This paper studies the monthly expiration effect in the bitcoin markets. The emergence of trading in bitcoin futures in regulated markets is an ideal occasion to test this effect on an asset with singular characteristics. Our results with intraday data show that around the time of maturity there are significant changes in the trading volume, volatility and return of bitcoin, an asset that is traded in many exchanges simultaneously. Therefore, there is a clear expiration effect related to bitcoin futures. The closer to the expiration time (shortly beforehand or afterwards), the more intense these effects are. However, in spite of these general results, the expiration effect is not homogeneous across exchanges and depends on the characteristics of the futures contract in question. Robustness tests are also applied to confirm the results. The increasing participation of institutional investors is consistent with our findings, particularly in relation to the expiration effects of cash-settled futures, as these contracts are more appealing for sophisticated investors who could be interested in arbitrage or speculative processes.
Marcin WÄ torek, JarosĹaw KwapieĹ, StanisĹaw DroĹźdĹź
In this study the cross-correlations between the cryptocurrency market represented by the two most liquid and highest-capitalized cryptocurrencies: bitcoin and ethereum, on the one side, and the instruments representing the traditional financial markets: stock indices, Forex, commodities, on the other side, are measured in the period: January 2020--October 2022. Our purpose is to address the question whether the cryptocurrency market still preserves its autonomy with respect to the traditional financial markets or it has already aligned with them in expense of its independence. We are motivated by the fact that some previous related studies gave mixed results. By calculating the $q$-dependent detrended cross-correlation coefficient based on the high frequency 10 s data in the rolling window, the dependence on various time scales, different fluctuation magnitudes, and different market periods are examined. There is a strong indication that the dynamics of the bitcoin and ethereum price changes since the March 2020 Covid-19 panic is no longer independent. Instead, it is related to the dynamics of the traditional financial markets, which is especially evident now in 2022, when the bitcoin and ethereum coupling to the US tech stocks is observed during the market bear phase. It is also worth emphasizing that the cryptocurrencies have begun to react to the economic data such as the Consumer Price Index readings in a similar way as traditional instruments. Such a spontaneous coupling of the so far independent degrees of freedom can be interpreted as a kind of phase transition that resembles the collective phenomena typical for the complex systems. Our results indicate that the cryptocurrencies cannot be considered as a safe haven for the financial investments.
Phumudzo Lloyd Seabe, Claude Rodrigue Bambe Moutsinga, Edson Pindza
Highly accurate cryptocurrency price predictions are of paramount interest to investors and researchers. However, owing to the nonlinearity of the cryptocurrency market, it is difficult to assess the distinct nature of time-series data, resulting in challenges in generating appropriate price predictions. Numerous studies have been conducted on cryptocurrency price prediction using different Deep Learning (DL) based algorithms. This study proposes three types of Recurrent Neural Networks (RNNs): namely, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bi-Directional LSTM (Bi-LSTM) for exchange rate predictions of three major cryptocurrencies in the world, as measured by their market capitalizationâBitcoin (BTC), Ethereum (ETH), and Litecoin (LTC). The experimental results on the three major cryptocurrencies using both Root Mean Squared Error (RMSE) and the Mean Absolute Percentage Error (MAPE) show that the Bi-LSTM performed better in prediction than LSTM and GRU. Therefore, it can be considered the best algorithm. Bi-LSTM presented the most accurate prediction compared to GRU and LSTM, with MAPE values of 0.036, 0.041, and 0.124 for BTC, LTC, and ETH, respectively. The paper suggests that the prediction models presented in it are accurate in predicting cryptocurrency prices and can be beneficial for investors and traders. Additionally, future research should focus on exploring other factors that may influence cryptocurrency prices, such as social media and trading volumes.
