Indranil Ghosh, Esteban Alfaro, Matías Gámez, Noelia García
Non Fungible Tokens (NFT) and Decentralized Finance (DeFi) assets have seen a growing media coverage and garnered considerable investor traction despite being classified as a niche in the digital financial sector. The lack of substantial research to demystify the dynamics of NFT and DeFi coins motivates the scrupulous analysis of the said sector. This work aims to critically delve into the evolutionary pattern of the NFTs and DeFis for performing predictive analytics of the same during the COVID-19 regime. The multivariate framework comprises the systematic inclusion of explanatory features embodying technical indicators, key macroeconomic indicators, and constructs linked to media hype and sentiment pertinent to the pandemic, nonlinear feature engineering, and ensemble machine learning. Isometric Mapping (ISOMAP) and Uniform Manifold Approximation and Projection (UMAP) techniques are conjugated with Gradient Boosting Regression (GBR) and Random Forest (RF) for enabling the predictive analysis. The predictive performance rationalizes the frameworks' capacity to accurately predict the prices of the majority of the NFT and DeFi coins during the ongoing financial distress period. Additionally, Explainable Artificial Intelligence (XAI) methodologies are used to comprehend the nature of the impact of the explanatory variables. Findings suggest that the daily movement of the NFTs and DeFi highly depends on their past historical movement.
Yang Wan, Yuncheng Song, Xinqian Zhang, Zhichao Yin
We explore the dynamics and determinants of volatility connectedness between cryptocurrencies and energy. We employed a block dynamic equicorrelation model and a group volatility connectedness measurement to measure the cross-equicorrelation and volatility connectedness between cryptocurrencies and energy. We also adopted dynamic model averaging to identify the time-varying drivers. The results suggest that changes in cross-equicorrelation between the two groups were affected by influential global events and increased after the COVID-19 pandemic. Volatilities were transmitted in both directions between cryptocurrencies and energy, but the transmission from energy to cryptocurrencies is by far the strongest. The driver identification implies that the factors related to cryptocurrencies and global financial markets had important roles in explaining the volatility connectedness from cryptocurrencies to energy in some periods after the COVID-19 pandemic, but the effects were marginal. In contrast, factors such as electricity consumption, cryptocurrency turnovers, and VIX were important in affecting the volatility connectedness from energy to cryptocurrencies, and the effects depended on factors and changed over time.
This paper introduces a unique perspective towards Bitcoin safe haven and hedge properties through the Bitcoin halving cycle. The Bitcoin halving cycle suggests that Bitcoin price movement follows specific sequences, and Bitcoin price movement is independent of other assets. This has significant implications for Bitcoin properties, encompassing its risk profile, volatility dynamics, safe haven properties, and hedge properties. Bitcoin’s institutional and industrial adoption gained traction in 2021, while recent studies suggest that gold lost its safe haven properties against the S&P500 in 2021 amid signs of funds flowing out of gold into Bitcoin. Amid multiple forces at play (COVID-19, halving cycle, institutional adoption), the potential existence of regime changes should be considered when examining volatility dynamics. Therefore, the objective of this study is twofold. The first objective is to examine gold and Bitcoin safe haven and hedge properties against three US stock indices before and after the stock market selloff in March 2020. The second objective is to examine the potential regime changes and the symmetric properties of the Bitcoin volatility profile during the halving cycle. The Markov Switching GARCH model was used in this study to elucidate regime changes in the GARCH volatility dynamics of Bitcoin and its halving cycle. Results show that gold did not exhibit safe haven and hedge properties against three US stock indices after the COVID-19 outbreak, while Bitcoin did not exhibit safe haven or hedge properties against the US stock market indices before or after the COVID-19 pandemic market crash. Furthermore, this study also found that the regime changes are associated with low and high volatility periods rather than specific stages of a Bitcoin halving cycle and are asymmetric. Bitcoin may yet exhibit safe haven and hedge properties as, at the time of writing, these properties may manifest through sustained adoption growth.
While volatility spillover is a vital research area in financial economics (due to its importance for risk valuation and portfolio diversification strategies), the volatility linkage between Bitcoin and electricity/energy markets has not received adequate attention. As the Bitcoin mining cost comes mainly from electricity (which is highly dependent on natural gas), we hypothesize that natural gas is a non-trivial Bitcoin price volatility driver and aim to test if this is the case. Specifically, we employ a widely used model called the HAR-RV model to assess volatility spillover across Bitcoin and natural gas using high-frequency data. We find a spillover effect from natural gas to Bitcoin, and the positive (negative) component of natural gas volatility stabilizes (destabilizes) Bitcoin volatility. The spillover effect is further examined and confirmed using an out-of-sample approach.
