Alexey Mikhaylov, Mir Sayed Shah Danish, Tomonobu Senjyu
Abstract In this study, it is examined how to improve the effectiveness of bitcoin market. In this framework, firstly, a wide literature review is made, and different factors that may be effective in this regard are identified. Afterward, an analysis is performed with Hurst method to determine which of these factors are more important. The study of cryptocurrency volatility is important in terms of financial instruments for hedging traditional assets, as well as in terms of pricing. The above results would be particularly useful in terms of portfolio management and risk management. This study discusses various financial issues related to cryptocurrencies, as well as some problems associated with the problems of their forecasting. To solve the problems of forecasting, the authors have not only studied, but also improved the forecasting method based on the machine learning algorithm in the MATLAB system using the tree model. Most of the analysis in the document relies heavily on studies by authors studying this issue. They can help make more informed decisions regarding financial investments and avoid the risks of using cryptocurrencies. The results may also assist regulators in creating a favorable legal environment for the functioning of the cryptocurrency market.
Ahmet Ĺensoy, Thiago Christiano Silva, Shaen Corbet, Benjamin Miranda Tabak
We examine the high-frequency return and volatility of major cryptocurrencies and reveal that spillovers among them exist. Our analysis shows that return and volatility clustering structures are distinct among different cryptocurrencies, suggesting that return and volatility might have different spillover patterns. Further investigation via minimal spanning trees points out that BTC, LTC and ETH are the most relevant cryptocurrencies in general, serving as connection hubs for linking many other cryptocurrencies. However, their role is challenged lately, potentially due to the increased usage of other cryptocurrencies in time.
Ahmet Faruk Aysan, Asad Ul Islam Khan, Humeyra Topuz, Ahmet Semih TunalÄą
This paper explores the applicability of universal cryptocurrency exchange by analyzing crypto exchanges of Binance, Latoken, Kucoin and Qash, which also have their own cryptocurrencies in the crypto market. Results of the recursive Johansen cointegration test proved that even though all of the cryptocurrencies have cointegration among each other, Binance positively disassociated itself from the others after it moved to Malta on 23 March 2018. Based on the daily prices of cryptocurrencies over the period from 6 November 2017 to 10 November 2019, taken from coinmarketcap, we conclude that Binance can be considered as a survival of the fittest among all of the crypto exchanges in this natural experiment.
This study aims that Bitcoin prices are considered as dependent variables, and the total number of Coronavirus cases in the world, Ethereum Prices, Gold Prices, Coronavirus Google Trend Index, and Crypto Money Google Trend Index are considered as independent variables. Using the ARDL model, it was analyzed with a daily data set between 21.01.2020 - 04.04.2020. It is concluded that the relationship between the variables included in the analysis and Bitcoin prices exists co-integrated in the long term. Within the framework of the findings, investors' fears were interpreted by associating them with Bitcoin and Covid-19.
We analyze the predictability of the bitcoin market across prediction horizons ranging from 1 to 60 min. In doing so, we test various machine learning models and find that, while all models outperform a random classifier, recurrent neural networks and gradient boosting classifiers are especially well-suited for the examined prediction tasks. We use a comprehensive feature set, including technical, blockchain-based, sentiment-/interest-based, and asset-based features. Our results show that technical features remain most relevant for most methods, followed by selected blockchain-based and sentiment-/interest-based features. Additionally, we find that predictability increases for longer prediction horizons. Although a quantile-based long-short trading strategy generates monthly returns of up to 39% before transaction costs, it leads to negative returns after taking transaction costs into account due to the particularly short holding periods.
Purpose With the unprecedented growth of digitalization across the globe, a new asset class, that is cryptocurrency, has emerged to attract investors of all stripe. The novelty of this newly emerged asset class has led researchers to gauge anomalous trade patterns and behavioural fallacies in the crypto market. Therefore, the present study aims to examine the herd behaviour in a newly evolved cryptocurrency market during normal, skewed, Bitcoin bubble and COVID-19 phases. It, then, investigates the significance of Bitcoin in driving herding bias in the market. Finally, the study gauges herding contagion between the crypto market and stock markets. Design/methodology/approach The study employs daily closing prices of cryptocurrencies and relevant stocks of S&P 500 (USA), S&P BSE Sensex (Index) and MERVAL (Argentina) indices for a period spanning from June 2015 to May 2020. Quantile regression specifications of Chang et al.â s (2000) absolute deviation method have been used to locate herding bias. Dummy regression models have also been deployed to examine herd activity during skewed, crises and COVID-19 phases. Findings The descriptive statistics reveal that the relevant distributions are leptokurtic, justifying the selection of quantile regression to diagnose tails for herding bias. The empirical results provide robust evidence of crypto herd activity during normal, bullish and high volatility periods. Next, the authors find that the assumptions of traditional financial doctrines hold during the Bitcoin bubble. Further, the study reveals that the recent outbreak of COVID-19 subjects the crypto market to herding activity at quantile ( t ) = 0.60. Finally, no contagion is observed between cryptocurrency and stock market herding. Practical implications Drawing on the empirical findings, it is believed that in this age of digitalization and technological escalation, this new asset class can offer diversification benefits to the investors. Also, the crypto market seems quite immune to behavioural idiosyncrasies during turbulence. This may relieve regulators of the possible instability this market may pose to the entire financial system. Originality/value The present study appears to be the first attempt to diagnose leptokurtic tails of relevant distribution for crypto herding in the wake of two remarkable events: the crypto asset bubble (2016â2017) and the outbreak of coronavirus (early 2020).
