Motivated by the theoretical prediction of a weak link between the cryptocurrency market and stock markets, most empirical literature contends that Bitcoin is a safe hedge for the stock market. Opposing this position is the view that portrays Bitcoin as a regular, high-risk asset, such that when stock prices rise, so does Bitcoin, and vice versa. To test the validity or otherwise of this competing view, we construct a bivariate predictive model to examine the predictive power of Bitcoin uncertainty on stock returns. In contrast to the extant literature, we rely on the novel measure of uncertainty (i.e., the Bitcoin uncertainty indices, hereinafter URCY) and specify a forecasting model. The objectives of this study are to examine (i) the predictive power of UCRY indices on stock returns and (ii) the extent to which UCRY can make accurate out-of-sample forecasts. Using data for the G7 countries, among other things, our findings show that Bitcoin uncertainty indices are negative predictors of stock returns across the countries under investigation. Results also reveal that the indices are accurate and reliable predictors of stock returns in the short-to-medium term. These results are robust to accounting for structural breaks (i.e., the COVID-19 pandemic) and some macroeconomic variables. The policy implications of these results are discussed.
Chengying He, Yong Li, Tianqi Wang, Salman Ali Shah
Abstract In light of the increasing investor interest in cryptocurrencies (CR) as alternative financial assets in financial markets, we sought to examine the connection between economic policy uncertainty (EPU) and cryptocurrencies. To do so, monthly data for Bitcoin (BTC), Ethereum (ETH), and Tether (THT) from January 2021 to April 2023 were employed. We utilized quantile regression and Granger causality analysis to investigate the relationship between EPU and cryptocurrencies. The initial results of this study suggest that EPU has little effect on the cryptocurrency market in the short-term. To enhance the strength and validity of these findings, we performed separate evaluations tailored to the unique contexts of the United States and China. The results revealed that the effects of EPU were adverse and statistically insignificant for China, while the situation differed slightly for the United States. Given that the United States has the most developed economy, its policies have a significant influence globally. As a result, cryptocurrencies have the potential to serve as efficient hedging tools. Furthermore, we incorporated nonlinear autoregressive distributed lag (NARDL) analysis to assess the asymmetric impact of EPU on cryptocurrencies by adopting both short-term and long-term perspectives. The outcomes demonstrated that both Bitcoin and Ethereum can serve as hedging tools in the short-term, although this utility diminishes in the long-term. Conversely, Tether displayed a positive association with EPU in the long-term. The findings of this study hold significance for policy-makers, offering valuable insights related to structuring efficient policies. The recommendations include fostering a rational framework for active participation from various stakeholders, including investors, governmental bodies, central banks, stock exchanges, and financial institutions. This collaborative effort aims to mitigate irrational fluctuations and enhance the acceptability of cryptocurrencies. In essence, this research underscores the potential of cryptocurrencies as a secure hedge against short-term EPU. However, we caution against assuming that any single cryptocurrency can consistently serve as a dependable investment haven.
Purpose Bitcoin (BTC) is significantly correlated with global financial assets such as crude oil, gold and the US dollar. BTC and global financial assets have become more closely related, particularly since the outbreak of the COVID-19 pandemic. The purpose of this paper is to formulate BTC investment decisions with the aid of global financial assets. Design/methodology/approach This study suggests a more accurate prediction model for BTC trading by combining the dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model with the artificial neural network (ANN). The DCC-GARCH model offers significant input information, including dynamic correlation and volatility, to the ANN. To analyze the data effectively, the study divides it into two periods: before and during the COVID-19 outbreak. Each period is then further divided into a training set and a prediction set. Findings The empirical results show that BTC and gold have the highest positive correlation compared with crude oil and the USD, while BTC and the USD have a dynamic and negative correlation. More importantly, the ANN-DCC-GARCH model had a cumulative return of 318% before the outbreak of the COVID-19 pandemic and can decrease loss by 50% during the COVID-19 pandemic. Moreover, the risk-averse can turn a loss into a profit of about 20% in 2022. Originality/value The empirical analysis provides technical support and decision-making reference for investors and financial institutions to make investment decisions on BTC.
