This paper examines price discovery between bitcoin spot and futures using static measures, namely information share (IS), component share (CS), modified information share (MIS), information leadership share (ILS), impulse response, and a time-varying parameter vector autoregressive (TVP-VAR) model with stochastic volatility and Markov Chain Monte Carlo (MCMC) sampling algorithm. Our one-minute and daily datasets cover 16 months before and 16 months during the Covid-19 pandemic (November 2018 to June 2021). Our IS, CS, MIS and impulse response results indicate a stronger bitcoin spot leadership, whereas our ILS results point to a weaker bitcoin futures dominance, during the Covid-19 pandemic. We construe this, as far as microstructure noise is concerned, as meaning that the bitcoin price is discovered in the spot market, and its dominance appears to have strengthened during the pandemic. However, as far as âpure speedâ is concerned, price discovery takes place in the bitcoin futures market, and its leadership seems to have weakened during the Covid-19 pandemic. The results of the time-varying measure (TVP-VAR) imply that, before the pandemic, price discovery took place within bitcoin futures but, during the pandemic, price discovery leadership has changed course, to occur within bitcoin spot.
We examine whether the occurrence of jumps in the return of major cryptocurrencies increases the likelihood of jumps in the stock returns of blockchain and crypto-exposed US companies. We use two criteria to identify the US stocks with blockchain and cryptocurrency exposure; i) text search and ii) membership in the blockchain indices. We first detect that both asset classes are subject to jump behaviour. Then, we employ logistic regressions and show that the occurrence of jumps in some cryptocurrencies increases the probability of jumps in several blockchain and crypto-exposed companies. The co-jumping behaviour is not affected by the COVID-19 outbreak.
Utilizing a large set of variables that include transaction information, public attention, blockchain information, macroeconomic variables and technical indicators, we compare different deep learning models with baseline methods, such as statistical and machine learning models, on Bitcoin volatility forecast. We find that feature selection approach strongly affects model performance. The results show that a simple Long Short-Term Memory (LSTM) model outperforms other models when using individual feature selection method.
Bitcoin is one of the most famous cryptocurrencies in the world and its price has changed frequently in recent years. Different from the traditional stock market, the changes of bitcoin's price are even more dramatic. Huge price movements have attracted not only investors but also more researchers to find different methods to predict bitcoin's price. In this research, we consider using change rate over previous a few days to predict the significant bitcoin price changes, which are defined as a three-category problem. Besides, we compare the predictive results using two deep learning models - LSTM and GRU, and then find LSTM perform better than GRU with the highest AUC value of 0.701 in most cases.
Cryptocurrencies have taken over the whole financial world with their high risk and high reward quality. Cryptocurrency markets are one of the most complex and volatile markets in the world. In this paper, we make an attempt to predict financial time series for popular cryptocurrencies like Ethereum, Binance coin and Bitcoin, using deep learning and multifractal detrended fluctuation analysis techniques. After testing for non-linearity in the financial time series of popular cryptocurrencies, it was revealed that cryptocurrency time series manifest latent relations, short term, and long term memory. The method uses Transformers and Long-short term Neural networks(LSTM) to forecast the prices of various cryptocurrencies. Although using LSTM along with transformers leads to longer computational times, but the predictive accuracy is better as compared to traditional regression neural networks and kNN forecasting models. Experimentation of the proposed model reveals that the deep learning model is profoundly efficient in predicting the intrinsic dynamics of cryptocurrency financial time series.
This article aims to test a causal nexus between bitcoin market and economic policy uncertainty. We use the continuous wavelet analysis to investigate lead-lag relationship between bitcoin market and economic policy uncertainty in different time-frequency domains. Our findings show the negative relationship between bitcoin returns and economic policy uncertainty around the period of bitcoinâs currency recognition and COVIC-19 pandemic crisis both daily and monthly time series test. Furthermore, we find that the causality relationship between bitcoin and economic policy uncertainty is relatively indistinct around the period of bitcoinâs currency recognition, while bitcoin returns are leading economic policy uncertainty changes during COVID-19 pandemic crisis, indicating the economic policy uncertainty fluctuation trend can refer to the fluctuation of bitcoin, bitcoin can be viewed as a leading indicator, but it could not be employed as a safe-haven asset hedge against uncertainty during the period of COVID-19 pandemic.
