Investment in cryptocurrencies has garnered substantial attention in the recent past as the prices for these digital currencies started recording all-time highs. While there are numerous contenders in the cryptocurrency market, bitcoin has emerged to be the most popular and sought after digital currency. Despite its popularity, the theoretical understanding of the value of this cryptocurrency is still limited. Hence this study aims to find out the significant predictors of the bitcoin price and build a machine-learning based model to evaluate and predict the complex phenomenon of bitcoin price. Here we contribute to the extant literature by searching for the potential contributors of bitcoin prices ranging from fundamental, macroeconomic, financial, speculative, and technical sources to the most marked event of 2020 i.e., Covid19 pandemic. For this purpose, we have used state-of-the-art machine learning, deep learning, and statistical time-series models (univariate and multivariate) to forecast bitcoin price. The study revealed that deep learning models performed almost at par with Random Forest model for both pre- and whilst-Covid19 era. Traditional time-series models, namely VAR and VECM gave the most consistent performance within acceptable margins for both pre- and whilst-Covid era. We have also found that macroeconomic factors play an important role in determining bitcoin price formulation process during both periods, while mining difficulty and market sentiment factors gain more importance during pre-Covid period. In addition, number of covid cases is also found to be a significant factor for the prediction of bitcoin price during whilst-Covid period.
The behavior of the Bitcoin market is dynamic and erratic, impacted by a range of elements including news developments and investor mood. One well-known aspect of bitcoin is its extreme volatility. This study uses both conventional econometric techniques and deep learning algorithms to anticipate the volatility of Bitcoin returns. The research is based on historical Bitcoin price data spanning October 2014 to February 2022, which was obtained using the Yahoo Finance API. In this work, we contrast the efficacy of generalized autoregressive conditional heteroskedasticity (GARCH) and threshold ARCH (TARCH) models with long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and multivariate Bi-LSTM models. Model effectiveness is evaluated by means of root mean squared error (RMSE) and root mean squared percentage error (RMSPE) scores. The multivariate Bi-LSTM model emerges as mostly effective, achieving an RMSE score of 0.0425 and an RMSPE score of 0.1106. This comparative scrutiny contributes to understanding the dynamics of Bitcoin volatility prediction, offering insights that can inform investment strategies and risk management practices in this quickly changing environment of finance.
Muhammad Abubakr Naeem, Nadia Arfaoui, Larisa Yarovaya
Artificial Intelligence (AI) stands as a transformative force across business, technology, and science, yet its comprehensive impact on innovative industries remains relatively unexplored. This study delves into the interconnectedness between AI and pivotal sectors such as cryptocurrency , blockchain, metaverse, democratized banking, and Cleantech, among others. Employing the conditional autoregressive value-at-risk (CAViaR) and time-varying parameters vector autoregressions (TVP-VAR) methods, we scrutinize daily data spanning from June 1, 2018, to October 11, 2023, encompassing 12 stock indices representing each industry. Our findings unveil a strong contagion effect from AI to other innovative sectors, with the exception of Cleantech, which appears to have decoupled from the AI surge. Notably, democratized banking and the metaverse emerge as key recipients of this contagion. Examination of tail-risk spillovers highlights AI as one of the most influential risk transmitters during market tumult, while cryptocurrency and blockchain consistently function as net risk receivers throughout the sample period. The implications of these findings are multifaceted, offering substantive insights into the risk profiles of these critical innovative sectors. Investors and regulatory bodies stand to benefit significantly from this analysis, as it illuminates potential avenues for portfolio diversification and deepens understanding of contagion mechanisms within these evolving industries.
Abstract This study examines the spillover of Bitcoin's jumps and diffusive variations to traditional assets using highâfrequency data. For our crossâasset analysis, we detect positive spillovers from Bitcoin to risk assets and negative spillovers to defensive assets. We also find evidence of positive jump and diffusion spillovers from Bitcoin to U.S. equity sectors, particularly the financials, technology, consumer discretionary, and communication services sectors. By examining the source of these risk transmissions, we show that these spillovers are exacerbated by increased economic exposures to blockchain and cryptocurrency technologies by U.S. companies. The empirical findings reveal that the price fluctuations of an unregulated asset such as Bitcoin can materially affect the price dynamics of regulated assets.
Agoestina Mappadang, Bayu Adi Nugroho, Setyani Dwi Lestari, Elizabeth Elizabeth · 5 authors
A significant amount of historical returns is needed for the generalized autoregressive conditional heteroscedasticity (GARCH) models to be calibrated. Newer cryptocurrencies, such as non-fungible tokens (NFTs), have relatively limited data to create robust parameter estimates. This study uses a newly developed method, the exponentially weighted moving average (EWMA) model, that takes into account the fat-tailed distributions of returns and volatility response to forecast Value-at-Risk (VaR) and Expected Shortfall (ES). We employ thorough back tests of daily VaR and ES forecasts, which are widely utilized for regulatory approval and are considered to be industry standards. We also use loss function ratios to select the best model. Our results indicate that simpler models are just as good as the complicated ones, provided the simpler models capture fat-tailed distributions of returns. The primary findings hold up through several tests.