Michael Demmler, Amilcar Orlian FernĂĄndez DomĂnguez
This paper examines historical Bitcoin price data together with the price data of a well-known and generally accepted historical asset price bubble (the 1720 South Sea Bubble) with the aim of identifying possible similarities. In order to find empirical evidence of speculative bubble tendencies, the article analyses distribution moments and autoregressive models of time series of both assets. Results show that historical daily prices of both assetsâtaking into account one year before and one year after the maximum price levelâclearly show the two phases of bubble expansion and subsequent crash. Furthermore, various similarities between the South Sea Bubble and Bitcoin can be found in descriptive statistics, such as mean of return, standard deviation, and skewness. Statistical tests also show several explosive moments in the time series of the South Sea Company and Bitcoin returns, which implies that both assets exhibit more than one financial bubble.
Ahmet Faruk Aysan, Asad Ul Islam Khan, Humeyra Topuz
The main aim of this article is to examine the inter-relationships among the top cryptocurrencies on the crypto stock market in the presence and absence of the COVID-19 pandemic. The nine chosen cryptocurrencies are Bitcoin, Ethereum, Ripple, Litecoin, Eos, BitcoinCash, Binance, Stellar, and Tron and their daily closing price data are captured from coinmarketcap over the period from 13 September 2017 to 21 September 2020. All of the cryptocurrencies are integrated of order 1 i.e., I(1). There is strong evidence of a long-run relationship between Bitcoin and altcoins irrespective of whether it is pre-pandemic or pandemic period. It has also been found that these cryptocurrenciesâ prices and their inter-relationship are resilient to the pandemic. It is recommended that when the investors create investment plans and strategies they may highly consider Bitcoin and altcoins jointly as they give sustainability and resilience in the long run against the geopolitical risks and even in the tough time of the COVID-19 pandemic.
Purpose It is crucial to find a better portfolio optimization strategy, considering the cryptocurrencies' asymmetric volatilities. Hence, this research aimed to present dynamic optimization on minimum variance (MVP), equal risk contribution (ERC) and most diversified portfolio (MDP). Design/methodology/approach This study applied dynamic covariances from multivariate GARCH(1,1) with Studentâs- t -distribution. This research also constructed static optimization from the conventional MVP, ERC and MDP as comparison. Moreover, the optimization involved transaction cost and out-of-sample analysis from the rolling windows method. The sample consisted of ten significant cryptocurrencies. Findings Dynamic optimization enhanced risk-adjusted return. Moreover, dynamic MDP and ERC could win the naĂŻve strategy (1/N) under various estimation windows, and forecast lengths when the transaction cost ranging from 10 bps to 50 bps. The researcher also used another researcher's sample as a robustness test. Findings showed that dynamic optimization (MDP and ERC) outperformed the benchmark. Practical implications Sophisticated investors may use the dynamic ERC and MDP to optimize cryptocurrencies portfolio. Originality/value To the best of the authorâs knowledge, this is the first paper that studies the dynamic optimization on MVP, ERC and MDP using DCC and ADCC-GARCH with multivariate- t- distribution and rolling windows method.
Recently, the deep learning architecture has been used with an increasing rate for forecasting in financial markets. In this paper, the LSTM model is used to forecast the daily closing price direction of the BTC/USD. Both model accuracy and the profit or loss of the trades made based on the proposed model are analyzed. In addition, the effects of the MACD indicator and the input matrix dimension on forecasting accuracy are evaluated. The potential risks and actual risks encountered by the trader who trades based on the proposed model were also analyzed. The obtained results indicate that the optimization of the LSTM parameters using the Bayesian optimization model has enhanced the modelâs accuracy. The results obtained from analyzing the drawdown and reward/risk resulting from the trades made based on the model show that the model enables the trader to trade with peace of mind due to the low level of actual risks and potential risks.
