Xinchen Zhang, Linghao Zhang, Qincheng Zhou, Xu Jin
As a result of the fast growth of financial technology and artificial intelligence around the world, quantitative algorithms are now being employed in many classic futures and stock trading, as well as hot digital currency trades, among other applications today. Using the historical price series of Bitcoin and gold from 9/11/2016 to 9/10/2021, we investigate an LSTM-P neural network model for predicting the values of Bitcoin and gold in this research. We first employ a noise reduction approach based on the wavelet transform to smooth the fluctuations of the price data, which has been shown to increase the accuracy of subsequent predictions. Second, we apply a wavelet transform to diminish the influence of high-frequency noise components on prices. Third, in the price prediction model, we develop an optimized LSTM prediction model (LSPM-P) and train it using historical price data for gold and Bitcoin to make accurate predictions. As a consequence of our model, we have a high degree of accuracy when projecting future pricing. In addition, our LSTM-P model outperforms both the conventional LSTM models and other time series forecasting models in terms of accuracy and precision.
The Bitcoin mining process is energy intensive, which can hamper the much-desired ecological balance. Given that the persistence of high levels of energy consumption of Bitcoin could have permanent policy implications, we examine the presence of long memory in the daily data of the Bitcoin Energy Consumption Index (BECI) (BECI upper bound, BECI lower bound, and BECI average) covering the period 25 February 2017 to 25 January 2022. Employing fractionally integrated GARCH (FIGARCH) and multifractal detrended fluctuation analysis (MFDFA) models to estimate the order of fractional integrating parameter and compute the Hurst exponent, which measures long memory, this study shows that distant series observations are strongly autocorrelated and long memory exists in most cases, although mean-reversion is observed at the first difference of the data series. Such evidence for the profound presence of long memory suggests the suitability of applying permanent policies regarding the use of alternate energy for mining; otherwise, transitory policy would quickly become obsolete. We also suggest the replacement of 'proof-of-work' with 'proof-of-space' or 'proof-of-stake', although with a trade-off (possible security breach) to reduce the carbon footprint, the implementation of direct tax on mining volume, or the mandatory use of carbon credits to restrict the environmental damage.
Michael Demmler, Amilcar Orlian Fernández Domínguez
This article explores the concepts of cryptocurrencies and speculative bubbles, as Bitcoin’s price behaviour shares characteristics with speculative bubbles that have occurred in recent years. Using a quantitative research design, the study examines daily market prices for the period between 2013 and 2019. Statistical moments, return stationarity, TARCH-type model estimations and Supremum Augmented Dickey-Fuller and Generalised Supremum Augmented Dickey-Fuller tests are analysed. We find evidence for multiple speculative bubble tendencies in Bitcoin prices caused by speculation, which reached their maximum at the end of 2017. Our results are in line with recent studies, which characterise Bitcoin as both highly speculative and vulnerable to financial bubbles.
Syed Ali Raza, Larisa Yarovaya, Khaled Guesmi, Nida Shah
Purpose This article aims to uncover the impact of Google Trends on cryptocurrency markets beyond Bitcoin during the time of increased attention to altcoins, especially during the COVID-19 pandemic. Design/methodology/approach This paper analyses the nexus among the Google Trends and six cryptocurrencies, namely Bitcoin, New Economy Movement (NEM), Dash, Ethereum, Ripple and Litecoin by utilizing the causality-in-quantiles technique on data comprised of the years January 2016–March 2021. Findings The findings show that Google Trends cause the Litecoin, Bitcoin, Ripple, Ethereum and NEM prices at majority of the quantiles except for Dash. Originality/value The findings will help investors to develop more in-depth understanding of impact of Google Trends on cryptocurrency prices and build successful trading strategies in a more matured digital assets ecosystem.
