This study examines the connectedness between technology stocks, cryptocurrencies, and non-fungible tokens (NFTs) using daily returns and risk data. We found that while there is strong connectedness within asset classes, connectedness between different types of assets is weak. Structural breaks in the VAR system did not change the degree of connectedness. Our findings suggest that interconnectivity between these assets is not significant enough to indicate a high level of correlation. This research provides valuable insights into the interplay between these markets and suggests diversifying portfolios to mitigate risks associated with these assets.
This paper investigates the connectedness among eighteen cryptocurrency assets including NFT, DeFi, gold-backed cryptocurrencies, and traditional cryptocurrencies. We also compute the Optimal hedge ratio for each pair of (gold-backed) cryptocurrency-NFT/DeFi and assess their hedge effectiveness. To this end, we use a combination of econometric methods. Our sample period goes from 01/11/2021 to 21/02/2023, making the empirical analysis insightful and interesting as it includes the Covid-19 health crisis and the RussiaâUkraine war. Our empirical findings highlight the dissimilarities between different cryptocurrencies in terms of connectedness with NFT/DeFi assets. They also reflect the diversification benefits generated by the inclusion of gold-backed cryptocurrencies into NFT/DeFi portfolios, in particular in times of unprecedented events. These findings could be useful for crypto-investors who search to diversify their portfolios.
ABSTRACT The introduction of regulated CME futures contracts on Bitcoin in 2017 raised an expectation that cryptocurrencies would become part of mainstream financial markets. This also heightened links between traditional markets and Bitcoin, implying that the cryptocurrency would be subject to systematic spillovers. This paper uses highâfrequency data to examine whether Bitcoin basis risk is linked to investor sentiment from established financial markets. Our findings indicate that extreme investor sentiment, as reflected by the tail risk in various volatility indices, including the VIX, consistently correlates with a negative Bitcoin basis, where Bitcoin futures prices are lower than spot prices. Fluctuations significantly influence this relationship in the trading volume of Bitcoin futures and are more pronounced during periods of substantial unexpected inflation and deflation. These results underline the complex dynamics between market sentiment and cryptocurrency pricing, offering insights with substantial implications for investors and policymakers.
Abstract The cryptocurrency market is a complex and rapidly evolving financial landscape in which understanding the inter- and intra-asset dependencies among key financial variables, such as return and liquidity, is crucial. In this study, we analyze daily return and liquidity data for six major cryptocurrencies, namely Bitcoin, Ethereum, Ripple, Binance Coin, Litecoin, and Dogecoin, spanning the period from June 3, 2020, to November 30, 2022. Liquidity is estimated using three low-frequency proxies: the Amihud ratio and the Abdi and Ranaldo (AR) and Corwin and Schultz (CS) estimators. To account for autoregressive and persistent effects, we apply the autoregressive integrated moving average-generalized autoregressive conditional heteroscedasticity (ARIMA-GARCH) model and subsequently utilize the copula method to examine the interdependent relationships between the return on and liquidity of the six cryptocurrencies. Our analysis reveals strong cross-asset lower-tail dependence in return and significant cross-asset upper-tail dependence in illiquidity measures, with more pronounced dependence observed in specific cryptocurrency pairs, primarily involving Bitcoin, Ethereum, and Litecoin. We also observe that returns tend to be higher when liquidity is lower in the cryptocurrency market. Our findings have significant implications for portfolio diversification, asset allocation, risk management, and trading strategy development for investors and traders, as well as regulatory policy-making for regulators. This study contributes to a deeper understanding of the cryptocurrency marketplace and can help inform investment decision making and regulatory policies in this emerging financial domain.
The efficient market hypothesis encounters scrutiny from behavioral finance insights, highlighting the pronounced influence of investor emotions on market dynamics, a phenomenon especially evident in the tumultuous cryptocurrency markets. This investigation utilizes the autoregressive distributed lag (ARDL) model and the error correction model (ECM) to examine the impact of the Bitcoin Sentiment Index (BSI), also known as the Crypto Fear & Greed Index (CFGI), on Bitcoin returns, leveraging monthly data spanning from 2016 to 2021. The ARDL analysis identifies a positive and statistically significant correlation between BSI and Bitcoin returns, indicating that strong sentiment may beneficially affect Bitcoinâs long-term returns. Concurrently, the ECM analysis reveals that fluctuations in the BSI positively influence the changes in Bitcoin returns in the short term. The error correction term demonstrates a significantly negative value, signifying an expedient adjustment toward long-term equilibrium following transient disturbances. These findings remain robust upon the integration of additional macroeconomic control variables. Unlike prior studies centered on singular sentiment indicators or limited temporal analyses, this research employs an extensive sentiment measure over an extended duration. The integrated application of ARDL and ECM methodologies facilitates a thorough and rigorous examination of short-term fluctuations alongside long-term equilibrium dynamics.
