cryptocurrencies. Furthermore, while the predictive performance is time varying, we find predictive ability in different regimes before and during the pandemic crisis. We think that these results are helpful to policymakers and investors because they open a new perspective on cryptocurrency investing strategies and regulations to improve financial stability.
Rapidly growing distributed ledger technologies (DLTs) have recently received attention among researchers in both industry and academia. While a lot of existing analysis (mainly) of the Bitcoin and Ethereum networks is available, the lack of measurements for other crypto projects is observed. This article addresses questions about tokenomics and wealth distributions in cryptocurrencies. We analyze the time-dependent statistical properties of top cryptocurrency holders for 14 different distributed ledger projects. The provided metrics include approximated Zipf coefficient, Shannon entropy, Gini coefficient, and Nakamoto coefficient. We show that there are quantitative differences between the coins (cryptocurrencies operating on their own independent network) and tokens (which operate on top of a smart contract platform). Presented results show that coins and tokens have different values of approximated Zipf coefficient and centralization levels. This work is relevant for DLTs as it might be useful in modeling and improving the committee selection process, especially in decentralized autonomous organizations (DAOs) and delegated proof-of-stake (DPoS) blockchains.
Isabela Ruiz Roque da Silva, Eli Hadad, Pedro Paulo Balbi
Abstract This study conducts a bibliometric analysis and systematic review of cryptocurrency trading algorithms to identify existing gaps in the area. From our standpoint, this is the first study to carry out a deep analysis of price forecasts and portfolio management in cryptocurrencies in addition to analyzing the most relevant studies and authors, trend topics of the area, and identifying countries with the most published studies. During our research, we identified some gaps that can be used for further research. Currently, there are approximately 16,000 cryptocurrencies; however, in majority of the papers, the authors have only used the top 10 ranking market capitalization cryptocurrencies, leaving aside potential minor cryptocurrencies. Thus, trading strategies using Big Data can be a potential research topic, considering the greater number of emerging cryptocurrencies.
Bitcoin investment gained great research interest, especially after the onset of the COVID-19 pandemic, a period marked by huge volatility in this asset class. This study investigated Bitcoin’s persistence and hedging properties in the pre-COVID era to establish its efficiency and safety by testing relevant data. We evaluated the role of persistence in Bitcoin trading to highlight its efficiency. The GPH estimator and ARFIMA were used to map the evolving efficiency of the Bitcoin price. Our analysis of intra-day data exhibited the presence of an anti-persistence effect, following the popular conclusion of momentum and speculative trading in the Bitcoin market. The second section of this study evaluated whether Bitcoin played the role of a hedge and an asset of protection in a global portfolio manager’s portfolio during extreme market volatility. Using the Threshold GARCH (TGARCH), we evaluated the trading correlation between Bitcoin prices and four major indices, namely S & P 500, FTSE, Hang Seng, and Nikkei, on daily and weekly data. We identified the time-varying hedge and safety properties of Bitcoin: volatility, speculation, less-traded history, and lack of regulatory infrastructure. Our findings added to the literature by testing the efficiency of Bitcoin in major developed economies using returns of high-frequency data, along with daily returns. We also considered extreme movements in the currency to check its hedging and protection properties in a portfolio of developed market stocks. We recommended that investors be cautious when combining this currency with different stock markets based on our findings.
Jacques Fleischer, Gregor von Laszewski, Carlos Theran, Yohn Jairo Parra Bautista
Digitization is changing our world, creating innovative finance channels and emerging technology such as cryptocurrencies, which are applications of blockchain technology. However, cryptocurrency price volatility is one of this technology’s main trade-offs. In this paper, we explore a time series analysis using deep learning to study the volatility and to understand this behavior. We apply a long short-term memory model to learn the patterns within cryptocurrency close prices and to predict future prices. The proposed model learns from the close values. The performance of this model is evaluated using the root-mean-squared error and by comparing it to an ARIMA model.