In the post-pandemic era, two issues including the currency competition between BTC and the US dollar and the competition between the commodity and monetary medium functions of BTC are critical. By applying the Markov switching model, the cyclical nature of the numbers of additional confirmed COVID-19 cases and deaths are verified on daily basis. So, these two factors are assumed to follow the OrnsteinâUhlenbeck process. Then, we estimate parameters to establish the structural characteristics of post-pandemic era and the start of post-pandemic era. In order to clarify these two issues, we use vector autoregression for testing the related macrocosmic and financial variables and BTC. Systematic evidences are provided regarding the relationships among BTC, related macrocosmic, related financial variables, related COVID-19 variables. Our findings provide a useful insight into currency competition and commodity competition on the basis of the impulse response of BTC to US dollar fluctuation and the impulse response of BTC to expected inflation and volatility in the post-pandemic era. These findings indicate increased currency competition between Bitcoin and the US dollar in the post-pandemic era. Therefore, currency competition should be more valued than Commodity Competition in the post-pandemic era. This provides a useful guideline for Bitcoinâs management.
The cryptocurrency market has enormous growth potential. In this study, the aim is to investigate how the news (shocks) affects cryptocurrency market volatility. This is significant because, while cryptocurrencies are gaining popularity among investors, the marketâs extreme volatility discourages some prospective buyers, while also causing large losses for inexperienced investors. From 8 March 2019 to 30 November 2022, data from Bitcoin, Binance Coin, Ethereum, Dogecoin, and XRP were collected for the current study. The E-GARCH model was applied to the framed dataset to achieve the research aim. We discovered that the value of the size factor for all currencies was statistically significant, indicating that the news (shocks) significantly impacts volatility. Furthermore, volatility persistence in all cryptocurrencies is found to be very high and statistically significant. These study findings can help investors understand the impact of the news (shocks) on volatility in cryptocurrency returns.
AntĂłnio Portugal Duarte, FĂĄtima Sol Murta, Nuno Baetas da Silva, Beatriz Rodrigues Vieira
This paper analysis and compares the volatility of seven cryptocurrencies â Bitcoin, Dogecoin, Ethereum, BitcoinCash, Ripple, Stellar and Litecoin â to the volatility of seven centralized currencies â Yuan, Yen, Canadian Dollar, Brazilian Real, Swiss Franc, Euro and British Pound. We estimate GARCH models to analyze their volatility. The results point to a considerably high volatility of cryptocurrencies when compared to that of centralized currencies. Therefore, we conclude that cryptocurrencies still fall far short of fulfilling all the requirements to be considered as a currency, specifically regarding the functions of store of value and unit of account.
Twitter sentiment has been found to be useful in predicting whether the price of Bitcoin will rise or fall will climb or decline. Modelling market activity and hence emotion in the Bitcoin ecosystem gives insight into Bitcoin price forecasts. We take into account not just the emotion retrieved not just from tweets, but also from the quantity of tweets. With the goal of optimising time window within which expressed emotion becomes a credible predictor of price change, we provide data from research that examined the link among both sentiment and future price at various temporal granularities. We demonstrate in this study that not only can price direction be anticipated, but also the magnitude of price movement with same accuracy, and this is the study's major scientific contribution. Non-Fungible Token (NFT) has gained international interest in recent years as a blockchain-based application. The most prevalent kind of NFT that can be stored on many blockchains is digital art. We did studies on CryptoPunks, the most popular collection on the NFT market, in examine and depict each and every major ethical challenges. We investigated ethical concerns from three perspectives: design, trade transactions, and relevant Twitter topics. Using Python libraries, a Twitter crawler, and sentiment analysis tools, we scraped data from Twitter and performed the analysis and prediction on bitcoin and NFTs.
High Resolution Image Download MS PowerPoint Slide Reduction of greenhouse gas emissions has been a top priority for activists, scientists, and policy makers across the globe, and it is one of the main drivers for the transition to renewable energy generation. Bitcoin is a decentralized global transaction network of an eponymous digital currency. It has been praised for its openness, decentralization, and censorship resistance, as well as criticized for its inefficiency, criminal use, and enormous electricity consumption. We discuss the challenges in the renewable energy transition, properties of the bitcoin network, and the role of bitcoin mining operations in the global energy production and consumption. Although the adoption path for bitcoin is likely to be volatile with an uncertain outcome, the opportunities offered by bitcoin mining in reduction of the greenhouse gas emissions and renewable energy transition are greater than generally assumed.