The introduction of Bitcoin as a distributed peer-to-peer digital cash in 2008 and its first recorded real transaction in 2010 served the function of a medium of exchange, transforming the financial landscape by offering a decentralized, peer-to-peer alternative to conventional monetary systems. This study investigates the intricate relationship between cryptocurrencies and monetary policy, with a particular focus on their long-term volatility dynamics. We enhance the GARCH-MIDAS (Mixed Data Sampling) through the adoption of the SB-GARCH-MIDAS (Structural Break Mixed Data Sampling) to analyze the daily returns of three prominent cryptocurrencies (Bitcoin, Binance Coin, and XRP) alongside monthly monetary policy data from the USA and South Africa with respect to potential presence of a structural break in the monetary policy, which provided us with two GARCH-MIDAS models. As of 30 June 2022, the most recent data observation for all samples are noted, although it is essential to acknowledge that the data sample time range varies due to differences in cryptocurrency data accessibility. Our research incorporates model confidence set (MCS) procedures and assesses model performance using various metrics, including AIC, BIC, MSE, and QLIKE, supplemented by comprehensive residual diagnostics. Notably, our analysis reveals that the SB-GARCH-MIDAS model outperforms others in forecasting cryptocurrency volatility. Furthermore, we uncover that, in contrast to their younger counterparts, the long-term volatility of older cryptocurrencies is sensitive to structural breaks in exogenous variables. Our study sheds light on the diversification within the cryptocurrency space, shaped by technological characteristics and temporal considerations, and provides practical insights, emphasizing the importance of incorporating monetary policy in assessing cryptocurrency volatility. The implications of our study extend to portfolio management with dynamic consideration, offering valuable insights for investors and decision-makers, which underscores the significance of considering both cryptocurrency types and the economic context of host countries.
This paper conducts an extensive analysis of Bitcoin return series, with a primary focus on three volatility metrics: historical volatility (calculated as the sample standard deviation), forecasted volatility (derived from GARCH-type models), and implied volatility (computed from the emerging Bitcoin options market). These measures of volatility serve as indicators of market expectations for conditional volatility and are compared to elucidate their differences and similarities. The central finding of this study underscores a notably high expected level of volatility, both on a daily and annual basis, across all the methodologies employed. However, it's crucial to emphasize the potential challenges stemming from suboptimal liquidity in the Bitcoin options market. These liquidity constraints may lead to discrepancies in the computed values of implied volatility, particularly in scenarios involving extreme moneyness or maturity. This analysis provides valuable insights into Bitcoin's volatility landscape, shedding light on the unique characteristics and dynamics of this cryptocurrency within the context of financial markets.
Purpose Owing to highly volatile and chaotic external events, predicting future movements of cryptocurrencies is a challenging task. This paper advances a granular hybrid predictive modeling framework for predicting the future figures of Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Stellar (XLM) and Tether (USDT) during normal and pandemic regimes. Design/methodology/approach Initially, the major temporal characteristics of the price series are examined. In the second stage, ensemble empirical mode decomposition (EEMD) and maximal overlap discrete wavelet transformation (MODWT) are used to decompose the original time series into two distinct sets of granular subseries. In the third stage, long- and short-term memory network (LSTM) and extreme gradient boosting (XGB) are applied to the decomposed subseries to estimate the initial forecasts. Lastly, sequential quadratic programming (SQP) is used to fetch the forecast by combining the initial forecasts. Findings Rigorous performance assessment and the outcome of the Diebold-Marianoâs pairwise statistical test demonstrate the efficacy of the suggested predictive framework. The framework yields commendable predictive performance during the COVID-19 pandemic timeline explicitly as well. Future trends of BTC and ETH are found to be relatively easier to predict, while USDT is relatively difficult to predict. Originality/value The robustness of the proposed framework can be leveraged for practical trading and managing investment in crypto market. Empirical properties of the temporal dynamics of chosen cryptocurrencies provide deeper insights.
Youcef Maouchi, Mohamed Fakhfekh, Lanouar Charfeddine, Ahmed Jeribi
The crypto assets market is growing rapidly, exposing investors to new risks. As a result, finding viable candidates to hedge and diversify crypto portfolios is a critical and timely topic. In this paper, we explore the potential of gold-backed cryptocurrencies as safe haven assets in the context of building a diversified digital assets portfolio. Empirically, we investigate the financial properties (diversification, safe haven, and hedging capabilities) of two gold-backed cryptocurrencies against the three main digital assets categories, i.e. traditional cryptocurrencies, Non-Fungible Tokens (NFTs), and Decentralized Finance (DeFi) tokens, considering major external and internal crises. We also estimate the hedge ratios and the hedging effectiveness of the considered pairs. Overall, our findings indicate that the examined gold-backed cryptocurrencies are good diversifiers, with varying hedging, and safe haven properties depending on the nature of the crises, such as the COVID-19 pandemic and the RussiaâUkraine War, as well as the digital asset category considered. Several financial implications for investors and policymakers are proposed and discussed.
