This paper examines the sentiment spillovers among oil, gold, and Bitcoin markets by employing spillovers index methods in a time-frequency framework. We find that the total sentiment spillover among crude oil, gold and Bitcoin markets is time-varying and is greatly affected by major market events. The directional sentiment spillovers are also time-varying. On average, the Bitcoin market is the major transmitter of directional sentiment spillovers, whereas the crude oil and gold markets are the major receivers. In particular, the sentiment spillover effects are major created at high-frequency components, implying that the markets rapidly process the sentiment spillover effects and the shock is transmitted over the short-term. Moreover, we also find that the sentiment spillover effects differ significantly in term of intensity and direction when compared with return and volatility spillover effects. The present study has certain applications for investors and policymakers.
The paper focuses on the analysis of the cryptocurrency open innovation market to predict sustainable growth in the future. The nature of cryptocurrencies ‘development leads to the rapid increase in their popularity and spread of trading at this new market. The high volatility of these assets is encouraging to understand and predict their price in ever changing market environment. The paper proposed the pool complexity approach to choose optimal technology using social activity in the internet, trading parameters, technical indicators and other cryptocurrency data. According to the results of the analysis, the most effective and promising cryptocurrency is EOS cryptocurrency, which has the lowest complexity and commission level among the analyzed digital currencies and allows you to implement third-party applications in the system.
COVID-19 affects significant human activity around the globe, including Bitcoin prices. The Bitcoin price is well known for its volatility, so it is not a big shocker when the panic-selling occurs during the pandemic. However, the mechanism to cope with these breakouts, especially the bearish one, is contentious. The experts give numerous pieces of advice with different conclusions in the end. It is also the same with Machine Learning. Various kernels show different results regarding how the price will move. It depends on the window size, how the data is being preprocessed, and the algorithm used. This paper inspects the best combination that various machine learning can offer with a linear approach to navigate the price prediction based on its depth interval, window size until the algorithms themselves. This paper also proposed a new approach to seeing the prediction range called s-steps ahead prediction using a linear model. The result shows that simple machine learning can herd 99.715% profit even during the bearish breakout.
In this study we discuss the determinants of the BRICS and GCC stock market returns during the COVID-19 outbreak. We employ the OLS regression to discern how crypto-currencies, VIX, oil, GOLD prices, and the number of COVID-19 cases and deaths, affect the Gulf and BRICS stock markets. We find that Bitcoin and Ethereum can generate benefits from portfolio diversification and hedging strategies but not from safe haven strategies for Russia, Brazil, Abu-Dhabi, Bahrain, and Qatar financial investors during the COVID-19 outbreak. Our results reveal that Gold is neither hedge nor a safe haven but is only an effective diversifier for investors during the COVID-19 outbreak. The results indicated that among all the BRICS and GCC stock indexes, the expected volatility of the US stock market has an effect only on china and Kuwait financial markets. Finally, our results show that the growth rate of confirmed COVID-19 cases has a negative impact only on South Africa and Brazil stock market.
In this paper, we explore the volatility spillovers across different Bitcoin markets. We decompose the realized volatility into common and idiosyncratic volatilities, as well as the good and bad volatilities. Then the asymmetry in volatility spillovers between Bitcoin markets is measured by the DY (Diebold and Yilmaz) index. In addition, we construct statistics to test the asymmetry in volatility spillovers between different Bitcoin markets. The results are achieved as follows. The spillovers of systematic and idiosyncratic volatilities dominate the connectedness among different Bitcoin markets. In addition, the idiosyncratic volatility spillovers are more easily influenced by policies. Good volatility spillovers dominate the Bitcoin markets and change over time. The further results suggest that there is significant asymmetry between systematic and idiosyncratic volatility spillovers in the Bitcoin markets, while the asymmetries between good and bad volatility spillovers are heterogeneous in different markets. The findings in this paper can provide some suggestions for regulators controlling market stability and investors generating investment strategies.
The present study is on the five cryptocurrency daily mean return time series linearity dynamics during the Covid-19 period. These cryptocurrencies were chosen based on their influence on the market, primarily driven by its market capitalisation. Tether is included as the most important stable coin on the market, nominally pegged to the U.S. dollar (USD). The reason to investigate it is that there are some inconsistencies in its behaviour as opposed to the other four cryptocurrencies. This study found that the behaviour of Tether cryptocurrency daily average return time series pattern is highly nonlinear and chaotic in nature, whereas the other four cryptocurrencies (namely Bitcoin, Ethereum, XRP and Bitcoin Cash) daily average return time series were found to be linear in nature. To further study Tether’s nonlinear time series rich dynamics, this study deployed one category of the regime switching models popularly known as the threshold regressions. The study estimates fairly suggest that both the threshold autoregression (TAR) and smooth transition autoregressive (STAR) models with lag 1 are adequate to capture the rich nonlinear and chaotic dynamics of Tether’s daily average return time series.