This paper attempts to understand the dynamic interrelationships and financial asset capabilities of Bitcoin by analysing several aspects of its volatility vis-a-vis other asset classes. This study aims to analyse the volatility dynamics of the returns of Bitcoin. An asymmetric GARCH model (EGARCH) is used to investigate whether Bitcoin may be useful in risk management and ideal for risk-averse investors in anticipation of negative shocks to the market (leverage effect). This paper also examines Bitcoin as an investment and hedge alternative to gold as well as NSE NIFTY using a multivariate DCC GARCH model. DCC GARCH models are also used to check whether correlation (co-movement) between the markets is time-varying, examine returns and volatility spillovers between markets and the effect of the outbreak of COVID-19 in India on the investigated markets. The results show that given the supply of Bitcoin is fixed, low returns realisation is equivalent to excess supply over demand wherein investors are selling off Bitcoin during bad times. The positive co-movement between Bitcoin and gold during the COVID-19 outbreak shows that investors perceived Bitcoin as a relatively safe investment. However, overall analysis shows that Bitcoin was not considered a safe hedge and an investment option by Indian investors during the study period.
Marcin WÄ torek, JarosĆaw KwapieĆ, StanisĆaw DroĆŒdĆŒ
Unlike price fluctuations, the temporal structure of cryptocurrency trading has seldom been a subject of systematic study. In order to fill this gap, we analyse detrended correlations of the price returns, the average number of trades in time unit, and the traded volume based on high-frequency data representing two major cryptocurrencies: bitcoin and ether. We apply the multifractal detrended cross-correlation analysis, which is considered the most reliable method for identifying nonlinear correlations in time series. We find that all the quantities considered in our study show an unambiguous multifractal structure from both the univariate (auto-correlation) and bivariate (cross-correlation) perspectives. We looked at the bitcoin--ether cross-correlations in simultaneously recorded signals, as well as in time-lagged signals, in which a time series for one of the cryptocurrencies is shifted with respect to the other. Such a shift suppresses the cross-correlations partially for short time scales, but does not remove them completely. We did not observe any qualitative asymmetry in the results for the two choices of a leading asset. The cross-correlations for the simultaneous and lagged time series became the same in magnitude for the sufficiently long scales.
Sotirios Oikonomopoulos, Katerina Tzafilkou, Dimitrios Karapiperis, Vassilios S. Verykios
In a paper that was anonymously published and signed by the pseudonym Satoshi Nakamoto, Bitcoin was introduced to the world. Due to its enormous success, a great number of cryptocurrencies were created in the upcoming years. This exponential growth relies mostly on the extreme volatility of the market, which led many people to become interested and get involved, primarily for profit. Cryptocurrency enthusiasts tend to share and learn news and opinions on social media platforms, one of the most popular being Twitter. In this paper, we study the extent to which Twitter sentiment analysis can be used to predict price fluctuations for cryptocurrencies. Initially, we gathered tweets and price data of seven of the most popular cryptocurrencies, which were processed to perform sentiment analysis using Valence Aware Dictionary for Sentiment Reasoning (VADER). The time-series stationarity was determined with Augmented Dicky Fuller (ADF) Kwiatkowski Phillips Schmidt Shin (KPSS) tests and then Granger Causality testing took place. While price fluctuations seem to cause sentiment for Bitcoin, Cardano, XRP and Doge, predictability was found for Ethereum and Polkadot, based on a bullishness ratio. Finally, predictability of price returns is examined with Vector Autoregression (VAR) and highly accurate forecasts for two of the seven cryptocurrencies were achieved. More specifically, price forecasts of Ethereumâs and Polkadotâs prices reached 99.67% and 99.17% accuracy, respectively.
This is the first study to examine the quantile connectedness for returns-volume and volatility-volume pairs for the three non-fungible tokens (THETA, Tezos, and Enjin Coin) using the quantile VAR approach. The results report the highest connectedness of volume with returns and volatility in the extreme upper quantile compared to other quantiles, implying the asymmetric connectedness. The spillover effect is observed from volume to returns and volatilities in extreme upper and lower market conditions, whereas opposite direction of spillovers is evident for the selected non-fungible tokens at median quantile. Our findings are useful for investors in predicting the returns and risk of NFTs using trading volume in the extreme market conditions.
Virtual currency has been greeted with an avalanche of attention these days. In this case, allocate investments into traditional assets and virtual currency properly seems very important. In this paper, we select gold and bitcoin as our research objects, and select a series of representative indicators in the financial field. After data preprocessing, XGBoost algorithm is used to sort the importance of indicators, thus eliminating some unimportant indicators. Next, LSTM is used to predict the price of gold and bitcoin respectively. Therefore, the portfolio can be built based on it. In reality, trades often come with transaction costs. So we improve the Mean-Variance model considering the transaction costs, so as to get the initial portfolio strategy. On this basis, taking investment potential into account, we propose Traffic Light Signal(TLS) model, and successfully increasing the gross profit rate from 11.582% to 13.614%. Finally, we prove our portfolio model earns the highest returns by comparing it to other traditional portfolio models in terms of metrics Cumulative Yield, Annual Yield, and Max Drawdown Ratio.