The cryptocurrency market has gained popularity in the last few years. Additionally, there is the availability of data on price fluctuations on cryptocurrency exchanges. Thus, statistical analysis can be conducted to identify the characteristics of the cryptocurrency market. This chapter aims to identify the characteristics of the cryptocurrency market in India and clarify to what extent the cryptocurrency market is similar or different from the traditional financial market of stock, currency, derivatives, commodities, and bonds. Thus, this chapter presents the history and development of the cryptocurrency market in India, cryptocurrency exchanges operating in India, and the differences between the cryptocurrency market and the traditional financial market. The chapter also presents the analysis of fluctuation in prices of cryptocurrency on varied platforms such as CoinSwitch, Binance, CoinMarketCap, etc. The statistical properties of the cryptocurrency market are compared with the traditional financial market.
This chapter investigates the linkages and connections between different cryptocurrencies. Johansen cointegration and network analysis is employed to examine top eight cryptocurrenices (i.e., Bitcoin, Dogecoin, Stellar, Cardano, Tether, XRP, Ethereum Classic, and Chainlink). The study documents evidence to support cointegration among different cryptocurrencies. The study finds that cryptocurrencies Ethereum Classic, Chainlink, Dogecoin, and Bitcoin are connected as one group, and XRP, Stellar, and Cardano are connected as another group whereas Tether does not fall under any group and indicates no connection with other cryptocurrencies considered.
The objective of this article is to analyze the co-movements in the G7 stock markets, such as DJ index, S&P500 (representing the USA stock market), FTSE 100 (United Kingdom), S&P/TSX (Canada), DAX 30 (Germany), CAC 40 (France), Nikkei 225 (Japan), Italy Ds market (Italy) and the cryptocurrencies Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH) and Crypto 10, during the period of February of 2018 to November of 2021. The results show that the cryptocurrencies BTC, ETH, and LTC increase the co-movements between their pairs, while the Crypto 10 index reduces the number of shocks when compared with the sub-period before COVID-19. Regarding the stock markets, DJ index kept the same level of shocks, whereas the Nikkei 225 decreased. For Germany (DAX), EUA (S&P500), Canada (S&P/TSX), United Kingdom (FTSE 100), France (CAC40), and Italy (Italy Ds Market) markets the results show an increase in movements during the global pandemic period. It is then possible to conclude the existence of evidence regarding synchronization and high co-movements, the results put at risk the implementation of efficient portfolio diversification strategies. These conclusions also open space for the market regulators to take steps to ensure better information on the dynamics of the international financial markets.
This study proposed an optimal model to examine the relationship between the Bitcoin price and six macroeconomic variables – the Bitcoin price, Standard and Poor's 500 volatility index, US treasury 10-year yield, US consumer price index, gold price and dollar index. It also examined the effectiveness of the vector error correction model (VECM) in analyzing the interrelationship among these variables. The authors employed the following approach: first, the authors sampled the period August 2010–February 2022. This is because Bitcoin achieved a market capitalization of more than US$1 tn over this period, gaining market attention and acceptance from retail, corporate and institutional investors. Second, the authors employed a VECM with the six macroeconomic variables. Finally, the authors expanded the long-run equilibrium relationship (time-invariant cointegration)-based VECM to develop a time-varying cointegration (TVC) VECM. The authors estimated the TVC VECM using the Chebyshev polynomial specification based on various information criteria. The results showed that the Bitcoin price can be modeled with the VECM ( p = 1, r = 1). The TVC approach generated more explanatory power for Bitcoin pricing, indicating the effectiveness of the approach for modeling the long-run relationship between Bitcoin price and macroeconomic variables.