We investigate the dynamic volatility connectedness of regional stocks, gold, Bitcoin, oil, and uncertainty index related to infectious diseases for the period from January 2014 to June 2022. We investigate the connectivity during Ebola & MERS periods, the normal period, the COVID-19 period and the full sample period. We find that the regional stock indices of the US, Europe, Africa and Latin America are net volatility transmitters whereas regional indices of Asia Pacific, Middle East and North Africa, and other assets like gold, oil and Bitcoin are net volatility recipients throughout the sample periods. By employing the TVP-VAR-based dynamic connectedness approach, we find the temporal evolution of system-wide total connectedness and pair-wise connectedness of financial assets to exhibit higher intensity of volatility spillover during the COVID-19 pandemic as compared to other sub-sample periods. We further observe, based on quantile connectedness approach, that the degree of dynamic connectedness is strong and significant across all the quantile spectrums only during the COVID-19 period. We observe that the safe haven characteristics of assets like gold, oil and Bitcoin diminish during the COVID-19 period due to strong dynamic connectedness with regional stock indices. Our findings have implications for policymakers, investors and portfolio managers in better risk management during periods of health epidemics and pandemics.
Bitcoin is a type of Cryptocurrency that relies on Blockchain technology and its growing popularity is leading to its acceptance as an alternative investment. However, the future value of Bitcoin is difficult to predict due to its significant volatility and speculative behavior. Considering this, the key objective of this research is to assess Bitcoinsâ explosive behavior during 2013â2022 including the most volatile COVID-19 pandemic and RussiaâUkraine war period and to forecast its price by comparing the predictive abilities offive different econometric, machine learning and artificial Intelligence methods namely, ARIMA, Decision Tree, Random Forest, SVM, and Artificial Intelligence Long Short-Term Memory Network (AI-LSTM). The precision of such methodologies has been assessed using root mean square error (RMSE) and mean average per cent error (MAPE) values. The findings confirmed that the AI-LSTM model performs better than other forecast models in predicting Bitcoinsâ opening price on the following working day. Therefore, Bitcoin traders, policymakers, and financial institutions can use the model effectively to better forecast the next dayâs opening price.
This paper examines return spillovers within and between different DeFi, cryptocurrency, stock, and safe-haven assets. For the period January 2019 to March 2022, we find that DeFi and cryptocurrency asset markets exhibit strong within-market and between-market return spillovers, that stock and safe-haven markets show weak connectedness, and that safe-haven assets are minor receivers and transmitters of between-market spillover effects. The connectedness between markets is time-varying and reveals structural changes in early 2020. Furthermore, we document that financial conditions shape the dynamics of return spillover effects between markets.
This paper applies the multivariate GARCH models to investigate the role of Bitcoin as a hedge and safe haven for ASEAN+6 stock markets compared to gold. We used daily data for the dates 2 January 2017â20 January 2023, covering the recent COVID-19 pandemic. The empirical findings provide compelling evidence of cross-market shock and volatility transmission between stock returns and Bitcoin returns in both directions. Therefore, the dynamics of Bitcoin returns significantly influence the volatility of stock returns, and the relationship also holds in reverse. All diagonal element estimations are statistically significant for both periods, as shown by the findings of the return and volatility spillovers between the returns of gold and the ASEAN+6 stock market. For most ASEAN+6 equity markets evaluated, Bitcoin and gold are not safe havens, and their inclusion increases the portfolio downside risk.
Previous research has shown volatility jumps and co-jumping behaviours in cryptocurrency markets. Motivated by these findings, we employ the herding effect and financial contagion channel to outline a theoretical framework of volatility-state-dependent correlations in cryptocurrency markets. We show that digital currency markets are more strongly correlated when experiencing an identical volatility regime, which echoes co-jumping behaviours addressed by the literature. Moreover, the strong correlation that occurs when the paired cryptocurrencies simultaneously experience a high volatility regime results in the least effectiveness of diversification in terms of a minimum portfolio risk reduction. Last but not least, the proposed state-dependent approach in this study proves effective at the task of risk forecasting and risk reduction for cryptocurrency portfolios, beyond the bivariate GARCH-based models, which are a pure and simple time-dependent approach.
