Cryptocurrencies have gained a lot of attraction across the globe. Most observers of the cryptocurrency market will agree that crypto volatility is in a different league altogether. There has been a growing need to understand the nature of volatility in cryptocurrency. This paper analyzes the performance of four mostly traded, different cryptocurrencies in terms of their risk and return. The relationship between the return and returns volatility among different currencies has been examined considering the daily closing prices from 1 January 2017 to 30 June 2022, using the family of the GARCH model. The study has explored the spillover and asymmetric effect of volatility by using the DCC GARCH model and EGARCH model, respectively. The causal behavior among different cryptocurrencies has also been examined using Granger causality. There has been a strong spillover effect among different cryptocurrencies, Bitcoin and Ether, which are the top two cryptocurrencies with the highest market capitalization which have exhibited an asymmetric impact in their volatility as compared to the other two currencies, which are Litecoin and XRP.
The creation of distributed ledger technology resulted in the use of secured peer-to-peer interactions that pave way for the invention of Bitcoin and other cryptocurrencies. Since its invention, the price of Bitcoin has exhibited excessive volatility and has attracted increasing attentions. This paper considers the isolated influence of network activities (confirmed payments and users’ adoptions), mining information (network difficulty, Hashrate and transaction fees) and market factors (such as, bitcoin supply and trade volume) as key drivers of Bitcoin price. Using the vector autoregressive model (VECM), the results identified the existence of both long-term equilibrium and short-term dynamic relationship amongst the endogenous system’s variables. The cointegration relation has reversed adjustment effects on the bitcoin return. Accordingly, any deviation from the equilibrium dynamics due to perturbations of network events, market forces and mining data would be minimised. This explains why the Bitcoin price, and by implication its return, continues to experience different massive run-up, spiky protrusions, resistance, reversals, strong supports and consolidations. Based on the finding, the study recommends increased regulatory efforts to curb the excessive fluctuations in Bitcoin price in order to prevent significant loss which could discourage digital investors in the cryptocurrency markets.
This article employs the DCC-GARCH-based approach proposed by Gabauer (2020) to measure the connectedness of the cryptocurrency market, and whether the connectedness indexes became significantly different after China issued the regulatory ban in May 2021. The empirical finding suggests that the cryptocurrency market became significantly more interconnected after the ban was issued. Furthermore, we find that the net connectedness indexes of the majority of cryptocurrencies in the sample changed significantly after the ban. Among all the studied cryptocurrencies, Bitcoin is the largest net volatility receiver and Solana is the largest net volatility transmitter.
This article proposes a graph neural network strategy (GNN), in which the long short-term memory (LSTM) and graph convolution network (GCN) are applied to capture both temporal and spatial features to forecast the price of Bitcoin, Litecoin, Ethereum, and Dash Coin with the ‘stable-coin’ Tether (USDT) and financial stress index (FSI). The main results show that the GNN strategy has better performance than univariate LSTM and multivariate LSTM in all of the seven steps forward forecasting. A sensitivity check shows that USDT and FSI/sub-FSI are important factors in the construction of the graphs and they verify the validity of the results.
The concept of blockchain and cryptocurrencies is one of the most popular concepts of recent years. Cryptocurrencies were first introduces with Bitcoin in 2008 and now they have an increasing variety and popularity. Recent developments in technology firms have brought into question whether there is a relationship between Bitcoin and technology indexes. To this end, this study investigates the causality relationship between Bitcoin and technology indexes using monthly data between the years 2016 and 2021 in G7 and E7 countries. To test the causality relationship between the variables, the Hatemi-J (2012) asymmetric causality test was used. Hatemi-J (2012) test reveals that the relationship between bitcoin and technology indexes becomes different for G7 and E7 countries. The results suggest that developed countries affect bitcoin prices while developing countries are affected by Bitcoin prices. The conclusion is that findings point out the existence of an asymmetric relationship between the series for G7 and E7 countries.
