Purpose The present work endeavors to explore the potential nonlinear and asymmetric effects of supply fundamental properties of Bitcoin mining process (velocity, size and stock of Bitcoins, cost of production and mining revenue), DJIA, VIX, economic policy uncertainty and Google Trend on the price of Bitcoin (PB). Design/methodology/approach The authors apply the Nonlinear Autoregressive Distributed lag (NARDL) approach for the period from November 31, 2013 to December 30, 2020. Findings The asymmetric effects of inflation, the size of Bitcoin economy, reveal a positive impact on the PB in the short and long run. In the short run, Bitcoin price shows negative statistically significant sensitivity to positive (negative) changes in DJIA (VIX) index. In addition, Google Trends have an impact on Bitcoin prices indicating that the Bitcoin market is also driven by investors' sentiments. In the long run, negative policy uncertainty shocks increase the PB while in the short run, negative shocks decrease it. Originality/value The authors give credence to the best ways of understanding the existence of asymmetries in the link between the PB and a number of influential macro-finance variables to improve the appropriate asset allocation and portfolio management.
The study analyzes the volatility spillover effects of cryptocurrencies and foreign exchange market in Nigeria, covering a two-year period from September 19th, 2019, to September 19th, 2021. It captures a period where the domestic and foreign economy experienced a series of challenges, reflecting on its financial markets and cryptocurrency. The study adopts the Vector Autoregressive - Multivariate Generalized Conditional Heteroskedastic methodological framework, with the Baba, Engle, Kraft, and Kroner transformation (VAR-MGARCH-BEKK), to determine the volatility spillover effect between Nigeriaâs Foreign exchange returns and the price returns of four of the largest cryptocurrencies traded in Nigeria. Findings indicate foreign exchange have positive effect on the mean spillovers on cryptocurrencies, and an overall market influence over cryptocurrencies, due to a high GARCH and low ARCH estimate. However, the ARCH parameters show that past errors of foreign exchange market are observed to be vulnerable to external volatilities. Therefore, the study is able to conclude that cryptocurrencies serve as a viable hedging, safe haven and an effective diversification instrument against financial uncertainties, and therefore, recommends optimal diversification strategies and low leverage contracts to avoid the high risks cryptocurrencies present, as they are highly volatile, hence, susceptible to speculative attacks.
This study sets out to explore the impacts of the Russian-Ukrainian conflict on worldwide financial markets by considering a large array of national currencies, precious metals and fuel, agricultural commodities and cryptocurrencies. Estimations span the period since the Russian invasion until the takeover of the Ukrainian city of Mariupol. Optimal portfolios are constructed for separate categories of financial assets for different levels of risk-aversion by investors. The Chinese yuan, gold, corn, soybeans, sugar and Bitcoin prove to be safe haven investments while the Japanese yen, natural gas, wheat and the combination of Bitcoin and Ethereum offer profit opportunities for risk-seekers. Notably, the agricultural commoditiesâ portfolio is the best performing while the cryptocurrency portfolio generates the worst risk-return trade-off. National currencies could act as safe havens in the place of gold when all types of assets can be combined. Natural gas is revealed to be the most reliable profit generator. Overall, high risk appetite does not result in large improvement in portfoliosâ returns. This study sheds light on investorsâ optimal decision-making during elevated geopolitical uncertainties and provides a compass for improving welfare.
Open access
Market Dynamics and Volatility
Environmental and Biological Research in Conflict Zones
This study conducted a systematic review regarding the association between cryptocurrency and the stock market. This study used bibliometric and content analysis covering 151 articles from 2008 to November 2021. Using VOSviewer software, we explored the influential aspects of the literature, such as the prominent institutions, authors, countries, and journals. Additionally, we performed co-authorship, bibliographic coupling, and co-occurrence of keywords to understand the network. Furthermore, in the content analysis, we discussed key findings of four major research streams that we identified. Finally, we present seven research questions that can be explored in the future. The findings have a number of implications for the present state of the literature on cryptocurrency and the stock market, including study gaps and potential future research initiatives.
<strong>Abstract</strong> The purpose of this article is to provide an outline of cryptocurrency's function in the global financial system. Another important goal of this essay is to understand the basic notion of digital money and to assess the potential of cryptocurrencies in the global financial system. This will be a descriptive study in which an attempt will be made to investigate the many benefits and applications of cryptocurrencies. Digital financial assets are cryptocurrencies for which ownership and transfers of ownership are guaranteed by a cryptographically decentralised system. The rise in the market value of cryptocurrencies, as well as their growing popularity around the world, has created a slew of commercial and industrial economic issues and worries. Acceptance as a kind of alternative currency, as well as the prohibition of any fraudulent use, should be vigorously encouraged.