Abstract This study examines how global geopolitical risks , threats , and acts impact the daily returns of 10 major cryptocurrencies (BTC, ETH, USDT, XRP, BNB, USDC, BCH, DOGE, LTC, and ADA). The statistically significant results that are robust to the consideration of alternative model specifications and control variables suggest that there is strong evidence for (i) ETH, XRP, BNB and BCH responding negatively to the shocks of geopolitical risks , (ii) BTC, ETH, BNB, BCH, LTC and ADA responding negatively to the shocks of geopolitical threats , and (iii) all 10 cryptocurrencies not responding to the shocks of geopolitical acts . As these 10 cryptocurrencies do not respond positively to any of the three shocks in a robust and statistically significant way either, it is implied that none of them offer a reliable hedge against geopolitical risks.
This paper analyses the transition of Ethereum (ETH) from the energy-intensive Proof-of-Work (PoW) to the less energy-intensive Proof-of-Stake (PoS). We analyze returns, volatility, return correlations and volume of ETH, ETC and Bitcoin for all events in the lead-up to the actual change from PoW to PoS also labelled "the merge." The analysis suggests that some investors value the less energy-intensive mining mechanism and invest in ETH. However, since the overall effect is weak, we conclude that despite all the media attention and the stated concerns about the high energy-intensity of Bitcoin and PoW, most investors do not react to the change with an increased investment in Ethereum.
The growing interest in cryptocurrencies has drawn the attention of the financial world to this innovative medium of exchange. This study aims to explore the impact of cryptocurrencies on portfolio performance. We conduct our analysis retrospectively, assessing the performance achieved within a specific time frame by three distinct portfolios: one consisting solely of equities, bonds, and commodities; another composed exclusively of cryptocurrencies; and a third, which combines both 'traditional' assets and the best-performing cryptocurrency from the second portfolio.To achieve this, we employ the classic variance-covariance approach, utilizing the GARCH-Copula and GARCH-Vine Copula methods to calculate the risk structure. The optimal asset weights within the optimized portfolios are determined through the Markowitz optimization problem. Our analysis predominantly reveals that the portfolio comprising both cryptocurrency and traditional assets exhibits a higher Sharpe ratio from a retrospective viewpoint and demonstrates more stable performances from a prospective perspective. We also provide an explanation for our choice of portfolio optimization based on the Markowitz approach rather than CVaR and ES.
In the realm of cryptocurrency, the prediction of Bitcoin prices has garnered substantial attention due to its potential impact on financial markets and investment strategies. This paper propose a comparative study on hybrid machine learning algorithms and leverage on enhancing model interpretability. Specifically, linear regression(OLS, LASSO), long-short term memory(LSTM), decision tree regressors are introduced. Through the grounded experiments, we observe linear regressor achieves the best performance among candidate models. For the interpretability, we carry out a systematic overview on the preprocessing techniques of time-series statistics, including decomposition, auto-correlational function, exponential triple forecasting, which aim to excavate latent relations and complex patterns appeared in the financial time-series forecasting. We believe this work may derive more attention and inspire more researches in the realm of time-series analysis and its realistic applications.
Cryptocurrencies are changing how we view and interact with traditional currencies, and they have become a disruptive force in the financial industry. Accurate price prediction is becoming more and more important as the bitcoin industry grows in size and complexity. This paper provides a thorough examination of deep learning models used in bitcoin price prediction. We explore the dynamic and unpredictable character of the cryptocurrency market, where price swings can happen quickly and without warning. To comprehend the present state of the art in this domain and pinpoint the shortcomings of the deep learning models in use today, we examine the body of existing literature. The data collecting and preprocessing methods used to get the bitcoin market data ready for modeling are described in the methodology section. Numerous deep learning modelsâRecurrent Neural Networks among them, Convolutional neural networks (CNNs) and Long Short-Term Memory (LSTM) networks are investigated. We go over hyperparameter tweaking, model training, and the assessment metrics that are used to gauge the performance of the model. We offer a thorough case study that focuses on forecasting the price of a particular cryptocurrency, like Bitcoin, in order to offer empirical insights. Our results provide light on the difficulties and possibilities involved in this project, emphasizing the need for creative solutions to address the market
The World Health Organization (WHO) announced the Covid-19 pandemic in March 2020, which had a negative impact on economic activities and financial markets. Cryptocurrencies with blockchain technology, whose history is not old, took off in the Covid-19 period thanks to digital transformation and became popular in the financial markets. However, the fact that cryptocurrencies lose blood after the pandemic period. This study examines the volatility of cryptocurrencies before, during and after the pandemic Covid-19 using data from 4 cryptocurrencies (Bitcoin, Ethereum, Binance and Litecoin) and the CCI30 index, using autoregressive conditional variance models with two dummy variables. According to the results, the volatility of cryptocurrencies decreases throughout the pandemic period, moreover, decreases more after the pandemic compared to the pre-pandemic period. Investors should be cautious about investing in these risky instruments, which may become popular again in the future, just in case.