The paper seeks to determine whether Bitcoin behaves differently from forex markets and Gold, and whether it offers any diversification, hedging, or safe-haven potential. A Markov regime-switching regression model is employed to determine the relationship between Bitcoin, the real economic activity, foreign exchange markets, financial markets, Energy, and Gold. The results indicate that, unlike USD/EUR and Gold, besides other variables, Bitcoin exhibits significant deviations in terms of its association with other financial and economic variables. Bitcoin appears to be strikingly positively associated with equity markets in both regimes. This may limit its potential to either act as a hedge or a safe-haven for US Equity markets. Bitcoin also deviates considerably from Gold and USD/EUR as it is not affected by the same set of variables as Gold or USD/EUR are under either regime. Moreover, while Gold appears to offer considerably weak safe-haven properties, particularly against equity, Bitcoin fails to be a safe-haven for any of the assets under study. The results, however, indicate that the properties of Bitcoin may range between a diversifier and a hedge, however, such potential of Bitcoin must be viewed with caution owing to the large volatility exhibited by Bitcoin.
Bu çalışmada, seçilmiş kripto varlıkların yüksek frekanslı gün içi varlık getirilerinin oynaklık (volatility) modelleri ve uzun hafıza özelliklerinin karşılaştırılmalı olarak incelenmesi amaçlanmıştır. Bitcoin (BTC), Ethereum (ETH), Cardano (ADA) ve Binance Coin (BNB) olmak üzere, 4 farklı kripto varlığın, 1 günlük, 12 saatlik, 8 saatlik, 6 saatlik, 4 saatlik, 2 saatlik, 1 saatlik, 30 dakikalık ve 15 dakikalık frekans düzeylerinde gerçekleşen 36 getiri serisi FIGARCH (Fractional Integrated- Kesirli Bütünleşik/Entegre edilmiş GARCH) modeli özelinde ele alınmıştır. Yapılan analizler sonucunda, ETH 30 dakikalık getiri serisi dışında, tüm serilerde uzun hafıza özelliğinin mevcut olduğu belirlenmiştir. Örneklem frekansının artması ile hataların bağımsız ve rassal dağılmakta güçlük çektiği, farklı örneklem frekanslarının uzun hafıza parametrelerinin ortalama olarak birbirine benzer olduğu, ancak bazı varlıkların çeşitli frekanslarının avantajlı bir yatırım stratejisi oluşturabileceği yönünde bulgular elde edilmiştir. FIGARCH modeli ile tüm koşul ve kısıtlar sağlanarak, 36 veri kümesinin 35’inin anlamlı ve iyi tanımlanmış olarak modellemede başarılı olduğu belirlenmiştir.
David Opeoluwa Oyewola, Emmanuel Gbenga Dada, Juliana Ngozi Ndunagu
Cryptocurrency is an advanced digital currency that is secured by encryption, making it nearly impossible to forge or duplicate. Many cryptocurrencies are blockchain-based with decentralized networks. The prediction of cryptocurrency prices is a very difficult task because of the absence of an appropriate analytical basis to substantiate their claims. Cryptocurrencies are also dependent on several variables, such as technical advancement, internal competition, market pressure, economic concerns, security, and political considerations. This paper proposed the hybrid walk-forward ensemble optimization technique and applied it to predict the daily prices of fifteen cryptocurrencies, such as Cardano (ADA-USD), Bitcoin (BTC-USD), Dogecoin (DOGE-USD), Ethereum Classic (ETC-USD), Chainlink (LINK-USD), Litecoin (LTC-USD), NEO (NEO-USD), Tron (TRX-USD), Tether (USDT-USD), NEM (XEM-USD), Stellar (XLM-USD), Ripple (XRP-USD), and Tezos (XTZ-USD). A performance comparison of these cryptocurrencies was done using classical statistical models, machine learning algorithms, and deep learning algorithms on different cryptocurrency time series. Simulation results show that our proposed model performed better in terms of cryptocurrency prediction accuracy compared to the classical statistical model and machine and deep learning algorithms used in this paper.