The present study is a novel attempt to unravel the connectedness of the green bond with energy, crypto, and carbon markets using the S&P green bond index (RSPGB). We consider MAC global solar energy index (RMGS) and ISE global wind energy index (RIGW) as proxies of the energy market and use bitcoin and the European energy exchange carbon index (REEX) for the cryptocurrency and carbon market. Employing the Diebold and Yilmaz (2012), BarunĂk and KrehlĂk (2018), and wavelet coherence econometric techniques, we find that the energy market (RMGS) has the highest connectedness derived from other asset classes, and bitcoin (RBTC) has the least connectedness. Concurrently, we find that the risk transmission is heterogeneous in different scales as the short period has less connectedness than the medium and long run. We conclude that the overall diversification opportunity among green bonds, energy stock, bitcoin, and the carbon market is more in the short-run than in the medium and long-run. In summary, our findings on the green bond market will provide investors, portfolio managers, and policymakers with critical insight into ensuring a sustainable financial market.
We examine the static and time-varying herding behavior in three cryptocurrency classes: âconventionalâ cryptocurrencies, non-fungible tokens, and DeFi assets during the most recent cryptocurrency bubble of 2021. While static herding analysis failed to demonstrate any evidence of herding, the time-varying herding has been identified in conventional cryptocurrencies and DeFi assets for the short investment horizons. The herding asymmetry analysis reveals that herding is not evident in conventional cryptocurrencies and NFT during up/down market, high/low volatility days, and high/low trading days. We only find herding in DeFi assets during the low volatility days.
Abstract Nowadays, the issue of fluctuations in the price of digital Bitcoin currency has a striking impact on the profit or loss of people, international relations, and trade. Accordingly, designing a model that can take into account the various significant factors for predicting the Bitcoin price with the highest accuracy is essential. Hence, the current paper presents several Bitcoin price prediction models based on Convolutional Neural Network (CNN) and Long-Short-Term Memory (LSTM) using market sentiment and multiple feature extraction. In the proposed models, several parameters, including Twitter data, news headlines, news content, Google Trends, Bitcoin-based stock, and finance, are employed based on deep learning to make a more accurate prediction. Besides, the proposed model analyzes the Valence Aware Dictionary and Sentiment Reasoner (VADER) sentiments to examine the latest news of the market and cryptocurrencies. According to the various inputs and analyses of this study, several effective feature selection methods, including mutual information regression, Linear Regression, correlation-based, and a combination of the feature selection models, are exploited to predict the price of Bitcoin. Finally, a careful comparison is made between the proposed models in terms of some performance criteria like Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Median Absolute Error (MedAE), and coefficient of determination (R 2 ). The obtained results indicate that the proposed hybrid model based on sentiments analysis and combined feature selection with MSE value of 0.001 and R 2 value of 0.98 provides better estimations with more minor errors regarding Bitcoin price. This proposed model can also be employed as an individual assistant for more informed trading decisions associated with Bitcoin.
This study examines whether Islamic gold-backed cryptocurrencies (Onegram and X8X) provide any diversification benefits to the Islamic investors of Indonesia. We study the co-movements between return and volatility of cryptocurrencies and Indonesian Islamic equity indices during the pre-COVID-19 and COVID-19 periods. We employ Multivariate Generalized Autoregressive Conditional Heteroscedastic-Dynamic Conditional Correlation (M-GARCH-DCC) and Continuous Wavelet Transforms (CWT) for this study. We find that the COVID-19 crisis enhanced the spillover effect among the Islamic gold-backed cryptocurrencies and Islamic equities. We also provide evidence that Indonesian investors may invest in cryptocurrencies to minimize the equity sector risks during the pandemic. Our results bear significant implications for portfolio diversification strategies for Indonesian investors.
The specific properties of assets such as cryptocurrencies, gold, and stocks have welcomed more empirical studies in assessing their nexus. As a result, market conditions, whether good or bad, become imperative to assess the benefits of safe have, hedges or diversification. Also, the presence of uncertainties in markets may have asymmetrical effects which make it necessary to assess their impact over time. The emergence of COVID-19 pandemic as a global uncertainty has altered the dynamics of most financial markets. Consequently, this may influence the lead/lag relationships in most financial time series at various frequencies to contribute to the heterogeneous nature of market participants. Hence, the study examines the interdependencies between cryptocurrencies, selected stocks markets of Africa, and Gold returns in a time-frequency domain before and during the COVID-19 pandemic. Using a day-to-day observations, from August 8th, 2015 to May 5th, 2020, we assess the benefits of portfolio diversification, hedges, and safe haven with the bi-wavelet technique. The findings reveal that gold and cryptocurrencies provide a safe haven, diversification and, hedge for investors of African stock especially in the Ghanaian stock market (short-term) and also during this COVID-19 period. These findings contribute to the literature on financial market interdependencies, asymmetries to demonstrate financial market participantsâ diverse investment horizons. Again, policymakers and governments of these stock markets should institute a sound system of controls in regulating stock markets. This will enable the benefits of safe haven, hedges or diversification to be efficiently realized for Gold and Cryptocurrencies during different market conditions.