Virtual kriptovalyuta olan bitkin rəqəmsal formata malik olan, texniki olaraq blokçeyn kimi ifadə edilən əməliyyatları əhatə edən və mərkəzi pul sisteminə daxil olmayan valyutadır. Tədqiqatda kriptovalyuta Bitcoin ilə valyuta məzənnələri arasındakı əlaqəni ortaya çıxarmaq hədəflənir. ABŞ Dolları ilə Avro, Yapon Yeni, İngilis Funtu, Avstraliya Dolları, Kanada Dolları, İsveçrə Frankı, Yuan Renminbi və İsveç Kronu məzənnələri ilə Bitcoin məzənnəsi arasındakı əlaqə, 3.02.2016- 04.10.2020 tarixləri arasındakı gündəlik məzənnələrə əsaslanaraq, struktur Qreqori və Hansen kointeqrasiyasını və Qrencer səbəb-nəticə analizini pozur. Təhlil nəticəsində müəyyən edilib ki, BTC/USD məzənnəsində struktur fasilələri 2017-cİ ilin aprel və dekabr aylarında baş verib. Bundan əlavə, tədqiqatda valyuta məzənnələrinin zaman silsiləsi arasında uzunmüddətli kointeqrasiya əlaqəsi, CNY/USD məzənnəsi ilə BTC/USD məzənnəsi arasında isə birtərəfli müsbət səbəb əlaqəsi müəyyən edilmişdir. Açar sözlər: kriptovalyuta, bitcoin, valyuta məzənnəsi, struktur fasilə, zaman seriyasi analizi Gunay Samir Karimli Relationship between cryptocurrency and rates Abstract Bitcoin, a virtual and cryptocurrency, is a digital currency that encompasses transactions, technically referred to as blockchain, and is not part of the central monetary system. The study aims to uncover the link between the cryptocurrency Bitcoin and exchange rates. The relationship between the US dollar and the euro, the Japanese yen, the British pound, the Australian dollar, the Canadian dollar, the Swiss franc, the yuan renminbi and the Swedish krona and the Bitcoin exchange rate, based on the daily exchange rates between 3.02.2016 and 04.10.2020, and Grenzer violates cause-and-effect analysis. The analysis revealed that structural breaks in the BTC / USD exchange rate occurred in April and December 2017. In addition, the study identified a long-term cointegration relationship between exchange rates over time, and a one-way positive causal relationship between the CNY / USD exchange rate and the BTC / USD exchange rate. Key words: cryptocurrency, bitcoin, exchange rate, structural break, time series analysis
Cryptocurrencies have surfaced as important fiscal software systems. cryptocurrency is a recent and significant invention in the fiscal assiduity. cryptocurrency relinquishment position has increased and the request has grown dramatically. cryptocurrency live only in digital form and can be transferred fully between digital addresses. According to a report by cryptocurrency exploration, India is one of the world’s fastest growing crypto requests, adding by 641.
Center for the Governance of Change, Mark Dempsey, Paula Oliver Llorente, Miguel Otero iglesias
Before the 2008 financial crisis, the term "crypto assets" was primarily the preserve of a<br> minority of computer scientists and engineers experimenting with new technologies as a<br> means of decentralizing finance. They subsequently launched the first projects involving<br> blockchain, but it was the white paper on Bitcoin by Satoshi Nakamoto in 2008 that<br> introduced crypto assets as an area of interest for investors and financial institutions with<br> higher risk appetites. The wider public followed shortly afterward and, later, regulators.
Kwapie\'n, Jaros{\l}aw, W\k{a}torek, Marcin, Marija Bezbradica, Martin Crane · 6 authors
We analyse tick-by-tick data representing major cryptocurrencies traded on some different cryptocurrency trading platforms. We focus on such quantities like the inter-transaction times, the number of transactions in time unit, the traded volume, and volatility. We show that the inter-transaction times show long-range power-law autocorrelations. These lead to multifractality expressed by the right-side asymmetry of the singularity spectra $f(\alpha)$ indicating that the periods of increased market activity are characterised by richer multifractality compared to the periods of quiet market. We also show that neither the stretched exponential distribution nor the power-law-tail distribution are able to model universally the cumulative distribution functions of the quantities considered in this work. For each quantity, some data sets can be modeled by the former, some data sets by the latter, while both fail in other cases. An interesting, yet difficult to account for, observation is that parallel data sets from different trading platforms can show disparate statistical properties.
Cryptocurrencies have emerged as a new asset class. In order to provide a thorough understanding of this new asset class, we study the dependencies in tail risk events within cryptocurrencies, and provide a hedging alternative in this paper. First, we adopt the Financial Risk Meter approach for cryptocurrencies, which is able to identify individual risk characteristics and indicate systemic risk in a network topology. Next, we detect the interdependencies across digital coins and study the spillover effects. Finally, we construct tail event sensitive portfolios and test the performance versus traditional approaches from January 2019 to May 2022.