Zaheer Anwer, Saqib Farid, Ashraf Khan, Noureddine Benlagha
In the wake of proliferation of cryptocurrencies and growing concerns regarding their environmental impact, we investigate the dynamic co-movement of digital assets and environmentally sustainable assets. We use daily data of five global indices from 01 March, 2017 to 15 May, 2022. The results suggest that environmentally sustainable indices and cryptocurrency indices demonstrate co-movements during pandemic. However, in the normal times, they mostly remain detached from each other. Therefore, it can be argued that both the asset classes can serve as hedge against each other. The findings carry important implications for the investment industry and regulators.
Muhammad Irfan, Mubeen Abdur Rehman, Sarah Nawazish, Yu Hao
This study aims to investigate the performance and behavior of fiat- and gold-backed cryptocurrencies to support stakeholders through the preparation of a portfolio from 1 January 2021 to 30 June 2022. Moreover, while searching for a hedge or a diversifier to construct a less risky portfolio with handsome returns, the prices of fiat-backed cryptocurrencies report high fluctuation during the sample period. ARIMA-EGARCH models have been employed to examine the volatile behavior of these cryptocurrencies. The empirical results are mixed as Bitcoin has been highly volatile during the economic recession. Due to its volatility, investors seek a safe haven. Ripple, on the other hand, shows low risk compared to Bitcoin. The results further reveal that PAX gold is more volatile than PM gold, while Bitcoin, being a highly traded cryptocurrency, is significantly correlated to other cryptocurrencies. The implications of this research showing the volatility of gold- and fiat-backed cryptocurrencies are equally important to stakeholders, such as investors, and policymakers.
This study investigates the relationship between expected returns on cryptocurrencies and macroeconomic fundamentals. Investors employ a lot of macroeconomic indicators for their investment decision, and hence adopting a few macroeconomic indicators is not sufficient in capturing a change in economic states. Moreover, due to aggregation, macroeconomic indicators are not measured precisely. To overcome these problems, we employ a dynamic factor model and extract common factors from a large number of macroeconomic indicators. We find that the common factors are strongly linked to the cryptocurrency expected returns at a quarterly frequency, while we do not observe this relationship using macroeconomic indicators such as inflation and money supply. This suggests that macroeconomic information matters in a longer term, which contrasts with the previous literature that explores a short-term relationship. The cryptocurrency prices are not determined by macroeconomic fundamentals in a short-term period since speculators impact the prices. However, in a long-term period, the prices are more linked to macroeconomic fundamentals.
This paper analyzes the correlation between bitcoin, oil price fluctuations and the DOW Jones Industrial Index in the time-frequency framework. Coherent wavelet method applied to recent daily data in the United States (1863 in total). Our research has several implications and supports for policy makers and asset managers. We find that oil prices lead the U.S. market at both low and high frequencies throughout the observation period. This result suggests that sanctions against Russia by a number of countries, including the U.S., are influencing oil prices, while oil remains a major source of systemic risk to the U.S. economy and economic uncertainty between the international level is exacerbated by tensions between Russia and Ukraine.
