The cryptocurrency market has been developing dynamically in recent years. The rapid development of the market is the result of increased interest in cryptocurrencies both from the entities treating it as a means of payment and from investors acquiring cryptocurrency for speculative purposes. Blockchain technology, on the basis of which cryptocurrencies are created, has gained acceptance in the financial industry and many entities are conducting advanced work on its use in their operations. On the other hand, numerous supervisors, including the European Banking Authority, the European Central Bank, the National Bank of Poland, and the Polish Financial Supervision Authority warn against investing in cryptocurrencies, indicating the numerous risks associated with such investments. The aim of the article is to analyze the potential risks and benefits of investing in cryptocurrencies. The main risks related to investments in cryptocurrency were analyzed on the example of bitcoin, and the rate of return and correlations with changes in the currency prices of other financial instruments were analyzed.
Using the coronavirus COVID-19 outbreak as a set-up for a quasi-experiment, this study derives novel insights on the dynamic correlation between Bitcoin and US stocks. Given the unprecedented scale of infections and the nature of the virus, the potential impact on the dynamic correlation was unpredictable and therefore uncertain. Using a difference-in-differences setting, the dynamic correlation between Bitcoin and stocks is controlled for the dynamic correlation between gold and stocks. This study finds that Bitcoin performed poorly in hedging this tail risk.
Ama -Son on ylda finans alannda dijital inovasyonlar zellikle Blockchain teknolojisine bal olarak ortaya kmaktadr. Blockchain teknolojisinin tm dnyada en yaygn olarak kullanld rn ise kripto para birimleridir. Kripto para birimleri ierisinde Bitcoin gerek piyasa kapitalizasyonu gerekse ilem hacmi ile dikkat ekmektedir
The analysis of cryptocurrencies market behaviour is receiving significant attention from researchers and practitioners in the last decades. This paper aims at contributes to volatility estimations of the cryptocurrencies helping to highlight the main stylized facts and characteristics. The performance of different specifications of volatility modelling, within the GARCH class, have been compared through the Model Confidence Set (MCS) over four of the most capitalised cryptocurrencies, namely Bitcoin, Ethereum, Stellar and Ripple. Our empirical findings give evidence of strong asymmetric effects in cryptocurrencies volatility leading to a better performance of asymmetric GARCH specifications..
Bitcoin is the first digital decentralized cryptocurrency that has shown a\nsignificant increase in market capitalization in recent years. The objective of\nthis paper is to determine the predictable price direction of Bitcoin in USD by\nmachine learning techniques and sentiment analysis. Twitter and Reddit have\nattracted a great deal of attention from researchers to study public sentiment.\nWe have applied sentiment analysis and supervised machine learning principles\nto the extracted tweets from Twitter and Reddit posts, and we analyze the\ncorrelation between bitcoin price movements and sentiments in tweets. We\nexplored several algorithms of machine learning using supervised learning to\ndevelop a prediction model and provide informative analysis of future market\nprices. Due to the difficulty of evaluating the exact nature of a Time\nSeries(ARIMA) model, it is often very difficult to produce appropriate\nforecasts. Then we continue to implement Recurrent Neural Networks (RNN) with\nlong short-term memory cells (LSTM). Thus, we analyzed the time series model\nprediction of bitcoin prices with greater efficiency using long short-term\nmemory (LSTM) techniques and compared the predictability of bitcoin price and\nsentiment analysis of bitcoin tweets to the standard method (ARIMA). The RMSE\n(Root-mean-square error) of LSTM are 198.448 (single feature) and 197.515\n(multi-feature) whereas the ARIMA model RMSE is 209.263 which shows that LSTM\nwith multi feature shows the more accurate result.\n
In this paper, the pricing performance of the generalised autoregressive conditional heteroskedasticity (GARCH) option pricing model is tested when applied to Bitcoin (BTCUSD). In addition, implied volatility indices (30, 60-and 90-days) of BTCUSD and the Cyptocurrency Index (CRIX) are generated by making use of the symmetric GARCH option pricing model. The results indicate that the GARCH option pricing model produces accurate European option prices when compared to market prices and that the BTCUSD and CRIX implied volatility indices are similar when compared, this is consistent with expectations because BTCUSD is highly weighted when calculating the CRIX. Furthermore, the term structure of volatility indices indicate that short-term volatility (30 days) is generally lower when compared to longer maturities. Furthermore, short-term volatility tends to increase to higher levels when compared to 60 and 90 day volatility when large jumps occur in the underlying asset.
Toan Luu Duc Huynh, Muhammad Shahbaz, Muhammad Ali Nasir, Subhan Ullah
Abstract This paper empirically investigates whether cryptocurrencies might have a useful role in financial modelling and risk management in the energy markets. To do so, the causal relationship between movements on the energy markets (specifically the price of crude oil) and the value of cryptocurrencies is analysed by drawing on daily data from April 2013 to April 2019. We find that shocks to the US and European crude oil indices are strongly connected to the movements of most cryptocurrencies. Applying a non-parametric statistic, Transferring Entropy (an econophysics technique measuring information flow), we find that some cryptocurrencies (XEM, DOGE, VTC, XLM, USDT, XRP) can be used for hedging and portfolio diversification. Furthermore, the results reveal that the European crude oil index is a source of shocks on the cryptocurrency market while the US oil index appears to be a receiver of shocks.