Many traders participate in activities known as "day-trading", trading Bitcoin against the dollar bill as the United States Dollar (USD) on very short timeframes to squeeze out profits from small market fluctuations. This paper aims to help traders decide how to best act by creating a model that can predict price movement's direction for the next 5-min time frame. Several machine-learning models have been tested for this Up/Down binary-classification problem. In this paper, we provide a comparison of the state-of-art strategies in predicting the movement direction for bitcoin, including Random Guessing and a Momentum-Based Strategy. The tested models include Autoregressive Integrated Moving Average (ARIMA), Prophet (by Facebook), Random Forest, Random Forest Lagged-Auto-Regression, and Multi-Layer Perceptron (MLP) Neural Networks. The MLP deep neural network has achieved the highest accuracy of 54% compared to other time-series prediction models. Also, in this paper, various data transformation and feature engineering have been applied in the comparison.
Cryptocurrencies, which the Bitcoin is the most remarkable one, have allured substantial awareness up to now, and they have encountered enormous instability in their price. While some studies utilize conventional statistical and econometric ways to uncover the driving variables of Bitcoin's prices, experimentation on the advancement of predicting models to be used as decision support tools in investment techniques is rare. There are many different predicting cryptocurrencies' price methods that cover various purposes, such as forecasting a one-step approach that can be done through time series analysis, neural networks, and machine learning algorithms. Sometimes realizing the trend of a coin in a long run period is needed. In this paper, some machine learning algorithms are applied to find the best ones that can forecast Bitcoin price based on three other famous coins. Second, a new methodology is developed to predict Bitcoin's worth, this is also done by considering different cryptocurrencies prices (Ethereum, Zcash, and Litecoin). The results demonstrated that Zcash has the best performance in forecasting Bitcoin's price without any data on Bitcoin's fluctuations price among these three cryptocurrencies.
This paper aims to enrich the understanding and modelling strategies for cryptocurrency markets by investigating major cryptocurrencies’ returns determinants and forecast their returns. To handle model uncertainty when modelling cryptocurrencies, we conduct model selection for an autoregressive distributed lag (ARDL) model using several popular penalized least squares estimators to explain the cryptocurrencies’ returns. We further introduce a novel model averaging approach or the shrinkage Mallows model averaging (SMMA) estimator for forecasting. First, we find that the returns for most cryptocurrencies are sensitive to volatilities from major financial markets. The returns are also prone to the changes in gold prices and the Forex market’s current and lagged information. Then, when forecasting cryptocurrencies’ returns, we further find that an ARDL(p,q) model estimated by the SMMA estimator outperforms the competing estimators and models out-of-sample.
Abstract We study the fundamental differences that separate: Litecoin; Bitcoin Gold; Bitcoin Cash; Ethereum; and Zcash from Bitcoin, and draw some analysis to how these features are appreciated by the market, to ultimately make an inference as to how future successful cryptocurrencies may be invented and behave. We use Google Trend data, as well as price, volume and market capitalization data sourced from coinmarketcap.com to support this analysis. We find that Litecoin’s shorter block times offer benefits in commerce, but drawbacks in the mining process through orphaned blocks. Zcash holds a niche use for anonymous transactions, benefitting areas of the world lacking in economic freedom. Bitcoin Cash suffers from centralization in the mining process, while the greater decentralization of Bitcoin Gold has generally left it to stagnate. Ether’s greater functionality offers the greatest threat to Bitcoin’s dominance in the market. A coin that incorporates several of these features can be technically better than Bitcoin, but the first-to-market advantage of Bitcoin should keep its dominant position in the market.
This paper analyzes the connectedness among bitcoin, gold, and crude oil between 3 January 2017 and 31 December 2019. The paper’s motivation is based upon the idea that bitcoin can be similar to gold in terms of its hedging properties and can be used for hedging for different assets. Moreover, although it is more metaphorical, bitcoin is also accepted because it is mined like crude oil, namely, a commodity. These similarities can be investigated by analyzing the connectedness among these financial assets. The connectedness results derived from both total connectedness and frequency connectedness methods indicate that volatility connectedness is higher than the return connectedness among these assets. Furthermore, connectedness in volatilities is mostly driven by medium frequency, although connectedness in returns mostly exists in high frequency. Therefore, these results suggest that investors should consider these financial assets for their diversification decisions. The results suggest that although diversification among these three assets is more difficult in the short- and medium-term, investors may benefit from diversification in the long-run.