As a consequence of rising geo-economic issues, global currency values have declined during the last two years, stock markets have performed poorly, and investors have lost money. Consequently, there is a renewed interest in digital currencies. Cryptocurrency is a fresh kind of asset that has evolved as a result of fintech innovations, and it has provided a major research opportunity. Due to price fluctuation and dynamism, anticipating the price of cryptocurrencies is difficult. There are hundreds of cryptocurrencies in circulation around the world and the demand to use a prediction system for price forecasting has increased manifold. Hence, many developers have proposed machine learning algorithms for price forecasting. Machine learning is fast evolving, with several theoretical advances and applications in a variety of domains. This study proposes the use of three supervised machine learning methods, namely linear regression, support vector machine, and decision tree, to estimate the price of four prominent cryptocurrencies: Bitcoin, Ethereum, Dogecoin, and Bitcoin Cash. The purpose of this study is to compute and compare the precision of all three techniques over all four datasets.
The study explores the spillover effect on Ethereum â one of the leading cryptocurrencies â stemming from key variables in the domains of cryptocurrencies, investor sentiment, and traditional financial markets. This paper is the first to analyze the influence of such dominant representatives from diverse, external fields on cryptocurrency. We select bitcoin, the Fear and Greed index, the Standard and Poorâs 500 index and the United States Dollar to Euro Exchange Rate as representatives to investigate the spillover effect on Ethereum. Utilizing linear regression models and vector autoregressive (VAR) models, we find strong correlations between Ethereumâs return and that of Bitcoinâs, along with investor sentiment. However, the influence of financial market variables on Ethereum are found to be virtually static and negligible. This research offers valuable insights to those seeking to forecast or manipulate crypto market movement through analyzing the complex interplay between these variables and Ethereum.
This paper illustrates the working process of predicting the Bitcoin price applying ARIMA, SARIMA and linear regression. Since more and more machine learning models were developed and tested in the financial field, these three models are selected to examine their reliabilities. In this study, three methodologies have been used for the Bitcoin predictions under the data set of Bitcoin historical prices. With the help of python notebook, order (1, 1, 1) and seasonal order (0, 1, 1, 12) were applied to the predictions in ARIMA and SARIMA respectively. In terms of linear regression, this paper used two independent variables including historical data and trading volume to predict the Bitcoin prices. It was discovered that the predictive graph for these three methodologies can match the actual value well, and linear regression performs the best. Considering the rapid development of machine learning methods, adopting alternative methods deserve in-depth investigations.
The energy sector is undergoing a period of technological transformation, driven by the emergence of blockchain and smart contracts. These technologies have the potential to revolutionize energy markets and significantly reduce transaction costs, improve efficiency, and increase transparency. The rising energy prices in recent years have been a cause for global concern. As the EU recorded historically high energy prices in 2022, according to the EU Council, this price rise is linked to increased energy demand following the COVID-19 pandemic, the war in Ukraine, and the acceleration of climate change. This paper aims to critically examine the current state of blockchain and smart contracts technology in the energy sector, focusing on use cases, key challenges, and potential solutions. It further explores the impact of these technologies on energy markets and their potential to contribute to a sustainable, low-carbon energy future. Finally, it examines the prospects of blockchain and smart contract technologies to transform the energy industry and the policy implications for governments and regulators.
Ahmed Bouteska, Mohammad Zoynul Abedin, Petr HĂĄjek, Kunpeng Yuan
Cryptocurrency price forecasting is attracting considerable interest due to its crucial decision support role in investment strategies. Large fluctuations in non-stationary cryptocurrency prices motivate the urgent need for accurate forecasting models. The lack of seasonal effects and the need to meet a number of unrealistic requirements make it difficult to make accurate forecasts using traditional statistical methods, leaving machine learning, particularly ensemble and deep learning, as the best technology in the area of cryptocurrency price forecasting. This is the first work to provide a comprehensive comparative analysis of ensemble learning and deep learning forecasting models, examining their relative performance on various cryptocurrencies (Bitcoin, Ethereum, Ripple, and Litecoin) and exploring their potential trading applications. The results of this study reveal that gated recurrent unit, simple recurrent neural network, and LightGBM methods outperform other machine learning methods, as well as the naive buy-and-hold and random walk strategies. This can effectively guide investors in the cryptocurrency markets.