The COVID-19 pandemic and the bearish market have led investors to find a safe-haven asset during this financial turbulence. Gold, US Dollar, and Bitcoin traditionally could be safe-haven assets in previous financial crises. However, safe-haven assets are mainly different during each market crash. Therefore, this paper aims to examine gold, US dollars, and Bitcoin as safe-haven assets during the COVID-19 market turmoil in several South East Asian countries such as Indonesia, Malaysia, Singapore, and the Philippines. All variables use daily data time series from January 2020 - September 2020. This study will conduct an empirical analysis using Generalized Autoregressive Conditional Heteroscedasticity (GARCH). Our result shows that during the COVID-19 pandemic, US Dollar could act as a safe-haven asset in Indonesia, Malaysia, and the Philippines. It implies that when the condition is uncertain during a pandemic, many investors switch their investments to US dollars in those three countries. On the other hand, gold and bitcoin are not safe-haven assets, but they could only act as hedging for several countries in South-East Asia.
This paper empirically assesses the ability of three putative stablecoins (two dollar-backed, Tether and USD Coin; and one gold-backed, Digix Gold) to mitigate the risk of facing severe losses (downside risk) of a traditional cryptocurrency portfolio. There are institutional features that induce cryptoinvestors to use stablecoins as diversifiers instead of withdrawing dollars or adding assets traditionally considered as safe havens, such as gold, crude oil, etc. Stablecoins, however, are not as stable as their name and collateralized peg suggest. A monthly rebalance experiment is conducted over an out-of-sample period considering higher order conditional moments when dynamically measuring the tail risk of cryptocurrency portfolios. The empirical evidence shows that the low conditional correlations of dollar-backed stablecoins with cryptocurrency portfolios make them particularly suitable as a hedge for crypto investors. It also shows that all stablecoins considered have high diversification capacities by systematically reducing portfolio tail risk.
Recent years have witnessed the rapid development of bitcoin as the first digital currency. Considering the advantages of bitcoin for both individuals and society, the price prediction of bitcoin is a hot topic. However, there remain two main challenges to be addressed in this task. Firstly, because bitcoin is vulnerable to the attitudes of the investors, incorporating the sentiment semantics from the social media into the prediction is challenging. Secondly, it is intractable to predict extreme volatility of the bitcoin price. To tackle the above challenges, this paper proposes to incorporate sentiment and temporal information simultaneously. For the first challenge, this paper employs external unsupervised corpus to conduct the domain-specific post-pretraining on the off-the-shelf language model. And the sentiment analysis on the tweets is done to obtain the scores. For the second one, Long Short Term Memory (LSTM) network is leveraged to joint model the temporal price data and the sentiment scores, thus deriving the final predictions. Experiments on the real data show that compared with the single-layer LSTM model, the model in this paper works better, which provides help for investors to specify trading strategies and also provides implications for government agencies that are developing digital currencies.
Cryptocurrency is the technological development of currency into the digital form which is considered for sustainable development. There are large numbers of investors, who have started investing on cryptocurrency and it is the emerging financial market across the globe. But the cryptocurrency poses various threats to the investor safety and security and it is hazardous to the environmental condition. There is still no consensus on cryptocurrency whether it is a better option to invest or not. Hence this studies to shed light on to the path of investment opinion about cryptocurrency. This paper will give the perspective of the current trends in the crypto market. This concept will be discussed through three steps: the good cryptocurrency and how the financial market has evolved into the digital technology and secondly now it could emerge as a sustainable development in the field of finance. Hence the technology encourages the development of investor and green cryptocurrency. There are various individual issues, related to bad cryptocurrency like money laundering, scam, crypto whale and viruses. Finally, the ugly cryptocurrency in the financial sector affects the environmental condition and how it could affect the ecosystem of the planet due to crypto mining. This study suggested the token offering organization to adopt the proof-of-authority process than the proof-of-work, to avoid carbon emission and more volume of energy consumption. This study also suggests the investor to invest on the green cryptocurrency than the hazardous cryptocurrency (bitcoin), which affects the environmental condition.
The use of cryptocurrencies is trending worldwide and represents cutting-edge development in the financial technology sector. Bitcoin is the most widely used cryptocurrency and holds the top spot in terms of market capitalization at present. This study contributes to the continuing discussion regarding whether Bitcoin is useful for financial investments. This paper aims to evaluate the viability of bitcoin as an alternative instrument for portfolio diversification and hedging. In this paper, the volatility of bitcoin is compared with stock market indices (SENSEX, Nifty-50), major currencies (USD (${\$}$), Euro (€), Pound Sterling (£), and Japanese Yen (¥) and commodity (Gold). DCC-GARCH model has been applied by using R language to evaluate the potential of bitcoin as an alternative hedging and diversification tool for the short and long term. The result of this study represents that Bitcoin can be used as an alternative tool for hedging and diversification.