The âgas feeâ paid for inclusion in the blockchain is analyzed in two parts. First, we consider how âeffortâ in terms of resources required to process and store a transaction turns into a âgas limit,â which, through a fee comprised of the âbaseâ and âpriority feeâ in the current version of Ethereum, is converted into the cost paid by the user. We adhere closely to the Ethereum protocol to simplify the analysis and to constrain the design choices when considering âmultidimensional gas.â Second, we assume that the âgasâ price is given deus ex machina by a fractional OrnsteinâUhlenbeck process and evaluate various derivatives. These contracts can, for example, mitigate gas cost volatility. The ability to price and trade âforwardsâ in addition to the existing âspotâ inclusion into the blockchain could enable users to hedge against future cost fluctuations. Overall, this article offers a comprehensive analysis of gas fee dynamics on the Ethereum blockchain, integrating supply-side constraints with demand-side modelling to enhance the predictability and stability of transaction costs.
Abraham Itzhak Weinberg, Pythagoras Petratos, Alessio Faccia
Abstract This paper explores the coexistence possibilities of Central Bank Digital Currencies (CBDCs) and blockchain-based cryptocurrencies within a post-quantum computing landscape. It examines the implications of emerging quantum algorithms and cryptographic techniques such as Multi-Party Computation (MPC) and Oblivious Transfer (OT). While exploring how CBDCs and cryptocurrencies might integrate defenses like post-quantum cryptography, it highlights the substantial hurdles in transitioning legacy systems and fostering widespread adoption of new standards. The paper includes comprehensive evaluations of CBDCs in a quantum context. It also features comparisons to alternative cryptocurrency models. Additionally, the paper provides insightful analyses of pertinent quantum methodologies. Examinations of interfaces between these methods and blockchain architectures are also included. The paper carries out considered appraisals of quantum threats and their relevance for cryptocurrency schemes. Furthermore, it features discussions of the influence of anticipated advances in quantum computing on algorithms and their applications. The paper renders the judicious conclusion that long-term coexistence is viable provided challenges are constructively addressed through ongoing collaborative efforts to validate solutions and guide evolving policies.
This article assesses the temporal and dynamic interconnectedness of cryptocurrency, gold, energy, and stock markets, essential for portfolio diversification. Using a TVP-VAR model, we analyze the return and realized volatility from November 11, 2013, to August 22, 2022. The study focuses on Bitcoin, gold, and renewable energy dynamics. Findings show that volatility shocks are most significant in the crude oil market, while Bitcoin's relationship with other assets is weak during non-crisis periods. Gold and Bitcoin's connection is less pronounced during crises. These results provide insights for portfolio optimization in both crisis and non-crisis periods.
As a decentralized digital currency, the price of Bitcoin is affected by multiple factors and has complex and non-linear characteristics. Traditional time series forecasting methods such as ARIMA models have limitations in dealing with these characteristics. In order to overcome these problems, a prediction algorithm based on the ARIMA-LSTM combined model is proposed. This algorithm captures the linear trend of Bitcoin through ARIMA model, and then models the nonlinear features and time dependence through LSTM model to improve the accuracy of prediction. Experimental results show that compared with a single model, the ARIMA-LSTM combination model has better prediction performance when dealing with highly volatile assets such as Bitcoin, which provides a good foundation for digital currency risk management and risk management in the financial market. It provides new ideas for investment decisions.
This study uses a hybrid model of the exponential generalised auto-regressive conditional heteroscedasticity (eGARCH)-extreme value theory (EVT)-Gumbel copula model to investigate the dependence structure between Bitcoin and the South African Rand, and quantify the portfolio risk of an equally weighted portfolio. The Gumbel copula, an extreme value copula, is preferred due to its versatile ability to capture various tail dependence structures. To model marginals, firstly, the eGARCH(1, 1) model is fitted to the growth rate data. Secondly, a mixture model featuring the generalised Pareto distribution (GPD) and the Gaussian kernel is fitted to the standardised residuals from an eGARCH(1, 1) model. The GPD is fitted to the tails while the Gaussian kernel is used in the central parts of the data set. The Gumbel copula parameter is estimated to be α=1.007, implying that the two currencies are independent. At 90%, 95%, and 99% levels of confidence, the portfolioâs diversification effects (DE) quantities using value at risk (VaR) and expected shortfall (ES) show that there is evidence of a reduction in losses (diversification benefits) in the portfolio compared to the risk of the simple sum of single assets. These results can be used by fund managers, risk practitioners, and investors to decide on diversification strategies that reduce their risk exposure.
Barbara BÄdowska-SĂłjka, Piotr WĂłjcik, Sabrina Giordano
Abstract This study aims to explore the dependencies on the cryptocurrency market using social network tools. We focus on the correlations observed in the cryptocurrency returns. Based on the sample of cryptocurrencies listed between January 2015 and December 2022 we examine which cryptos are central to the overall market and how often major players change. Static network analysis based on the whole sample shows that the network consists of several communities strongly connected and central, as well as a few that are disconnected and peripheral. Such a structure of the network implies high systemic risk. The day-by-day snapshots show that the network evolves rapidly. We construct the ranking of major cryptos based on centrality measures utilizing the TOPSIS method. We find that when single measures are considered, Bitcoin seems to have lost its first-mover advantage in late 2016. However, in the overall ranking, it still appears among the top positions. The collapse of any of the cryptocurrencies from the top of the rankings poses a serious threat to the entire market.