This study explores the causal relationship between COVID-19 pandemic and Bitcoin returns by applying the time and frequency domain Granger causality framework. We find that COVID-19 has a causal effect on Bitcoin returns across time. We further find that the causal effect of COVID-19 on Bitcoin returns, varies across different frequencies from short to medium and long term. From a policy perspective, investors need to be alert while investing in Bitcoin.
The goal of this project is to develop a system that can predict the price of a cryptocurrency (Bitcoin) based on the sentiment of the input provided. This input will be supplied to the model using the Cryptopanic API, which will extract the latest news related to Bitcoin. These technological advancements can help us make accurate predictions thereby facilitating investments. We have tried to accomplish this by using a series of deep learning techniques and methodologies. Our decision to build this model using LSTM was based on the comparison of results between other algorithms like CNN (Convolutional Neural Network), GRU (Gated Recurrent Unit) and RNN (Recurrent Neural Network). Unlike technical analysis methods which are used for normal stock market prediction we have built a model which will be trained to classify news headlines based on the sentiment detected and give a predicted price. We believe that the use of LSTM to give accurate price prediction would be extremely useful for novice as well as professional Bitcoin traders. Also, it has been proven that public sentiments have been very influential in determining the price of Bitcoin and thus taking that into consideration would improve our understanding and prediction.
Muhammad Husaini Bin Mohd Sabri, Amgad Muneer, Shakirah Mohd Taib
Machine learning has become the backbone of bitcoin portfolio optimization in today's technological era. This research applies a deep neural network (DNN) model, Long Short-Term Memory (LSTM), to historical bitcoin prices and Sentiment Analysis to tweet data gathered from Twitter. The LSTM algorithm is used to train the model and forecast the future cryptocurrency price. Sentiment analysis, on the other hand, examines sentiment on Twitter to determine the relationship between sentiment and cryptocurrency price fluctuations. Sentiment analysis categorizes Twitter sentiment as positive or negative, and the fraction of positive and negative tweets is used to forecast bitcoin price fluctuations. The predicted price fluctuation data is then added to the LSTM predicted price to predict the new price for the next time frame. Finally, both models forecast future cryptocurrency prices and patterns, particularly Bitcoin.
Abstract This research makes the first attempt to design, optimize and use average true range (ATR)âbased trading systems for five popular cryptocurrencies. We used particle swarm optimization procedures to optimize systems with multiple objectives that are based on the ATR concept. Our aim was to determine the best configurations for each system that would maximize net profits, the profit factor, and the percentage of profitable trades. We demonstrate that the ATRâbased systems can predict the price trends of the examined cryptocurrencies. Our results also indicate that optimized Keltner Channelâbased systems improve the ability of the standâalone optimized ATR systems to forecast trends, net profits, and the profit factor. Finally, both systems perform better for long trades than for short trades.
Kokulo K. Lawuobahsumo, Bernardina Algieri, Leonardo Iania, Arturo Leccadito
We use a robust measure of non-linear dependence, the Gerber cross-correlation statistic, to study the cross-dependence between the returns on Bitcoin and a set of commodities, namely wheat, gold, platinum and crude oil WTI. The Gerber statistic enables us to obtain a more robust co-movement measure since it is neither affected by extremely large nor small movements that characterise financial time series; thus, it strips out noise from the data and allows us to capture effective co-movements between series when the movements are âsubstantialâ. Focusing on the period 2014â2022, we construct the bootstrapped confidence intervals for the Gerber statistic and test the null that all the Gerber cross-correlations up to lag kmax are zero. Our results indicate a low degree of dependence between Bitcoin and commodities prices, both when we consider contemporaneous correlation and when we employ correlations between current Bitcoin and lagged (one day, one week, or one month) commodities returns. Further, the cross-correlation between Bitcoin and commoditiesâ returns, although scanty, shows an increasing trend during periods of economic, health and financial turbulence. This increased cross-correlation of returns during hectic market periods could be due to the contagion effect of some markets by others, which could also explain the strong dependence across volatilities we detected. Based on our results, Bitcoin cannot be considered the ânew digital goldâ.