Bitcoin is the most famous digital currency in the world and has become an investment asset. Prediction is one of the important matters in the investment market. In the economic field, there are different studies on the reasons for the price change of Bitcoin and how to predict the price trend of Bitcoin or how Bitcoin studies the market. Therefore, for Bitcoin, predicting the trend of Bitcoin price can effectively help Bitcoin investors. Data from www. Coingecko, the price of bitcoin is sorted according to the time sequence. Using the time series model, the change of bitcoin price in a specific period which is from 28 April 2013 to 22 August 2022 is calculated to predict the future trend of bitcoin price. Data preprocessing includes attributes removal, stationary test, and differencing. In predicting the price of Bitcoin, the ARIMA method that can produce high accuracy in short-term prediction is adopted. Use prediction test AIC and Check the residuals to select the best prediction model among the candidate models. The results of model testing show that AIC of ARIMA (5,1,2) is the smallest among all candidate models, and the results of residual check also show that ARIMA (5,1,2) model is the best model for predicting four periods.
In this paper, Autoregressive Integrated Moving Average (ARIMA) model is used for analysing and forecasting the adjusted closing price of bitcoin. The whole dataset used is daily bitcoin closing price dating from Jan 2017 to Sep 2022. However, for testing the performance of the ARIMA model, the dataset is divided into two parts: the ARIMA model is built on training set and later using the test set to check the accuracy of the prediction. Two models, namely ARIMA (5, 2, 1) and ARIMA (0, 2, 2) are selected and comparations between them are made. ARIMA (5, 2, 1) is chosen with stepwise selection and approximated information criteria while ARIMA (0, 2, 2) is without stepwise selection and the information criteria is not approximated. Both pass the residual test and ARIMA (0, 2, 2) is slightly better according to AIC, AICc and BIC. Later, the predicting accuracy of the two models for different forecasting periods (5-day, 10-day, and long-term forecasting) are compared. It is not surprising that ARIMA performs better while making short term prediction. The 5-day and 10-day forecast works well while the long-term forecast is of limited practical value.
Given the skyrocketing returns earned by bitcoin, it has received widespread attention as an investment asset. The shocks experienced by stock and bond markets over time and especially during the COVID-19 pandemic has led to an evaluation of bitcoin as a wealth protection asset, a role that gold has played until now. The current paper tests the hedging and safe haven properties of bitcoin in a broad portfolio of both developed and emerging markets stocks, bonds and real estate over a period of 10 years and during COVID-19 pandemic. Using a DCC-GARCH method, the study finds weak hedge and safe haven benefits of bitcoin. The results of the study establish that there is still a long way to go before bitcoin displays a strong safe haven behavior. However, there is a need for portfolio managers to become more cognizant about bitcoin given its potential to protect their portfolios.
The time series movements of Bitcoin prices are commonly characterized as highly nonlinear and volatile in nature across economic periods, when compared to the characteristics of traditional asset classes, such as equities and commodities. From a risk management perspective, such behaviors pose challenges, given the difficulty in quantifying and modeling Bitcoin’s price volatility. In this study, we propose hybrid analytical techniques that combine the strengths of the non-stationary properties of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models with the nonlinear modeling capabilities of deep learning algorithms, such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM) algorithms with single, double, and triple layer network architectures to forecast Bitcoin’s realized price volatility. Our findings, both in-sample and out-of-sample, show that such hybrid models can generate accurate forecasts of Bitcoin’s price volatility.
With the rise of Blockchain technology, the cryptocurrency market has been gaining significant interest. In particular, the number of cryptocurrency traders and the market capitalization have grown tremendously. However, predicting cryptocurrency price is very challenging and difficult due to the high price volatility. In this paper, we propose a classification machine learning approach in order to predict the direction of the market (i.e., if the market is going up or down). We identify key features such as Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD) to feed the machine learning model. We illustrate our approach through the analysis of Bitcoin close price. We evaluate the proposed approach via different simulations. Particularly, we provide a backtesting strategy. The evaluation results show that the proposed machine learning approach provides buy and sell signals with more than 86% accuracy.
This research provides the very first empirical investigation of the impact of the Russia-Ukraine war on the cryptocurrency market (Bitcoin trading volume, and returns). The findings indicate that the Russia-Ukraine war impedes Bitcoin trading volume. A 1% increase in the Russia-Ukraine war leads to a 0.2% reduction in Bitcoin trading volume. The findings also indicate that the impact is more pronounced during the post-invasion period, especially after one week of the invasion. Finally, the Russia-Ukraine war predicts Bitcoin returns in both the short and long run.