This article's motivation is to understand the volatile Bitcoin price increase. The objective is to develop price estimation methods. The methodology is to present five differential equation models estimated against the 23 July 2010-21 June 2021 Bitcoin data. The findings are that Gompertz growth fits the damped oscillations and lengthening cycles well, and tracks the early data better with the weighted least squares method. Gompertz growth combined with charged capacitor growth tracks the early data even better. Logistic growth is too slow to track the early data. Logistic growth combined with charged capacitor growth to some extent tracks the early data. Pure charged capacitor growth is unrealistic. The dates for the future bull market maxima depend to a low degree on the growth model carrying capacity approached asymptotically, assumed to match gold at $10 trillion, and to be 50 times higher. The implications for traders are to focus on the large standard deviations. Investors should understand the growth potential compared with other asset classes. Regulators should ensure financial stability by focusing on the fluctuations. Central banks should adjust the money supply while acknowledging. Bitcoin competition. Collective units should understand Bitcoin growth models to determine whether to accept Bitcoin transactions.
Research on cryptocurrencies has proliferated in recent years. Our research objective was to answer the question of whether macroeconomic news from the U.S. affects Bitcoin in the same way it affects other common investment assets such as gold, the S&P 500, 2-year Treasury bills, and 10-year Treasury bills. Following previous research, seven macroeconomic news announcements from the U.S. were selected, and an empirical analysis of the daily returns, volatility, and volume of the selected assets was conducted. The results show that while Bitcoin is the most volatile (i.e., riskiest) of all the assets, the expected direction of movement is visible after the official announcement of the macroeconomic news on that day, and is comparable to that of the 2-year Treasury bills. It is also evident that the trading volume of Bitcoin does not change, unlike other assets, suggesting that the price of Bitcoin is always moved by the same players, indicating the closed and, therefore, riskier nature of cryptocurrency markets. Finally, we found evidence that the impact of macroeconomic announcements on Bitcoin returns is stronger when the announcements are negative but, interestingly, the returns of Bitcoin, unlike those of other assets, are more volatile after positive announcements.
In this research, we provided an answer to a very important trading question, what is the optimal number of technical tools in order to achieve the best trading results for both swing trade that uses daily bars and intraday trade that uses minutes bars? We designed Machine Learning (ML) systems that can trade four major cryptocurrencies: Bitcoin, Ethereum, BNB, and Solana. We found that more indicators do not necessarily mean better trading performance. Swing traders that use daily bars should trade Bitcoin and Solana using Ichimoku Cloud (IC) plus Moving Average Convergence Divergence (MACD), Ethereum with IC plus Chaikin Money Flow (CMF), and BNB with IC alone. With regard to intraday trading, we documented that different cryptocurrencies should be trading using different time frames. These results emphasize that the optimal number of indicators that are used to trade daily bars is one or, at maximum, two. The Multi-Layer (MUL) system that consists of all three examined technical indicators failed to improve the trading results for both days (swing) and intraday trades. The main implication of this study for traders is that more indicators does not necessarily improve trades performances.
This study investigates the asymmetric shock transmission mechanisms between seven large cryptocurrencies and crude oil at different market conditions across time. Wavelet technique was used to decompose the daily return series of the assets into wavelet scales to capture trading horizons. We applied quantile regression (QR) and quantile-in-quantile Regression (QQR) on the decomposed series to capture the bear (bull) market conditions. Applying the QR, we found Ethereum, Steller, Ripple and Monero as hedges for oil market volatility at all market regimes from medium to long terms. The QR undermined the hedging properties of Bitcoin, Litecoin and Das, suggesting possible spread of market disruptions from these markets to crude oil market. We observe from QQR that the assets have negative influence on each other at bear market but positive influence at bull market across time, signifying hedging possibilities for both assets in bear market. The significance of our finding is strengthened by the recent rise in the market share of cryptocurrencies.