Kate Murray, Andrea Rossi, Diego Carraro, Andrea Visentin
Traders and investors are interested in accurately predicting cryptocurrency prices to increase returns and minimize risk. However, due to their uncertainty, volatility, and dynamism, forecasting crypto prices is a challenging time series analysis task. Researchers have proposed predictors based on statistical, machine learning (ML), and deep learning (DL) approaches, but the literature is limited. Indeed, it is narrow because it focuses on predicting only the prices of the few most famous cryptos. In addition, it is scattered because it compares different models on different cryptos inconsistently, and it lacks generality because solutions are overly complex and hard to reproduce in practice. The main goal of this paper is to provide a comparison framework that overcomes these limitations. We use this framework to run extensive experiments where we compare the performances of widely used statistical, ML, and DL approaches in the literature for predicting the price of five popular cryptocurrencies, i.e., XRP, Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), and Monero (XMR). To the best of our knowledge, we are also the first to propose using the temporal fusion transformer (TFT) on this task. Moreover, we extend our investigation to hybrid models and ensembles to assess whether combining single models boosts prediction accuracy. Our evaluation shows that DL approaches are the best predictors, particularly the LSTM, and this is consistently true across all the cryptos examined. LSTM reaches an average RMSE of 0.0222 and MAE of 0.0173, respectively, 2.7% and 1.7% better than the second-best model. To ensure reproducibility and stimulate future research contribution, we share the dataset and the code of the experiments.
We considered the daily price dynamics of the US Bitcoin market in the period from 2015 to 2022. In the first step, we used a singular value decomposition (SVD) entropy method for assessing time-varying informational efficiency over different time scales, from weeks to quarters. It was shown that the US Bitcoin market has been informationally efficient most of the time, except for some isolated periods where the returns exhibited deviations from the random behavior. The COVID-19 pandemic has not impacted the informational efficiency. This suggests that the Bitcoin market is unpredictable, and no reliable predictions can be obtained. A further analysis was carried out by considering the recurrence intervals for different positive and negative returns. We found that the distribution of recurrence intervals for positive and negative returns is asymmetric, with mean values higher for negative returns. We found that the distribution of recurrence intervals can be described by a stretching exponential distribution, such that the empirical and analytical hazard probabilities as functions of the elapsed time show good agreement.
Çalışmanın amacı kripto para piyasasına öncülük eden Bitcoin ile seçilmiş altcoinler arasındaki ilişkinin ve nedenselliğin varlığını tespit ederek yatırımcıların kararlarına ışık tutmaktır. Bu amaç doğrultusunda çalışmada, Bitcoin (BTC) ile Ethereum(ETH), Binance Coin (BNB), Cardano (ADA) , Litecoin (LTC), Tron (TRX), Stellar (XLM), IOTA altcoinlerinin arasındaki eşbütünleşme ve nedensellik ilişkisi araştırılmıştır. Uzun dönemli ilişkinin var olup olmadığı Engle Granger Eşbütünleşme yöntemiyle, kısa dönemli ilişkinin var olup olmadığı Sıradan En Küçük Kareler Yöntemiyle (OLS), nedensellik ilişkisinin var olup olmadığı ise Toda Yamamato Nedensellik yöntemiyle test edilmiştir. Çalışma kapsamında veri seti olarak 14.06.2018-01.12.2021 tarihleri arasında kripto para birimlerinin günlük kapanış fiyatları kullanılmıştır. Araştırma sonucunda Engle Granger Testine göre BTC ile seçilen altcoinler arasında uzun dönemli eşbütünleşme ilişkisi bulunamamıştır. OLS testine göre bağımsız ve bağımlı değişkenler arasında kısa dönemli ilişkinin var olduğu sonucuna ulaşılmıştır. Toda Yamamato Testine göre BTC’den ADA, ETH, IOTA, TRX, XLM alt coinlerine doğru granger nedensellik olduğu, BTC’den BNB ve LTC coinlerine doğru granger nedensellik olmadığı ve BTC’den etkilenmedikleri sonucuna ulaşılmıştır.
We investigate the multifractal properties of daily price returns and trading volume variations in 35 cryptocurrencies by using the method of wavelet leaders prior and during the COVID-19 pandemic. The obtained results from the analysis of scaling exponent functions and multifractal spectrums show that, in general, price returns and trading volume variations exhibit multifractal properties prior to the COVID-19 pandemic and that they tend to exhibit monofractal behavior during the pandemic. As a result, the level of multifractality diminished during the COVID-19 for both price returns and trading volume variations. Since complexity in price returns and trading volume variations decreased during the pandemic, cryptocurrencies may offer an interesting investment during times of serious world economic downturns.