Sulalitha Bowala, Japjeet Singh, A. Thavaneswaran, Ruppa K. Thulasiram · 5 authors
Data-driven volatility models and neuro-volatility models have the potential to revolutionize the area of Computational Finance. Volatility measures the variation of a time series data, and thus it is also a driving factor for the risk forecasting of returns from investment in cryptocurrencies. A cryptocurrency is a decentralized medium of exchange that relies on cryptographic primitives to facilitate the trustless transfer of value between different parties. Instead of being physical money, cryptocurrency payments exist purely as digital entries on an online ledger called blockchain that describe specific transactions.Many commonly used risk forecasting models do not take into account the uncertainty associated with the volatility of an underlying asset to obtain the risk forecasts. Some tools from the fuzzy set theory can be incorporated into the forecasting models to account for this uncertainty. Interest in the use of hybrid models for fuzzy volatility forecasts is growing. However, a major drawback is that the fuzzy coefficient hybrid models used in fuzzy volatility forecasts are not data-driven. This paper uses fuzzy set theory with data-driven volatility and data-driven neuro-volatility forecasts to study the fuzzy risk forecasts. The study focuses on long-term volatility forecasts with daily price data while briefly exploring forecasting models with high-frequency (hourly) data as an avenue for future research. Simple yet effective models incorporating fuzziness to obtain fuzzy risk volatility forecasts and fuzzy VaR forecasts are presented. The key underlying idea, unlike the existing risk forecasting, is the use of a hybrid nonlinear adaptive fuzzy model for volatility.
This study examines the effects of the epidemic and the price bubble on the effectiveness of the cryptocurrency market. In this Research, We collect the daily closing price of 5 cryptocurrencies from https://coinmarketcap.com/. The data was taken from 01 September 2017 to 14 December 2021 with a total data or sample of 1231 daily data from each currency or a total of 6155 samples from a total of all tested currencies. The five cryptocurrencies are Litecoin (LTC), Cardano (ADA), Ethereum (ETH), Ripple (XRP), and Bitcoin (BTC). To measure market inefficiency we use magnitude market inefficiency (MIM) and the study by Le Tran and Leirvik (2019) is used to establish the adjusted magnitude of market inefficiency (AMIM). In this study AMIMt is calculated on a daily frequency by using the daily closing price as the basis for calculation.We found that the three periods of the cryptocurrency bubble in the cryptocurrency market occurred in late 2017, early 2018, and July 2020. The cryptocurrency financial bubble had a lesser impact than the announcement of a worldwide pandemic being declared for COVID-19 on March 11, 2020. It is very likely that a bubble will occur during July 2020 related to the declaration that COVID-19 is a pandemic of global scope.
Yizhi Wang, Brian M. Lucey, Samuel A. Vigne, Larisa Yarovaya
Based on coverage of over 660m news stories from LexisNexis News & Business between 2015–2021, we provide two new indices around the growing area of Central Bank Digital Currency (CBDC): the CBDC Uncertainty Index (CBDCUI) and CBDC Attention Index (CBDCAI). We show that both indices spiked during news related to new developments in CBDC and in relation to digital currency news items. We demonstrate that CBDC indices have a significant negative relationship with the volatilities of the MSCI World Banks Index, USEPU, and the FTSE All-World Index, and positive with the volatilities of cryptocurrency markets, foreign exchange markets, bond markets, VIX, and gold. Our results suggest that financial markets are more sensitive to CBDC Uncertainty than CBDC Attention as proxy by these indices. These findings contain useful insights to individual and institutional investors, and can guide policymakers, regulators, and the media on how CBDC evolved as a barometer in the new digital-currency era.
This paper aims to analyze the effect of Bitcoin on portfolio optimization using mean-variance, conditional value-at-risk (CVaR), and Markov regime switching approaches. I assessed each approach and developed the next based on the prior approach's weaknesses until I ended with a high level of confidence in the final approach. Though the results of mean-variance and CVaR frameworks indicate that Bitcoin improves the diversification of a well-diversified international portfolio, they assume that assets' returns are developed linearly and normally distributed. However, the Bitcoin return does not have both of these characteristics. Due to this, I developed a Markov regime switching approach to analyze the effect of Bitcoin on an international portfolio performance. The results show that there are two regimes based on the assets' returns: 1- bear state, where returns have low means and high volatility, 2- bull state, where returns have high means and low volatility.