Abstract Through the application of the VAR-AGARCH model to intra-day data for three cryptocurrencies (Bitcoin, Ethereum, and Litecoin), this study examines the return and volatility spillover between these cryptocurrencies during the pre-COVID-19 period and the COVID-19 period. We also estimate the optimal weights, hedge ratios, and hedging effectiveness during both sample periods. We find that the return spillovers vary across the two periods for the Bitcoin-Ethereum, Bitcoin-Litecoin, and Ethereum-Litecoin pairs. However, the volatility transmissions are found to be different during the two sample periods for the Bitcoin-Ethereum and Bitcoin-Litecoin pairs. The constant conditional correlations between all pairs of cryptocurrencies are observed to be higher during the COVID-19 period compared to the pre-COVID-19 period. Based on optimal weights, investors are advised to decrease their investments (a) in Bitcoin for the portfolios of Bitcoin/Ethereum and Bitcoin/Litecoin and (b) in Ethereum for the portfolios of Ethereum/Litecoin during the COVID-19 period. All hedge ratios are found to be higher during the COVID-19 period, implying a higher hedging cost compared to the pre-COVID-19 period. Last, the hedging effectiveness is higher during the COVID-19 period compared to the pre-COVID-19 period. Overall, these findings provide useful information to portfolio managers and policymakers regarding portfolio diversification, hedging, forecasting, and risk management.
Tahmin teknikleri ve modelleri, doğru karar alma ve yatırım aşamasında kişiler ve kuruluşlar için son derece önemlidir. Tahminin doğruluğu başarılı kararlar alınmasını sağlar ve yatırımcıların fayda maksimizasyonuna ulaşmasına imkân tanır. Bu çalışmada, kripto para türlerinden en yaygın olarak kullanılan Bitcoin fiyatlarının yapay sinir ağları yöntemi ile tahmin edilmesi amaçlanmıştır. Girdi değişkenler olarak; Dow-Jones, S&P500, Nasdaq100, Eurostoxx Endeksleri, İsviçre Frangı, İngiliz Sterlini, Euro, Altın, Gümüş yatırım araçları alınmıştır. 2013-2018 tarihleri arasında günlük kapanış fiyatları verileri kullanılmıştır. Çalışmada geri beslemeli yapay sinir ağı modeli kullanılmıştır. 2019 Ocak ayı tahmini yapılarak model test edilmiştir ve modelin tahmin doğruluğu R2 değeri %99 başarı ile gerçekleşmiştir.
Raja Nabeel‐Ud‐Din Jalal, Massimo SARGIACOMO, Najam Us Sahar
The study investigates the role of commodity prices and tax purpose recognition on bitcoin prices. Since the introduction of bitcoin in 2008, emphasis has focused on economists, policy-makers and analysts drastically increasing bitcoin's accessibility and commodity values (Dumitrescu & Firică, 2014). This study employs GARCH and EGARCH from ARCH/GARCH family on daily nature data. We measure the volatile behavior of bitcoin by employing auto-regressive conditional heteroscedasticity model with the aim to explore the relationship between major commodities and bitcoin volatility. We focus on major commodities like gold, silver, platinum, and crude oil to be regressed with bitcoin. The daily prices of commodities were retrieved from www.investing.com and bitcoin prices from www.coindesk.com for the period from 29April 2013 to 16 October 2018. Results confirmed the currency's long-term volatile behavior, which is due to its composition and market dynamics, whereas the existence of asymmetric information effect is not confirmed. Tax recognition by other countries may in future help in controlling the volatility as bitcoin is not a country-specific security. But, only silver impacts on volatility in comparison to oil prices and platinum, which is due to its similar features with gold. Eventually, bitcoin can be used for risk diversification and money making.
Jeremy Eng‐Tuck Cheah, Di Luo, Zhuang Zhang, Ming‐Chien Sung
This paper comprehensively examines the performance of a host of popular variables to predict Bitcoin returns. We show that time-series momentum, economic policy uncertainty, and financial uncertainty outperform other predictors in all in-sample, out-of-sample, and asset allocation tests. Bitcoin returns have no exposure to common stock and bond market factors but rather are affected by Bitcoin-specific and external uncertainty factors.