Abstract This study examines the portfolio diversification benefits of alternative currency trading in Bitcoin and foreign exchange markets. The following methods are applied for the analysis: the spillover index method of Diebold and Yilmaz (Int J Forecast 28(1): 57â66, 2012. 10.1016/j.ijforecast.2011.02.006 ), the spillover asymmetry measures of Barunik et al. (J Int Money Finance 77: 39â56, 2017. 10.1016/j.jimonfin.2017.06.003 ), and the frequency connectedness method of Barunik and KĹehlĂk (J Financ Econom 16(2): 271â296, 2018. 10.1093/jjfinec/nby001 ). The findings identify the presence of low-level integration and asymmetric volatility spillover as well as a dominant role of short horizon spillover among Bitcoin markets and foreign exchange pairs for six major trading currencies (US dollar, euro, Japanese yen, British pound sterling, Australian dollar, and Canadian dollar). Bitcoin is found to provide significant portfolio diversification benefits for alternative currency foreign exchange portfolios. Alternative currency Bitcoin trading in euro is found to provide the most significant portfolio diversification benefits for foreign exchange portfolios consisting of major trading currencies. The findings of the study regarding spillover dynamics and portfolio diversification capabilities of the Bitcoin market for foreign exchange markets of major trading currencies have significant implications for portfolio diversification and risk minimization.
Tiejun Chen, Chi Keung Marco Lau, Sadaf Cheema, Chun Kwong Koo
This paper analyses the effects of the Chinese Economic Policy Uncertainty (CEPU) index on the daily returns of Bitcoin for the period from December 31, 2019 to May 20, 2020. Utilizing the Ordinary Least Squares (OLS) and the Generalized Quantile Regression (GQR) estimation techniques, the paper illustrates that the current CEPU has a positive impact on the returns of Bitcoin. However, the positive impact is statistically significant only at the higher quantiles of the current CEPU. It is concluded that Bitcoin can be used in hedging against policy uncertainties in China since significant rises in uncertainty leads to a higher return in Bitcoin. JEL Codes: G32; G15; C22
We investigate the financial assets status (diversifier, hedge and safe haven) of Bitcoin and gold against the world and U.S. stock markets. We employ the copula-DCC approach to consider the tail dependence between Bitcoin or gold return and stock return. Our results indicate that Bitcoin is a weak hedge against the world and U.S. stock markets, while gold is a diversifier against the world stock market, but a strong hedge against the U.S. stock market. We also estimate the dynamic conditional betas and find that the returns of Bitcoin and gold are not sensitive to changes in the value of market portfolio. Moreover, we employ the threshold model to investigate whether there exist contagion effects between Bitcoin or gold market and stock markets. Our results show that the increase in market uncertainty weakens the role of Bitcoin as a weak hedge and Bitcoin becomes a diversifier, while it changes the role of gold as a diversifier into a hedge or a safe haven. The above results mean that although Bitcoin is called as ââŹĹnew goldâ⏠, the financial assets status of Bitcoin and gold are different.
This work shows the results obtained from a comparison between a restricted and a unrestricted Bitcoin price classification, verifying whether the addition of technical indicators to the classic macroeconomic variables leads to an effective improvement in the prediction of Bitcoin price changes. The goal was achieved implementing different machine learning algorithms, such as Support Vector Machine (SVM), XGBoost (XGB), a Convolutional Neural Network (CNN) and a Long Short Term Memory (LSTM) neural network. Macroeconomic variables data were gained from Yahoo Finance website spanning a 4-year interval with a hourly resolution, while technical indicators data are provided by the python talib library. The variance problem on test samples has been taken into account through the cross validation technique which also allowed to evaluate a more reliable estimate of the model's performance. Furthermore, the Grid Search technique was used to find the best hyperparameters values for each implemented algorithm. The results were evaluated in terms of the well known classification metrics, i.e. accuracy, precision, recall and f1 score. Based on the results, it was possible to demonstrate that the unrestricted case outperforms the restricted one, verifying that the addition of the technical indicators to the macroeconomic variables actually improves the accuracy on Bitcoin price classification.
We investigate whether stablecoins are safe havens for traditional cryptocurrencies with fresh evidence from the recent crisis period of the COVID-19 pandemic. Our results support the safe-haven properties of Tether for both before and during the pandemic. For Digix, a gold-backed stablecoin with relatively small market capitalization, we find a change in characteristics before and during the pandemic, but do not find statistically significant evidence for its safe-haven properties. Furthermore, we document that, when considering the economic benefits and costs of adding safe-haven assets into cryptocurrency portfolios, the one with Tether outperforms both a naked portfolio and the portfolio with a traditional safe-haven asset such as gold.