Open access
Environmental and Biological Research in Conflict Zones
This paper uses ARIMA-GARCH model to predict the prices of gold and bitcoin from 9/10/2016 to 9/11/2021, and fully analyzes the transaction date and constructs a price prediction model based on ARIMA-GARCH model, which can accurately predict the short-term price fluctuations in the future. When the transaction commission increases, the investor’s return decreases, but the gap between the investor’s return and the basic investment return gradually shortens. According to the official data in this thesis, the simulated transaction finally changed 1000 dollars into more than 9700 dollars, which performs well in the actual market.
Aymen TURKI, Ahmed OBEID, Sahar Loukil, Ahmed Jeribi
This study examines the connectedness between G7 indices, Bitcoin, and oil during the COVID-19 pandemic. Based on daily data from January 1, 2016 to April 1, 2021, a vector auto-regression model and an impulse response function are employed to illustrate the time path of these assets following own and cross-shocks. Our study exhibits the considerable effect of the pandemic on increasing directional causalities and time-varying connectedness between G7 indices, Bitcoin, and oil. The findings indicate that G7 indices’ own shocks almost immediately lower forecasts of stock return urging the diversification to reduce risk. Moreover, the significant negative response of oil to shocks amid the pandemic reflects its high vulnerability during mitigated periods. Unlike other countries, we find a relative resilience of Bitcoin to S&P 500 shocks, and we consequently recommend Bitcoin as a diversifier to Americaninvestors during the pandemic. Our results are useful for both investors and policymakers who need to think ahead, rather than waiting to have a downside G7 returns movement in turbulent periods.
This paper extends the study of Bourghelle et al. (2022) to check whether collective emotions could help to forecast bitcoin volatility over the period 2018-2021. To this end, we first assess whether consideration of investor sentiment and collective emotions can give us clearer insights into bitcoin dynamics over the period in question and whether they can help to explain the different price fluctuations. Formally, we ran causality tests and, as in Bourghelle et al. (2022), built a two-equation nonlinear vector autoregressive (VAR) model to assess for further lead-lag effects between bitcoin volatility and collective emotions. Second, we proposed in-sample forecasts of bitcoin volatility to test whether our forecasts could be improved by taking investors’emotions and sentiment into account. Our findings show that market sentiment and investors’ emotions provide useful information that can help to explainfluctuations, structural breaks, and changes in bitcoin volatility. Further, collective emotions improve bitcoin volatility forecasting as our nonlinear model, including emotions-related news, supplants the benchmark linear model.
Currently, the most popular cryptocurrency is bitcoin. Predicting the future value of bitcoin can help investors to make more educated decisions and to provide authorities with a point of reference for evaluating cryptocurrency. The novelty of the proposed prediction models lies in the use of artificial intelligence to identify movement cryptocurrency prices, particularly bitcoin prices. A forecasting model that can accurately and reliably predict the market’s volatility and price variations is necessary for portfolio management and optimization in this continually expanding financial market. In this paper, we investigate a time series analysis that makes use of deep learning to investigate volatility and provide an explanation for this behavior. Our findings have managerial ramifications, such as the potential for developing a product for investors. This can help to expand upon our model by adjusting various hyperparameters to produce a more accurate model for predicting the price of cryptocurrencies. Another possible managerial implication of our findings is the potential for developing a product for investors, as it can predict the price of cryptocurrencies more accurately. The proposed models were evaluated by collecting historical bitcoin prices from 1 January 2021 to 16 June 2022. The results analysis of the GRU and MLP models revealed that the MLP model achieved highly efficient regression, at R = 99.15% during the training phase and R = 98.90% during the testing phase. These findings have the potential to significantly influence the appropriateness of asset pricing, considering the uncertainties caused by digital currencies. In addition, these findings provide instruments that contribute to establishing stability in cryptocurrency markets. By assisting asset assessments of cryptocurrencies, such as bitcoin, our models deliver high and steady success outcomes over a future prediction horizon. In general, the models described in this article offer approximately accurate estimations of the real value of the bitcoin market. Because the models enable users to assess the timing of bitcoin sales and purchases more accurately, they have the potential to influence the economy significantly when put to use by investors and traders.