Purpose This paper investigates the impact of global sentiment and various coronavirus disease 2019 (COVID-19)-related media coverage news (Media-Hype index; Panic Index; Media Coverage Index, infodemic index and coronavirus statistics) on the dynamics of bitcoin returns during the COVID-19 pandemic using an asymmetric framework. Design/methodology/approach The authors use an asymmetric framework based on quantile regression (QR) and quantile-on-quantile regression. Findings QR results show that COVID-19 panic news negatively affects bitcoin market returns at times of extreme bearish. However, COVID-19 bullish sentiment negatively impacts bitcoin market returns during bullish market conditions. Quantile-on-quantile approach's (QQA) empirical results show that the effects of COVID-19-related news on bitcoin returns were heterogeneous, mainly negative and varied across quantiles. Research limitations/implications The authors find some significant differences regarding the impact of news on bitcoin return dynamics compared to stock markets, suggesting the safe-haven role of bitcoin against stock during the ongoing epidemic. Practical implications The authors find some significant differences regarding the impact of news on bitcoin return dynamics compared to stock markets, suggesting the safe-haven role of bitcoin against stock during the ongoing epidemic. Originality/value This study contributes to understanding the dynamics of bitcoin returns using various COVID-19 media news.
Purpose This paper aims to attempt to examine some of the unique features of cryptocurrency and the reasons for its growing market acceptability. Given the expanding size of cryptocurrency markets, the present study strives to identify whether it can be used as an alternative financial asset in place of traditional financial assets to meet firms' financial constraints. It also provides issues for future research in the area of cryptocurrency markets. Design/methodology/approach This paper analysed 94 research papers from databases such as ScienceDirect, Proquest, EBSCO, Emerald Insight and Web of Science. Articles connected to cryptocurrency, financial assets and corporate financial constraints research were explored. VOSviewer software has been used to visualise the specified body of literature and identify eight clusters in previous literature using keyword and abstract analysis. Findings Studies reveal that cryptocurrency markets are independent of traditional financial markets and cryptocurrency returns have less correlation with traditional financial asset classes. This can be an advantage to firms, especially during times of crisis when traditional financial assets are impacted by significantly lower returns, while cryptocurrencies can serve as an alternative. Realtime data reveals that during the pandemic, cryptocurrencies had the maximum growth in returns which also happened to be a time when firms faced severe cash constraints. While accepting cryptocurrency as a means of exchange is still under review by regulatory authorities, it can be considered an alternative asset for investment purposes. Firms can take advantage of it to overcome financial constraints and thus reap the gains from holding crypto assets for precautionary reasons. Originality/value The present study investigates using cryptocurrency as an alternative financial asset to solve the financial constraint problem in corporates. The issues regarding volatility, cyber securities, gold returns, long-term and short-term returns have been some of the most prominent studies in the area of cryptocurrency. The present study uses eight theme-based clusters to identify the role of cryptocurrency as an alternative investment class and examines evidence-based research regarding the financial returns from holding cryptocurrency over certain traditional asset classes such as gold, currency or stocks. In recent years, it has been found that investors' growing interest in holding cryptocurrency as part of their financial portfolio has led to the substantial appreciation of cryptocurrency prices. To the best of the authorsâ knowledge, the study will be a novel attempt to identify the role of cryptocurrency as an antidote to the companiesâ financial constraints and liquidity issues.
By now, cryptocurrencies have almost become a part of the modern financial asset space, but the cryptocurrency market itself is not homogeneous, and individual cryptocurrencies can differ significantly in their properties and functions. For example, the cryptocurrency Ether is second in capitalization after Bitcoin, but the Ethereum and Bitcoin blockchains differ significantly in their properties and functions. In particular, Ethereum is the most popular digital platform for creating decentralized applications (dApps). The purpose of this work is to try to answer the question "Does the market take into account the features of the Ethereum blockchain in the price dynamics of the Ether cryptocurrency?" This question is also directly related to the search for potential fundamental factors that can explain the price dynamics of Ether. The main econometric method used in the study is generalized autoregressive conditional heteroskedasticity (GARCH) models. Having evaluated about 15 thousand different specifications of GARCH models, where various Ethereum blockchain usage metrics were used as explanatory variables, we obtained the results that Ethereum network usage metrics do not significantly correlate with Ether cryptocurrency returns. Moreover, these metrics are also unable to explain the relative strengthening/weakening of Ether relative to Bitcoin. Thus, we conclude that despite the presence of a number of special functional properties of the Ethereum blockchain, the price dynamics of the Ether cryptocurrency does not reflect them.