Numerous research have been done to predict cryptocurrency prices since cryptocurrency prices affect global economic and monetary systems. However, investigations using linear connection approaches and technical analysis indicators frequently fall short of providing an explanation for changes in the pattern of BitCoin pricing. This paper is proposed to study time-varying parameters with long short-term memory (LSTM). The study is investigated on a dataset retrieved from Binance from March 2022 to April 2022. The proposed LSTM used a variety of hyperparameter settings, particularly time parameters, to predict the cryptocurrency price (BTC/USDT) on the dataset. Additionally, it is evaluated in terms of mean absolute percentage error (MAPE) in comparison to smooth moving average (SMA), weighted moving average (WMA), and exponential moving averages (EMA). From the investigation, using the previous 3 days for prediction gives the lowest of the MAPE values and the proposed LSTM outperformed the other models. When considering the last three days' value of pricing, the indicated LSTM offers the best accurate prediction, with a MAPE percentage of 0.0927%.
This study estimates the effects of the dual long memory property and structural breaks on the persistence level of six major cryptocurrency markets. We apply the Bai and Perron structural break test, Inclán and Tiao’s iterated cumulative sum of squares (ICSS) algorithm, and the fractionally integrated generalized autoregressive conditional heteroscedasticity (FIGARCH) model, with different distributions. The results show that long memory and structural breaks characterize the conditional volatility of cryptocurrency markets, confirming our hypothesis that ignoring structural breaks leads to an underestimation of the persistence of volatility modeling. The ARFIMA-FIGARCH model, with structural breaks and a skewed Student-t distribution, fits the cryptocurrency market’s price dynamics well.
In this paper we develop a linear expectile hidden Markov model for the analysis of cryptocurrency time series in a risk management framework. The methodology proposed allows to focus on extreme returns and describe their temporal evolution by introducing in the model time-dependent coefficients evolving according to a latent discrete homogeneous Markov chain. As it is often used in the expectile literature, estimation of the model parameters is based on the asymmetric normal distribution. Maximum likelihood estimates are obtained via an Expectation-Maximization algorithm using efficient M-step update formulas for all parameters. We evaluate the introduced method with both artificial data under several experimental settings and real data investigating the relationship between daily Bitcoin returns and major world market indices.
Among the most recent and quickly growing industries worldwide is metaverse cryptocurrency. Despite the fact that the first cryptocurrency namely Bitcoin was only developed 13 years ago, the use and value of digital currencies have increased dramatically. Their marketplaces are among the most intricate and unpredictable markets over the globe. Despite being a decentralized system that does not have a single point of failure, it is commonly known that many individuals who invest in it lose their money. In this article, we attempt to use Monte Carlo simulation to forecast the price of a selected few well-known cryptocurrencies for the next 1000 days namely Doge coin (DOGE), Ethereum (ETH), Litecoin (LTC) and HEX.The mean return on a cryptocurrency and the standard deviation of past returns are the two key factors that affect how the simulations end out. Additionally, the study evaluates the projected returns for cryptocurrencies as well as the correlation between all the different currencies. It also looks into the statistics of cryptocurrency, average true range, volatility and cumulative returns. By calculating the growth rate and sharpe ratio, the entire research would also assist investors in deciding if they should purchase a specific cryptocurrency or not.
Blanka Łęt, Konrad Sobański, Wojciech Świder, Katarzyna Włosik
Stablecoins are a relatively recent phenomenon in the cryptocurrency market, gaining prominence particularly since 2018. These digital currencies are usually pegged to nonvolatile underlying assets, offering a solution to the problem of the high price volatility of nonstable cryptocurrencies such as Bitcoin. Therefore, stablecoins seem attractive to participants in the cryptocurrency market, especially in times of turmoil. This paper aims to measure the spillover effect of shocks in volatile cryptocurrencies (Bitcoin, Ethereum, Litecoin and the Cryptocurrencies Index) on the activity of investors in the stablecoin market (Tether, USD Coin, Binance USD, DAI, Paxos, Huobi USD, and Gemini USD). Using the spectral representation of variance decomposition, the paper measures the strength of the spillover effect and investigates whether the cryptocurrency market processes information rapidly or slowly. The results suggest that shocks in the volatile cryptocurrency market moderately drive the popularity of stablecoins. The spillover effects on stablecoin popularity are short-lived, as they are observed mostly within up to 3 days of the shock. The findings indicate that investors use stablecoins as safe haven assets after bad news in the volatile cryptocurrency market; however, they react more strongly to news related to individual cryptocurrencies and not so intensively to the general sentiment in the cryptocurrency market. Investors with larger capital are more resistant to shocks.