Nosipho Mthembu, Kazeem Abimbola Sanusi, Joel Hinaunye Eita
The study investigates the effects of stock market volatility and cybercrime on cryptocurrency returns in the South African economy. Daily time series data on four different types of cryptocurrencies (Bitcoin, Ethereum, Tether, and BMB) were employed. The data covers the period from 1 January 2019–31 December 2021. The study employed the dynamic conditional correlation (DCC GARCH) and Bayesian liner regression model to investigate time-varying correlations among the variables. Empirical findings suggest that stock market volatility has a positive impact on the returns of BNB, Bitcoin, and Ethereum. However, it has a negative impact on Tether. Expectedly, cybercrime poses negative impacts on the returns of BNB, Bitcoin, and Ethereum but could be said to have no impact on the returns of Tether. The study concludes that ongoing efforts to reduce cybercrime activities need to be strengthened to further the use of digital currencies.
Bitcoin is a very attractive financial asset for traders and speculators worldwide. This paper first describes how traditional analysis methods can be applied to build statistical models (ARIMA and GARCH) to forecast future returns of bitcoin trading activity. Then a recurrent neural network (RNN) is constructed and trained for bitcoin time-series data. The RNN is a deep learning model for time series, which demonstrates ability to acquire more pattern information from the data.
Wajid Shakeel Ahmed, Ahsan Mehmood, Talha Sheikh, Allah Bachaya
This paper investigated the relationship between cryptocurrencies and emerging stock market indices using fractional integration and co-integration technique. Particularly, fractional integration is applied to examine stochastic properties of individual assets and fractional cointegration to analyse bivariate connectedness. Our findings unveil the absence of mean reversion in majority cases which indicates high persistence in series. Furthermore, bivariate analysis reveals disconnection between cryptocurrencies prices and stock indices. Surprisingly, a different picture emerges on using conditional volatility instead of prices. Like, conditional volatility-based estimation uncovers evidence of mean reversion in univariate analysis as expected. There is some evidence of cointegration on volatility grounds between cryptocurrencies and emerging stock market indices. Our findings implies that investment decision regarding digital currencies should be taken cautiously. As cryptocurrencies are extremely volatile with high degree of persistence which can make them counterproductive.
J.C. Chan, Michael Nayat Young, Yogi Tri Prasetyo, Reny Nadlifatin
This paper presents a comparative analysis of Mean-Variance Theory (MVT) and Safety - First model with SP/A criterion utilizing Non-Fungible Tokens and Collectibles in the crypto market. The criterion used to determine the investment pool is the mean volume price of the Top 100 NFTs and collectibles. Historical data are gathered and computed for return estimation with equal probability. Also, different portfolio weight threshold parameters were used for the mean-variance (risk-return threshold) and safety first (relative value for fear and hope). Using backtesting, safety-first portfolios show higher cumulative returns from the US dollar benchmark. Overall, this study offers portfolio optimization and an alternative portfolio selection model as references for generic investment procedures for digital asset investors, and for educational purposes.
The research and investment community seems to ignore the long-term sustainability of Bitcoin, which is reflected in four flaws: transaction fees, miners' revenue, concentration and electricity consumption. While most of the authors have aimed to examine one topic at a time, with a particular interest in electricity consumption and carbon footprint, the aim of this paper is to examine all these issues simultaneously to provide a more comprehensive view on long-term sustainability of Bitcoin. This paper looks at these flaws and reveals why Bitcoin is not sustainable in the long run, how decentralization is being lost, how the design is putting artificial and unrealistic pressure on the ecosystem, while all being powered by an unjustifiable amount of dirty electricity sources. Our main findings are as follows. Firstly, transaction fees are already high and set to increase in time, further discriminating small transactions against big ones. Secondly, miners' revenue comes mostly from the block reward. The block reward is the main income source for miners, but is set to be cut on a regular basis, making miners' revenue not sustainable in the long run. Thirdly, miner concentration is already an issue, with a possibility of deepening even more and diminishing the idea of decentralization. Fourthly, the high electricity demand and the associated carbon footprint thus cannot be justified by any means. We deem our results useful for overall policy and regulatory implications.
The global economy receives a catastrophic blow due to the COVID-19 epidemic, with long-term pessimism shown towards the global market and exponential increasing expectation for looking for a reliable safe-haven asset. Now, it seems the possible alternatives to traditional currencies issued and backed by governments have primarily emerged - in the form of Bitcoin and Ethereum, as well as several other cryptocurrencies. Cryptocurrencies have been on the market for a long time and have been controversial in the international financial markets for their unique properties. Since the outbreak of COVID-19, the price of cryptocurrencies has seen an unprecedented increase. Whether the price increase of cryptocurrencies is linked to the COVID-19 outbreak is a mystery. This paper will focus on exploring this question through a linear regression machine learning model. The data in the U.S.A are used here. Our results show that the price of bitcoin is significantly related to the price of Ethereum. There is some correlation between covid-19 new cases incensement and the price of Bitcoin and Ethereum, indicating the legitimacy of predicting cryptocurrencies’ price using covid-19 new cases incensement as a factor.