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
The Theta method has attracted academic attention lately due to its simplicity and superior performance. This paper proposes a new hybrid forecasting approach based on combining the Theta decomposition method and support vector regression (SVR) for forecasting highly volatile and noisy Bitcoin price time series. Using Theta decomposition with coefficients ranging from 0 to 2 with 0.1 steps, we extracted 20 Theta lines from the original time series. Each of these 20 lines is used for a univariate regression. Then the results of each forecasts aggregated to construct the final predicted values. Moreover, we used the Theta lines to construct a predictor space for multivariate regression using SVR. However, due to poor performance of the multivariate regression and to further enhance its performance, we eliminated inefficient Theta lines from the predictor space. Enhanced MASE by 10.45% and 5.68% in comparison to the Theta-SES (classic Theta) and SVR, the results indicate the superiority of the proposed hybrid Theta-SVR.
Purpose Considering the different motivation for the creation of each of these cryptocurrencies, the purpose of this paper is to examine whether there is a dominant external factor in the cryptocurrency world. Using a novel two-step time and frequency independent methodology, the authors examine a large scope of cryptocurrencies and external factors within the same period, and analytical framework. Design/methodology/approach The examined cryptocurrencies are Bitcoin, Ethereum, Ripple, Litecoin, Monero and Dash. In total, 18 external factors from 5 factor families are selected based on the mining motivation of these cryptocurrencies. The study first examines discrete wavelet transform-based (WTB) correlations, reduce the dimension and focuson relevant pairs. Selected pairs are further examined by wavelet coherence to capture the intermittent nature of the relationships allowing the most needed “Flexibility of frequency and time domains”. Findings Each coin appears to operate as a unique character with the exception of Bitcoin and Litecoin. There is no prominent external driver. The cryptocurrency market is not a clear substitute for a specific factor or market. Two-step WTB filtered wavelet coherence analysis help us to analyze a large number of factor without the loss of focus. The co-movements within the cryptocurrencies spillover from Ethereum to altcoins and later to Bitcoin. Originality/value The study presents one of the first examples of two-step WTB filtered wavelet coherence analysis. The methodology suggests an approach for simultaneous examination of large number of variables. The scope of the study provides a rather holistic view of the co-movements of external factors and major cryptocurrencies.
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.
The long memory is usually defined via auto-covariances which further connect with Hurst exponent. The heavy tails in Bitcoin returns can cause infinite auto-covariances which make the analysis of long memory and market efficiency in Bitcoin based on estimation of Hurst exponent inappropriate. Few literatures focus on this problem. We provide two approaches based on shuffling method and rank-ordered technique to this problem, and further combine them to analyse the time-varying efficiency and long memory in Bitcoin using sliding window. Results show that the inefficiency and long memory exist in Bitcoin before 2014 and after mid-2017. Especially, the latest data reveal a recent new change that the Bitcoin market has become inefficient and exhibited long memory behaviour since mid-2017, but is turning back to efficiency recently. This change may be due to the frequent key events of Bitcoin in 2017 and 2018, which can break the weak efficiency of Bitcoin. The heavy negative tails with α<2 before September 2016 validate the necessity of our analysis under heavy tails. Besides, the change trend and exact sub-periods of efficiency and long memory are first obtained via empirical mode decomposition of Hurst exponent estimates.
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.
Khánh Hoàng, 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. 2013–Jun. 2018, using time- and frequency-domain frameworks. The time-domain framework’s 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 Bitcoin’s 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 author’s 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 ‘’On Digital Financial Assets’’ 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.
Fredrik Aurbakken Enoksen, Ch.J. Landsnes, Katarína Lučivjanská, Péter Molnár
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.
Abstract The objective of this work is to understand the dynamics of cryptocurrency prices. Specifically, how prices switch between different regimes, going from “bull” to “stable” and “bear” times. For this purpose, we propose a hidden Markov model that aims at explaining the evolution of Bitcoin prices through different, unobserved states. The implementation of the proposed model includes a likelihood ratio test that allows to compare models with different states and with different covariance structures. Our empirical findings show that the time movements of Bitcoin prices across different exchange markets are well‐described by the proposed model. In particular, a parsimonious model with a diagonal covariance matrix leads to better predictions, compared with a model with a full covariance matrix.