KhĂĄnh HoĂ ng, Cuong Nguyen, Kongchheng Poch, Thang Xuan Nguyen
This paper examines the connectedness between Bitcoin and commodity volatilities, including oil, wheat, and corn, during the period Oct. 2013âJun. 2018, using time- and frequency-domain frameworks. The time-domain frameworkâs results show that the connectedness is 23.49%, indicating a low level of connection between Bitcoin and the commodity volatilities. Bitcoin contributes only 2.55% to the connectedness, while the wheat volatility index accounts for 12.51% of the total connectedness. The frequency connectedness shows that Bitcoinâs contribution to the total connectedness increases from high-frequency to low-frequency bands, and the total connectedness reaches up to 22.47%. It also indicates that Bitcoin is the spillover transmitter to the wheat volatility, while being the spillover receiver from the oil and corn volatilities. The findings suggest that Bitcoin could be a hedger for commodity volatilities.
Bitcoin volatility was investigated with various symmetric and asymmetric models in the study. In addition, value at risk (VaR) was calculated by using the Kupiec LR test and the error prediction performances of the models were compared. As a result of the work, the long memory of volatility in Bitcoin returns was found. It means the cryptocurrency market is not efficient. According to the FIAPARCH asymmetric model, it was determined that positive information shocks reaching the Bitcoin market increased volatility more than negative information shocks. Comparing the error prediction performance of the models by calculating VaR, the HYGARCH model prediction results were found to be superior to other models included in the study. Thus, it was determined that the most suitable model in predicting the volatility, namely the risk of Bitcoin in short and long positions for those who consider investing in Bitcoin, is the asymmetric model HYGARCH.
The aim of this work is to study the pricing in the cryptocurrency market and applying cryptocurrencies by the Bank of Russia for its monetary policy. The research objectives are to identify the cyclical nature of price dynamics, to study market maturity and potential risks that have a long-term positive relationship with the financial stability of the cryptocurrency market. The author uses the Hurst method with the Amihud illiquidity measure to study the resistance of four cryptocurrencies (Bitcoin, Litecoin, Ripple and Dash) and their evolution over the past five years. The study results in the authorâs conclusion that the cryptocurrency market has entered a new stage of development, which means a reduced possibility to have excess profits when investing in the most liquid cryptocurrencies in the future. However, buying new high-risk tools provides opportunities for speculative income. The author concludes that illiquid cryptocurrencies exhibit strong inverse anti-persistence in the form of a low Hurst exponent. A trend investing strategy may help obtain abnormal profits in the cryptocurrency market. The Bank of Russia could partially apply digital currency to implement monetary policy, which would soften the business cycle and control the inflation. If Russia accepts the law ââOn Digital Financial Assetsââ and legalizes cryptocurrencies after the economic crisis caused by the COVID-19 pandemic, the Bank of Russia might act as a lender of last resort and offer crypto loans.
As cryptocurrencies emerged only recently, they are subject to only very limited financial regulations. In this paper we study which variables can predict bubbles in the prices of eight major cryptocurrencies, focusing on uncertainty measures as predictors. We detect multiple bubble periods for all eight cryptocurrencies, particularly in 2017 and early 2018. We find that higher volatility, trading volume and transactions are positively associated with the presence of bubbles across cryptocurrencies. Regarding the uncertainty variables, the VIX-index consistently demonstrates negative relationships with bubble occurrence, while the EPU-index mostly exhibits positive associations with bubbles. These results may assist authorities in designing appropriate regulations.
This paper examines the behaviour of Bitcoin returns and those of several other cryptocurrencies in the pre and post period of the introduction of the Bitcoin futures market. We use the principal component-guided sparse regression (PC-LASSO) model to analyze several sample sizes for the pre and post periods. Besides the neighbourhood of the break time, the current period is also investigated as returns start to recover after some time. Search intensity is observed to be the most important variable for Bitcoin for all periods, whereas for the other cryptocurrencies there are other variables that seem more important in the pre period, while search intensity still stands out in the post period. Furthermore, GARCH analyses suggest that search intensity increases the volatility of Bitcoin returns more in the post period than it does in the pre period. Our empirical findings suggest that the top five cryptocurrencies are substitutes before the launch of Bitcoin futures. However, this effect is lost, and moreover, there are spillover effects on altcoins during both the post and the recovery period. We find a spillover effect of the introduction of bitcoin futures on altcoins and this effect seems to persist during the recovery period.