Abstract As a new type of currency introduced in the new millennium, cryptocurrency has established its ecosystems and attracts many people to use and invest in it. However, cryptocurrencies are highly dynamic and volatile, making it challenging to predict their future values. In this research, we use a multivariate prediction approach and three different recurrent neural networks (RNNs), namely the long short-term memory (LSTM), the bidirectional LSTM (Bi-LSTM), and the gated recurrent unit (GRU). We also propose simple three layers deep networks architecture for the regression task in this study. From the experimental results on five major cryptocurrencies, i.e., Bitcoin (BTC), Ethereum (ETH), Cardano (ADA), Tether (USDT), and Binance Coin (BNB), we find that both Bi-LSTM and GRU have similar performance results in terms of accuracy. However, in terms of the execution time, both LSTM and GRU have similar results, where GRU is slightly better and has lower variation results on average.
In this work, I studied whether news media sentiments have an impact on Bitcoin volatility. In doing so, I applied three different range-based volatility estimates along with two different sentiments, namely psychological sentiments and financial sentiments, incorporating four various sentiment dictionaries. By analyzing 17,490 news coverages by 91 major English-language newspapers listed in the LexisNexis database from around the globe from January 2012 until August 2021, I found news media sentiments to play a significant role in Bitcoin volatility. Following the heterogeneous autoregressive model for realized volatility (HAR-RV)—which uses the heterogeneous market idea to create a simple additive volatility model at different scales to learn which factor is influencing the time series—along with news sentiments as explanatory variables, showed a better fit and higher forecasting accuracy. Furthermore, I also found that psychological sentiments have medium-term and financial sentiments have long-term effects on Bitcoin volatility. Moreover, the National Research Council Emotion Lexicon showed the main emotional drivers of Bitcoin volatility to be anticipation and trust.
Our research investigated the effect of COVID-19 cases (cumulative positive and death cases) on Bitcoin price in the top three infected countries based on WHO (United States, Brazil, and India). Macro-financial and internal factors are employed as the other independent determinants of Bitcoin prices. We utilized feasible generalized least squares (FGLS) alongside generalized method of moments (GMM) for robustness check. The output revealed robustness across different econometric models. The findings unraveled that COVID-19 cumulative positive cases brought positive but insignificant impacts on Bitcoin returns, while its death cases stated the opposite. Macro-financial factors represented by stock indices and gold price imposed that they could be alternative investments to Bitcoin under the uncertain times of COVID-19. Liquidity and volume in respect to return discovery of Bitcoin are imperative instruments, as these internal factors move in the same direction with Bitcoin's demand and return movement. Efficiency in internal factors drives investors’ demand, hence pushing the increase in Bitcoin's return.
Amadeo Christopher, Kevin Deniswara, Bambang Leo Handoko
This research aims to analyze the calculation of volatility stage from five cryptocurrency products, which are Bitcoin, Ethereum, Binance Coin, Dashcoin, and Litecoin from 1st January 2018 to 1st April 2021 where it consists of calculation of each of the cryptocurrency products' volatility. The research method is a quantitative method by gaining data from Investing.com. Then, analyzing the data using Autoregressive Conditional Heteroscedasticity (ARCH) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models. This research aims to know whether ARCH and GARCH models apply to daily life situations in the field. The result shows that the data from ARCH and GARCH models are not suitable on daily basis. Further research should calculate cryptocurrency products to use differentiated GARCH models, such as GJR-GARCH or GARCH-MIDAS. It is also better to calculate the volatility of cryptocurrency products annually. According to some thesis, the volatility cryptocurrency products are more suitable to calculate annually than daily.
Purpose This paper aims to analyse COVID-19 indices and blockchain features on Bitcoin and Ethereum returns, respectively. The authors focus on the most used and owned cryptocurrencies that cover Europe, the US and Asian countries. Design/methodology/approach An autoregressive distributed lag panel (pooled mean group and mean group) is utilized, and a robustness check is incorporated by using a Random Effect Model and Generalized Method of Moments (GMM). Findings Four new findings were discovered, including (1) the vaccine confidence index (VCI) pushes economic recovery and increased demand for the Bitcoin market, but the opposite result was interestingly observed from Ethereum; (2) the blockchain features were revealed to be essential to Bitcoin, while they were irrelevant to Ethereum for short-run country-specific results; (3) the hash rate and network difficulty moved inversely during the pandemic; and (4) the government played a significant role in taking action during uncertain times and regarding cryptocurrency policies. Research limitations/implications VCI is constructed by the most used vaccine type in our sample countries (i.e. Pfizer), as the data for a specific classification by each type is still unavailable. Practical implications Providing an evenly distributed vaccination program primary vaccination series against COVID-19 to the citizens is an essential duty of the government. Bitcoin policymakers and investors should watch the COVID-19 vaccine distributions closely as it will affect its return. Ethereum is emphasized to keep developing its smart contract which appeared to outplay other blockchain features. Cryptocurrency investors should be wise in their investment decisions by analysing the news thoroughly. Social implications This research emphasizes that the success in the roll-out of COVID-19 vaccination requires citizens' willingness to participate and their trust in the vaccine's efficacy. Such self-awareness and self-discipline in society can ultimately empower individuals and stabilise the economy. Nevertheless, the implementation of health protocols is still highly required to prevent the spread of new variants of COVID-19. Originality/value This is the first study that attempts to construct a VCI which denotes the confidence derived from the administration of full-dose COVID-19 vaccines (an initial vaccine and a second vaccine). The authors further find the impact on cryptocurrency returns. Next, blockchain size is utilized as a new determinant of cryptocurrencies.