Abstract New Technologies (blockchain, autonomous cars, artificial intelligence, and the internet of things) are revolutionizing industries such as finance, banking and transportation by improving efficiency and effectiveness in these businesses around the world. However, not much work is done on applying these technologies in the oil and gas industry. So, a tremendous opportunity exists to leverage these new technologies for possible applications in oil and gas. This paper explores the application of blockchain and smart contracts in managing an effective Management of Change (MOC) program and integrating it with operations to process real-time changes leading to faster and safer decision making. The paper is divided into two sections; the first provides an overview of the technology, and the second focuses on integrating smart contracts and blockchain technology to leverage the MOC program. This paper could be used to roll out new and improve existing MOC programs. The application of emerging technologies such as distributed ledger and smart contracts are still at the rudimentary level for oil and gas with few applications around trading and logistics. This paper unlocks the possible application in managing changes in the oil and gas, which will provide a solid platform for future innovations. The oil and gas industry are moving at a faster pace to achieve the next level of efficiency and effectiveness for sustainable growth, the key will remain in the early adoption of these emerging technologies for safer operation. The application mentioned in this paper will provide a company with an additional competitive advantage as they could effectively manage their data to make faster decisions; and achieve higher efficiency and safer operations simultaneously.
Virtual currencies have been declared as one of the financial assets that are widely recognized as exchange currencies. The cryptocurrency trades caught the attention of investors as cryptocurrencies can be considered as highly profitable investments. To optimize the profit of the cryptocurrency investments, accurate price prediction is essential. In view of the fact that the price prediction is a time series task, a hybrid deep learning model is proposed to predict the future price of the cryptocurrency. The hybrid model integrates a 1-dimensional convolutional neural network and stacked gated recurrent unit (1DCNN-GRU). Given the cryptocurrency price data over the time, the 1-dimensional convolutional neural network encodes the data into a high-level discriminative representation. Subsequently, the stacked gated recurrent unit captures the long-range dependencies of the representation. The proposed hybrid model was evaluated on three different cryptocurrency datasets, namely Bitcoin, Ethereum, and Ripple. Experimental results demonstrated that the proposed 1DCNN-GRU model outperformed the existing methods with the lowest RMSE values of 43.933 on the Bitcoin dataset, 3.511 on the Ethereum dataset, and 0.00128 on the Ripple dataset.
Cryptocurrencies emerged with the invention of Bitcoin in 2008 and in a short time they managed to attract the attention of a significant number of investors. Since 2012, cryptocurrency markets have become markets where unpredictable transaction volumes take place around the world. Bitcoin is the most recognized asset which has the highest trading volume in cryptocurrency markets today. This is due to the dominance effect of Bitcoin on cryptocurrency markets. It is a matter of curiosity in the world and in our country whether savings are transferred from traditional financial markets to cryptocurrency markets. In this study, the possible relationships between Bitcoin trading volumes and Bitcoin price changes between 2017 and 2021, and the trading volumes realized in Borsa Istanbul (BIST) and the BIST 100 index were examined with the Granger causality approach and the direction of these relationships was tried to be determined using the Toda Yamamoto causality model. As a result of the examination, it has been determined that there is no causal relationship between Bitcoin trading volumes and Borsa Istanbul trading volumes according to the Toda Yamamoto approach.