The rapid rise of Bitcoin, a decentralized digital currency, has attracted significant attention from investors, researchers, and policymakers alike. The relationship between traditional stock prices and Bitcoin prices has garnered considerable attention in recent years. This research paper aims to explore the interconnections and dynamics between stock prices and Bitcoin prices by employing a Vector Autoregression (VAR) model. The study utilizes a comprehensive dataset spanning a specific time period, encompassing daily or monthly observations of stock prices and Bitcoin prices. The VAR model allows for the analysis of the joint behavior of these variables, capturing both short and long-term relationships, showing the effects of stocks on Bitcoin, but not the other way around. The research also underscores the necessity for continuous monitoring and analysis as the cryptocurrency landscape evolves rapidly. It highlights the significance of understanding the intricate dynamics between traditional financial markets and emerging digital assets, such as Bitcoin, in order to make informed investment decisions and mitigate potential risks.
Bitcoin, a pioneering cryptocurrency, has captivated the world with its volatility and price swings. Its price forecasts hold vital importance for investors, policymakers, and technologists. This article delves into the intricate domain of researching and predicting Bitcoin prices, grounded in diverse data exploration and stability assessment. The application of sophisticated predictive models further underscores the analysis, encompassing mathematics, statistics, and AI. Beyond financial gains, these forecasts impact regulatory decisions and technological advancements. This article converges multiple disciplines, bridging finance, technology, and data science to unveil Bitcoin's enigmatic behavior. This paper finds that the ARIMA Model can help predict the price of bitcoin. Itâs not just about predicting prices; it's about deciphering the potential of blockchain and reshaping our understanding of modern finance in an era of profound technological transformation. So investors should consider bitcoin as a long-term investment. The value of Bitcoin has historically appreciated over time, but short-term price fluctuations are common. Investors should avoid making impulsive decisions based on daily price movements. The second is to use reputable cryptocurrency exchanges and hardware wallets to securely store investors' bitcoins.
Several papers estimate the time series properties of bitcoin prices. However, to know that bubbles occur, an estimate of fundamental value is needed. Few estimates of bitcoinâs fundamental price exist, and these are either statistical in nature or based on questionable economic theory. This paper calculates the non-bubble dollar price of bitcoin using a modification to the quantity theory of money, and it is estimated to be about $54.00. At the November 2021 peak market price of bitcoin, the bubble component was more than 99%. Even at end-August 2023 after bitcoin prices had more than halved, the bubble component was still more than 99%.
Purpose : It can be stated that in todayâs competitive conditions, where portfolio management is very important, it has become necessary to examine the relationship between global financial assets and major cryptocurrencies, such as Bitcoin and Ethereum. This paper aims to investigate the cointegration and causalityrelationships between Bitcoin, Ethereum, and global financial assets such as gold, oil, the S&P Global 100, the Dow Jones Commodity, and the US Dollar Indices, and to determine the diversification role of Bitcoin and Ethereum comparatively for the period between April 2016 and January 2024. Methodology: The ADF Unit Root, Johansen Cointegration, Granger Causality, Rolling Window Causality tests, and Variance Decomposition Analysis methods were used in the analysis process. Results: Based on the findings obtained from the paper, it was determined that Bitcoin and Ethereum have no cointegration with selected financial asset classes. Granger causality analysis results indicated that there were unidirectional causalities from Bitcoin and Ethereum prices to Dow Jones Commodity Index prices. In addition to the results of the Rolling Window causality tests, it was also determined that there are some causalities between Bitcoin, Ethereum, and other variables, especially after the 2021-2022 period. Conclusion: It can be concluded that Bitcoin and Ethereum are effective portfolio diversifiers throughout the entire period; however, the diversification effects of Bitcoin and Ethereum weakened towards the end of the review period. Therefore, it can be said that Bitcoin and Ethereum act similarly in the global investment portfolio.
Bitcoin, a decentralized digital currency, has gained widespread acceptance and recognition in recent years. The prediction of Bitcoin prices is a challenging task due to its relatively young age and high volatility. Therefore, this study explores the accuracy of price prediction for Bitcoin using machine learning models and makes comparsion on the outcome of different models, Linear Regression, Long Short-Term Memory, and Recurrent Neural Network. This study utilizes the closing price of Bitcoin in USD from a Kaggle dataset as the independent variable. The study also adopts Mean Absolute Error (MAE) as the measurement indicators, and comparative performance analysis is conducted under various circumstances. The experimental results demonstrate that LR performs poorly in Bitcoin price prediction, while LSTM and RNN outperform LR. Further analysis reveals that LSTM performs better during price apexes, while RNN performs better during price recessions. Graphical representations illustrate the strengths and weaknesses of each model under different market scenarios. Through comparison, the article provides an insight for other researchers to choose corresponding machine learning models under different circumstances to predict bitcoin price.