Abstract The Bitcoin futures market has grown rapidly since its 2017 introduction. Along with enabling institutional traders to access a regulated cryptocurrency product, futures provide a means to improve market efficiency by shorting Bitcoin. We examine trading behavior in Bitcoin futures utilizing the Commodity Futures Trading Commission Commitment of Traders report. Leveraged money traders tend to hold the largest positions, be net short, and their trading behavior plays a key role in the Bitcoin futures market. Our empirical results show that leveraged money traders display market timing ability, largely by adjusting their short positions. It seems that other trader types follow this “smart money” in adjusting their own positions in subsequent periods. We also demonstrate that it is possible to construct profitable trading strategies based on observed variations in leveraged money positions.
The Covid-19 crisis, or even the restrictions, quarantines, and lifestyle changes that it brought, occurred in the year 2020. Economic statistics mentioned the effects of the crisis. Stock exchanges around the world, for example, have experienced substantial collapses, leading in a drop in the value of various individuals' assets. During the Covid-19 crisis, this master's study attempts to understand the utility of cryptocurrencies for hedging and safe haven objectives. It's difficult to make consistent conclusions about the suitability of cryptocurrencies for hedging against financial market risks based on existing research. Previous results have varied greatly based on the model utilized, the time period, and the asset risk hedged. Usability for hedging purposes varies in general. In this study, we wrote an article based on the most popular cryptocurrencies in the world and their development mechanisms, history and other facts. The article also discusses the role and importance of cryptocurrencies as a means of payment in the future. Keywords: cryptocurrency, covid-19 crisis, bitcoin, ethereum, blockchain technology
Overview The aim of the International Conferences "Economic Scientific Research-Theoretical, Empirical and Practical Approaches"- (ESPERA), initiated in 2013 by the "Costin C. Kirițescu" National Institute for Economic Research (NIER) within the Romanian Academy is to present and evaluate the economic scientific research portfolio, to argue and substantiate the Romanian development strategies - including European and global best practices, to provide an opportunity for researches, practitioners, and academics interested in economic scientific research, both theoretical, practical and empirical discuss and exchange insightful research ideas. The 7th edition of the International Conferences “Economic Scientific Research-Theoretical, Empirical and Practical Approaches”- (ESPERA), under the title ”30 Years of Inspiring Academic Economic Research – From the Transition to a Market Economy to the Interlinked Crises of 21st Century” was organized virtually during 26th -27th November 2020, In Bucharest, Romania. The event, dedicated to the 30th anniversary of NIER and its economic research network of its return under the auspices of the Romanian Academy, will include a scientific program of wide diversity initiatives, bringing together researchers from all NIER institutes and centers, members of the Romanian Academy, Romanian academic researchers and also guests from other countries. The
YUTENG LIU, YUXUAN TIAN, Tianxing Zhou, HONGZHOU WANG
Since the rise of Data Analysis, forecasting of price markets has never stopped and there are numerous forecasting methods, but most of them are only for a single price data.We have chosen bitcoin and gold as the subjects of our study, addressing the multi-objective related prediction problem, explores the volatility relationship between gold and bitcoin to improve its forecasting accuracy, and in doing so, we establishes multiple prediction models,and determines the relationship between prediction accuracy and prediction range.