Today's discussion centers on whether cryptocurrencies may be used to pay for products and services in developed and underdeveloped nations. The role of cryptocurrencies as investment and speculative trading vehicles is also expanding. The cryptocurrency bitcoin serves as an illustration of such use. The first cryptocurrency to develop value without initially satisfying requirements and carrying any type of collateral in the form of traditional currencies is bitcoin. The analysis of the variables influencing the price of bitcoin is one of the most hotly debated subjects in the financial literature. The purpose of this study is to look into the connection between the price of natural gas and crude oil and the cryptocurrency Bitcoin. In this study, the effect of Brent oil, crude oil and natural gas prices on Bitcoin was examined. For the data containing the weekly time series for the period 05.01.2020-26.12.2021, FMOLS and DOLS tests were conducted, which show the coefficient of cointegration, causality and relationship. According to the findings of the study, according to the FMOLS test, 1% Bitcoin in brent oil price increases 0.000176% (probability values according to DOLS do not confirm the effect). Likewise, when we look at crude oil, according to FMOLS test, 1% Bitcoin in crude oil price increases 0.000180% (the probability values according to DOLS do not confirm the effect). When we look at the changes in the Bitcoin price, according to the DOLS test, a 1% increase in the Bitcoin price increases the Brent oil by 77.86132% (the probability values according to FMOLS do not confirm the effect).
Abstract As a financial innovation of the information age, cryptocurrency is a complex concept with clear advantages and disadvantages and is worthy of discussion. Exploring from a terrorism perspective, this study uses the time-varying parameter/stochastic volatility vector autoregression model to explore the risk hedging and terrorist financing capabilities of Bitcoin. Empirical results show that both terrorist incidents and brutality may explain Bitcoin price, but their effects are slightly different. Compared to terrorist brutality, terrorist incidents have a weaker impact on Bitcoin price, showing that Bitcoin investors are more concerned about the number of deaths than the frequency of terrorist attacks. In turn, the impact of Bitcoin price on terrorist attacks is negligible. Bitcoin is a potential means of financing terrorism, but it does not currently play an important role. Our research findings can help investors analyze and predict Bitcoin prices and help improve the theoretical system of anti-terrorist financing, helping to maintain world peace and security.
As a new type of digital currency, Bitcoin is considered as “future gold” by various scholars. Therefore, this study considers Bitcoin and gold as a group of hedging assets to conduct investment research and it also discusses the investment rules between Bitcoin and gold: prediction of the rise and fall of Bitcoin, comparison of the characteristics of Bitcoin and gold, and the impact of the transaction procedures of Bitcoin and gold on the final trading results, and formulates trading strategies through optimization algorithms. Then, four machine learning algorithms, i.e., LSTM, BP neural network, Adaboost, and Bagging, are introduced to predict the rise and fall of gold and Bitcoin the next day, and then, the entropy weight method is used to synthesize four predicted results to ensure the robustness of the predicted results. To establish the optimal trading strategy, this study considers the maximum expected return as the goal to develop a single-objective optimization model and historical five-day price volatility as a risk factor. In this study, ant colony, simulated annealing, and genetic algorithms are used to solve the single-objective optimization model. Finally, we conclude that Bitcoin, similar to other financial assets, e.g., gold, is sensitive to shocks and volatile and possesses a relatively quiet cycle. When Bitcoin has an asymmetric impact, Bitcoin and gold can equally treat transactions.
Krishna Pal Sharma, Shivam Kumar Singh, Ankur Choudhary, Himanshu Goel
The world's most well-known cryptocurrency, Bitcoin, is much attractive to financial market players due to its recent price boom and fall. It is very difficult to anticipate because of the substantial volatility of the Bitcoin conversion scale. Forecasting of its behavior is crucial for the financial industry sectors. Some of the research works have been used machine learning techniques on past trends data of Bitcoin for its price predictions but they lack in accuracy. Therefore, the aim of this paper is to look into the global crypto-currency price movement trends about social media communication data as well as historical data trends. The thought is to analyze informative trends in online groups and social media networks to fully understand and extract useful information which could be used to improve the prediction accuracy of cryptocurrency price fluctuations. In this work we examine the behavior of Artificial Neural Networks (ANN), Recurrent Neural Networks (RNN) with LSTM and GRU, ARIMA model, Random forests(RF), and others for price direction predictions. Similarly, the SVM and ANN are used to predict Bitcoin’s minimum, maximum, and closing prices. The outcomes of the forecasts are also utilized as inputs to improve forecasts of price movement. The results showed that performance was greatly improved by the characteristics that were picked as well as by the effective machine learning and deep learning techniques.
Abstract There has been a tremendous growth in cryptocurrencies, which has challenged policy makers around the globe. We obtain millisecond data of some of the most frequently traded cryptocurrencies – bitcoin, ethereum, ripple, litecoin and dash – and two cryptocurrency indices – CRIX and CCI30 – to examine their profitability. Our profitability findings suggest that cryptocurrency traders generate significant profits after considering reasonable transaction costs. We also observe that cryptocurrency market participants can expand and sustain the levels of profitability levels in the subsequent trading activity. Our robustness checks with more recent post‐Covid data are consistent with the initial profitability findings, although we observe lower levels of profits for the two indices and weaker profit persistency for all digital assets.