Abstract Crypto currencies have sparked great interest lately not only among regular people, billionaires and Wall Street, but it also caught the attention of national and global financial regulators across the world. In this article, we try to answer the following questions: what is bitcoin? It is money, a mean of payment, a huge bubble or just a way to evade taxes, launder money and fund illegal trade? We will answer these questions by testing whether bitcoin is a bubble with the help of right-tailed ADF tests and analyzing if the price of bitcoin has experienced shocks. We identify bitcoin price shock when the price of bitcoin is above its Hodrick-Prescott trend plus one standard deviation. Also, we will analyze if bitcoin fulfils the roles of money and if itself or a stablecoin like Libra can attain an important place within the international monetary system. We will also research the potential risks associated with the adoption of Libra, especially in poorer countries. Despite Bitcoin and Libraâs weaknesses, an advantage is that they insist on the necessity of faster and cheaper cross-border funds transfers 24/7, 365 days a year.
Twitter is becoming an increasingly popular platform used by financial analysts to monitor and forecast financial markets. In this paper we investigate the impact of the sentiments expressed in Twitter on the subsequent market movement, specifically the bitcoin exchange rate. This study is divided into two phases, the first phase is sentiment analysis, and the second phase is correlation and regression. We analyzed tweets associated with the Bitcoin in order to determine if the userâs sentiment contained within those tweets reflects the exchange rate of the currency. The sentiment of users over a 2-month period is classified as having a positive or negative sentiment of the digital currency using the proposed CNN-LSTM deep learning model. By applying Pearson's correlation, we found that the sentiment of the day (d) had a positive effect on the future Bitcoin returns on the next day (d+1). The prediction accuracy of the linear regression model for the next day's revenue was 78%.
Pınar Kaya Soylu, Mustafa Okur, ĂzgĂŒr ĂatıkkaĆ, Ayca Altintig
This paper examines the volatility of cryptocurrencies, with particular attention to their potential long memory properties. Using daily data for the three major cryptocurrencies, namely Ripple, Ethereum, and Bitcoin, we test for the long memory property using, Rescaled Range Statistics (R/S), Gaussian Semi Parametric (GSP) and the Geweke and Porter-Hudak (GPH) Model Method. Our findings show that squared returns of three cryptocurrencies have a significant long memory, supporting the use of fractional Generalized Auto Regressive Conditional Heteroscedasticity (GARCH) extensions as suitable modelling technique. Our findings indicate that the Hyperbolic GARCH (HYGARCH) model appears to be the best fitted model for Bitcoin. On the other hand, the Fractional Integrated GARCH (FIGARCH) model with skewed student distribution produces better estimations for Ethereum. Finally, FIGARCH model with student distribution appears to give a good fit for Ripple return. Based on Kupieckâs tests for Value at Risk (VaR) back-testing and expected shortfalls we can conclude that our models perform correctly in most of the cases for both the negative and positive returns.
In contrast with robust systems that resist noise or fragile systems that break with noise, antifragility is defined as a property of complex systems that benefit from noise or disorder. Here we define and test a simple measure of antifragility for complex dynamical systems. In this work we use our antifragility measure to analyze real data from return prices in the stock and cryptocurrency markets. Our definition of antifragility is the product of the return price and a perturbation. We explore different types of perturbations that typically arise from within the system. Our results suggest that for both the stock market and the cryptocurrency market, the tendency among the 'top performers' is to be robust rather than antifragile. It would be important to explore other possible definitions of antifragility to understand its role in financial markets and in complex dynamical systems in general.
Multifractal processes reproduce some of the stylised features observed in financial time series, namely heavy tails found in asset returns distributions, and long-memory found in volatility. Multifractal scaling cannot be assumed, it should be established; however, this is not a straightforward task, particularly in the presence of heavy tails. We develop an empirical hypothesis test to identify whether a time series is likely to exhibit multifractal scaling in the presence of heavy tails. The test is constructed by comparing estimated scaling functions of financial time series to simulated scaling functions of both an iid Student t-distributed process and a Brownian Motion in Multifractal Time (BMMT), a multifractal processes constructed in Mandelbrot et al. (1997). Concavity measures of the respective scaling functions are estimated, and it is observed that the concavity measures form different distributions which allow us to construct a hypothesis test. We apply this method to test for multifractal scaling across several financial time series including Bitcoin. We observe that multifractal scaling cannot be ruled out for Bitcoin or the Nasdaq Composite Index, both technology driven assets.
MarĂa de la O GonzĂĄlez, Francisco Jareño, Frank S. Skinner
This article examines the connectedness between Bitcoin returns and returns of ten additional cryptocurrencies for several frequenciesâdaily, weekly, and monthlyâover the period January 2015âMarch 2020 using a nonlinear autoregressive distributed lag (NARDL) approach. We find important and positive interdependencies among cryptocurrencies and significant long-run relationships among most of them. In addition, non-Bitcoin cryptocurrency returns seem to react in the same way to positive and negative changes in Bitcoin returns, obtaining strong evidence of asymmetry in the short run. Finally, our results show high persistence in the impact of both positive and negative changes in Bitcoin returns on most of the other cryptocurrency returns. Thus, our model explains about 50% of the other cryptocurrency returns with changes in Bitcoin returns.