The acceleration of the globalization process and the structural changes in technology that emerged in the 2000s have affected financial markets. This interaction in the financial markets has made the emergence of new financial assets necessary. According to the ARDL boundary test results, there is no significant relationship between cryptocurrency markets and stock returns in both the long and short term for the UK financial markets. For the German financial markets, it has been determined that there is a significant and positive long-term relationship between the cryptocurrency market assets Bitcoin and Tether and stock market returns. In the short term, no significant relationship has been detected. For the long term in the U.S. financial markets, it has been determined that there is a significant and positive relationship between Bitcoin, a cryptocurrency market asset, and stock market returns, while there is no significant relationship between Ethereum and Tether with stock market returns. In the short term, no significant relationship has been detected. These findings offer significant implications for policymakers, investors, and market analysts.
The chapter examines the nature of long-run nonlinear trends of the closing price of Ethereum in terms of USD, from 2015m 08 to 2023m 05 using the econometric model of the Box and Jenkinsâ ( 1976 ) methodology of ARIMA (p, d, q) and Hamilton (2018) decomposition. Additionally, the forecast behaviour for 2025m 01 was computed with/without the Hamilton regression filter. The automatically selected ARIMA model of Ethereum price is convergent, and its forecast path for 2025m 01 showed insignificance with seasonal fluctuations. However, its decomposition model is cyclical, cyclically trending, and seasonally fluctuated, with forecast behaviour that is convergent, stable and significant without seasonal variation.
Purpose This study investigates the time-varying volatility spillover connectedness among seven major cryptocurrencies before and during the COVID-19 pandemic. It aims to understand contagion risk and its implications for diversification and financial stability, especially during periods of extreme price volatility. Design/methodology/approach Using the frequency-domain spillover index, the study analyzes the interconnectedness of cryptocurrency markets with daily data from 10 August 2015 to 10 December 2021. This method allows for examining volatility spillovers across different time frequencies. Findings The study finds that cryptocurrencies are highly interconnected at higher frequencies, indicating significant contagion risk and limited short-term diversification opportunities. The spillover effects are frequency-dependent, varying across different time horizons. Practical implications The findings suggest the need for targeted regulatory policies focused on short-term cryptocurrency behavior to maintain financial stability. Investors should exercise caution when using cryptocurrencies for portfolio diversification, given the high interconnectedness and contagion risk. Originality/value This study uniquely contributes to the literature by applying a frequency-domain approach to analyze volatility spillovers across multiple cryptocurrencies, particularly in the context of the COVID-19 pandemic. It provides novel insights into the frequency-dependent nature of spillover effects, offering a deeper understanding of the contagion risk in cryptocurrency markets.
Hanen Ben Ameur, Fouad Jamaani, Mohammed N. Abu-Alfoul
This paper examines gold and cryptocurrencies' hedge and safe-haven capabilities against various downturns, including the COVID-19 pandemic and Geopolitical Risks (GPR), across different market conditions. The study covers a sample period from 2013 to 2021 at a daily frequency, employing the GARCH model and quantile regression with binary variables. The empirical results indicate that neither gold nor cryptocurrencies can act as strong hedges against infectious disease pandemics. However, gold, Bitcoin, and Ethereum exhibit weak safe-haven abilities during geopolitical risks. Using regression quantiles, the study finds that gold demonstrates a strong safe-haven against low and high Infectious Disease Epidemic Market Volatility (IDEMV) during extremely bearish and bullish markets. In contrast, Bitcoin and Ethereum act as strong safe havens only against low IDEMV during extreme bearish markets. Gold also shows a strong hedge propriety against extreme geopolitical events, while cryptocurrencies provide a weak hedge. Overall, gold exhibits strong safe-haven properties against low and high Geopolitical tensions, while cryptocurrencies' hedging and safe-haven abilities vary across markets. These findings convey insights for investors and guidance to supervisors on the evolution of gold, Bitcoin, and Ethereum as safe-haven and hedge instruments during both bearish and bullish markets.
This paper examines the price impact of large block trades in cryptocurrency markets by using a natural experiment in Bitcoin provided by the Gemini exchange. The exchange introduced a block trading facility in 2018, but in December 2019, it changed the minimum size threshold that allows market participants to trade a block and report it with a delay. Consistent with theoretical predictions and earlier empirical findings, we largely confirm that the information content of large trades is significantly lower in the upstairs market than in the downstairs. In contrast with prior research in traditional markets, we find that delaying the reporting of a block traded away from the continuous book discourages informed trading and potentially decreases the informativeness of trading and, therefore, information efficiency. Further, we find that the newly implemented size requirement for upstairs trades increases the total market impact, thereby not working as the intended introduction of a block trading facility.