Abstract From the past two years with increasing geopolitical and economic issues, global currency values have been falling and stock markets have been having a poor run & investors losing wealth. This has led to a renewal of interest in digital currencies. Cryptocurrency one of the most prominent digital currency has found itself in spotlight with investors wanting a piece of it and business establishments accepting it as a source of payment due to its stable performance in the last few years. This research has been done on predicting cryptocurrency prices using machine learning based neural network which has a lowest the model loss over 100 epochs during training and Technical Trade Indicators (TTI) graphs depicts a real BTC value 5 to 10 times in 300-days of current fiscal year has further supported this increasing trader confidence and a shift in global cryptocurrency graph by predicted BTC values. On the same lines, we are analyzing bitcoin prices using Machine Learning and Sentiment Analysis. We also study stock market trends in order to better predict bitcoin prices quantitively. In this work we analyze the impact of global currencies like US Dollar, foreign exchanges on Bitcoin prices and whether Bitcoin has the stability to dethrone global currencies and become the single medium of transaction. This work is adequate enough to aid in predicting price and with results obtained from predicting Bitcoin prices using machine learning based neural network achieving an accuracy of 94.89% under all circumstances of technical trade indication thereby bringing down its price prediction by over 13.7% in April 2020 itself during evaluation.
In this paper, we discuss the behavior of stock market returns in Tunisia during the COVID-19 outbreak. Using the OLS regression, we find that Bitcoin act as a hedge and Ethereum as a diversifier for Tunisia’s stock market before the COVID-19 outbreak; however, Bitcoin and Ethereum cannot generate benefits from portfolio diversification and hedging strategies for financial investors during the COVID-19. Moreover, Dash, Monero, and Ripple act as hedges before the COVID-19 outbreak and as diversifiers during this pandemic. Our results reveal that gold acts as a hedge and diversifier before the pandemic, but it's neither hedge nor a haven during the COVID-19 pandemic. Besides, the results indicated that the expected volatility of the US stock market has an impact on the Tunisian stock market. Finally, our results indicate that the growth rate of the COVID-19 confirmed cases and deaths harms Tunisia's stock market.
Hanna Danylchuk, Liubov Kibalnyk, Oksana Kovtun, Arnold Kiv · 6 authors
In this article, we present the results of simulation for cryptocurrency market based on fractal and entropy analysis using six cryptocurrencies in the first 20 of the capitalization rating. The application of the selected research methods is based on an analysis of existing methodologies and tools of economic and mathematical modeling of financial markets. It has been shown that individual methods are not relevant because they do not provide an adequate assessment of the given market, so an integrated approach is the most appropriate. Daily values of cryptocurrency pairs from August 2016 to August 2020 selected by the monitoring and modelling database. The application of fractal analysis led to the conclusion that the time series of selected cryptocurrencies were persistent. And the use of the window procedure for calculating the local Hurst coefficient allowed to detail and isolate the persistant and antipersistant gaps. Interdisciplinary methods, namely Tsallis entropy and wavelet entropy, are proposed to complement the results. The results of the research show that Tsallis entropy reveals special (crisis) conditions in the cryptocurrency market, despite the nature of the crises’ origin. Wavelet entropy is a warning indicator of crisis phenomena. It provides additional information on a small scale.
This paper examines the relationship of the leading financial assets, Bitcoin, Gold, and S&P 500 with GARCH-Dynamic Conditional Correlation (DCC), Nonlinear Asymmetric GARCH DCC (NA-DCC), Gaussian copula-based GARCH-DCC (GC-DCC), and Gaussian copula-based Nonlinear Asymmetric-DCC (GCNA-DCC). Under the high volatility financial situation such as the COVID-19 pandemic occurrence, there exist a computation difficulty to use the traditional DCC method to the selected cryptocurrencies. To solve this limitation, GC-DCC and GCNA-DCC are applied to investigate the time-varying relationship among Bitcoin, Gold, and S&P 500. In terms of log-likelihood, we show that GC-DCC and GCNA-DCC are better models than DCC and NA-DCC to show relationship of Bitcoin with Gold and S&P 500. We also consider the relationships among time-varying conditional correlation with Bitcoin volatility, and S&P 500 volatility by a Gaussian Copula Marginal Regression (GCMR) model. The empirical findings show that S&P 500 and Gold price are statistically significant to Bitcoin in terms of log-return and volatility.