<span lang="EN-US">Cryptocurrency is a virtual or digital currency used in financial systems that utilizes blockchain technology and cryptographic functions to gain transparency, decentralization, and conservation. Cryptocurrency prices have a high level of fluctuation; thus, tools are needed to monitor and predict them. RNN is a deep learning model that is capable of strongly predicting data time series. Some types of Recurrent Nureal Network layers, such as Long Short Term Memory, have been used in previous studies to prediction common used currency. In this study, we used the Gate Recurrent Unit and Bidirectional</span><span lang="EN-US">–</span><span lang="EN-US">LSTM hybrid model to predict cryptocurrency prices to improve the accuracy of previously proposed prediction LSTM Model to predict the Bitcoin, Using four cryptocurrencies (Bitcoin, Ehtereum, Ripple, and Binance), we obtained very good results with RMSE after normalization the results get closer to 0 and with MAPE values all below &lt;10%.</span>
This study employs the ADCC-GARCH approach to investigate the dynamic correlation between bitcoin and 14 major financial assets in different time-frequency dimensions over the period 2013-2021, for which the risk diversification, hedging and safe-haven properties of bitcoin for those traditional assets are further examined. The results show that, first, bitcoin is positively linked to risk assets, including stock, bond and commodity, and negatively linked to the U.S. dollar, which is a safe-haven asset, so bitcoin is closer in nature to a risk asset than a safe-haven asset. Second, the high short-term volatility and speculative nature of the bitcoin market makes its long-term correlation with other assets stronger than the short-term. Third, the positive linkage between the prices of bitcoin and risk assets increases sharply under extreme shocks (e.g., the outbreak of COVID-19 in early 2020). Fourth, bitcoin can hedge against the U.S. dollar, and in the long term, bitcoin can hedge against the Chinese stock market and act as a safe haven for the U.S. stock market and crude oil. However, for most other traditional assets, bitcoin is only an effective diversifier.
Bassam A. Ibrahim, Ahmed A. Elamer, Hussein A. Abdou
This study aims to explore the role of cryptocurrencies and the US dollar in predicting oil prices pre and during COVID-19 pandemic. The study uses three neural network models (i.e., Support vector machines, Multilayer Perceptron Neural Networks and Generalized regression neural networks (GRNN)) over the period from January 1, 2018, to July 5, 2021. Our results are threefold. First, our results indicate Bitcoin is the most influential in predicting oil prices during the bear and bull oil market before COVID-19 and during the downtrend during COVID-19. Second, COVID-19 variables became the most influential during the uptrend, especially the number of death cases. Third, our results also suggest that the most accurate model to predict the price of oil under the conditions of uncertainty that prevailed in the world during the bear and bull prices in the wake of COVID-19 is GRNN. Though the best prediction model under normal conditions before COVID-19 during an uptrend is SVM and during a downtrend is GRNN. Our results provide crucial evidence for investors, academics and policymakers, especially during global uncertainties.
This paper aims to analyze the volatility spillover relationship between cryptocurrencies and stablecoins dynamically. Within the scope of the study, the daily closing price data of Bitcoin (BTC), Ethereum (ETH), BNB cryptocurrencies, and Tether (USDT) and USD Coin (USDC) stablecoins covering the period from January 1, 2019 to April 6, 2022 was analyzed using the Q-VAR model. Our results suggest that the volatility spillover between the cryptocurrency and stablecoins increased during the Covid-19 pandemic. Moreover, the direction and severity of volatility spillover between cryptocurrencies and stablecoins are affected by global events. While the relationship between cryptocurrencies and stablecoins themselves is strong, the relationship between each other is weak. Our findings suggest that global events influence the interaction between crypto-assets and that cryptocurrencies and stablecoins can be good diversifiers for each other. These findings have important implications for financial market regulators, portfolio investors, and academic research.
In recent years, as a result of the increasing popularity of crypto-assets all over the world, an increasing number of innovative crypto assets are coming to the market. In this context, Fan Tokens, which is a type of "utility token", are also discussed in this research as a prominent crypto asset in recent years. As developments in both cryptocurrency and sports sector increase the interest in Fan Tokens day by day, the financial volume of the system is constantly growing. With the growing volume, the risks taken by those who buy Fan Tokens are also increasing. Although there are studies on the risks of crypto assets in the literature, since the issue is not addressed specifically for Fan Tokens, it is deemed worthy of our review. The relationship between the price movements of Fan Tokens and the movements of the dominant crypto assets has been examined. It has been determined by the regression and correlation analyzes in the research that the movements in the crypto money exchanges have an effect on the Fan Token exchanges. Therefore, the developments in the crypto money exchanges should be followed especially and carefully for those who see the Fan Tokens as an investment tool.