David Korsah, Lord Mensah, Kofi A. Osei, Godfred Amewu
Purpose This study seeks to: (1) examine the extent of interconnectedness prevailing between the cryptocurrency market, the stock market and the precious metals market. (2) Conduct thorough assessment of hedge and safe-haven qualities of broad range of precious metals and cryptocurrencies against returns on the African stock market. Design/methodology/approach This study applied two novel approaches that is Cross-quantilogram, an advanced statistical technique used to examine the relationship between quantiles of response variable and the quantiles of predictor variables, and TVP-VAR, a technique that captures the dynamic connectedness of variables under consideration. Findings It was found that the three markets are highly interconnected, particularly among assets under the respective financial markets. It was further revealed that the Johannesburg Stock Exchange (JSE) was the most resilient stock market, whereas Bitcoin, BNB, Silver (XAG) and Platinum (XPT) also exhibited notable resistance to shocks. Finally, the study found that cryptocurrencies and precious metals portrayed varying hedge and safe haven qualities under the various stock markets. Practical implications The high interdependency between the African stock market, cryptocurrencies and precious metals suggests that none of the markets is immune to shocks form the other market. The finding that cryptocurrencies and precious metals exhibit some degree of safe-haven and hedge potentials, albeit limited in certain stock markets, provides investors with alternative investment options during market downturns. Since most African stock markets, except the JSE, are net receivers of shocks, investors in these markets should exercise caution during periods of global financial uncertainty. Originality/value To the best of our knowledge, this study is the first to explore the dynamic interconnectedness between seven carefully selected African stock markets, three distinct cryptocurrencies and four precious metals, while also assessing the hedge and safe-haven potential of the cryptocurrencies and precious metals against stock market returns. Additionally, the study stands out in recent literature by employing two novel approaches: the TVP-VAR model, which captures the dynamic connectedness among variables, and the Cross-Quantilogram, an advanced statistical method that analyzes the relationship between the quantiles of the response and predictor variables, all within a single study.
Seyed Alireza Athari, Derviş Kırıkkaleli, Chafic Saliba, Victoria Olushola Olanrewaju
In recent years, cryptocurrencies have emerged as a prime digital currency and an important asset, and the financial system is emerging as an important aspect while artificial intelligence (AI) has advanced expeditiously. Although AI and Bitcoin are among the most important topics in the world, empirical findings in this area are very limited. Thus, this study aims to explore co-movement between AI and Bitcoin price using quantile-based approaches from 2012 to 2024. Remarkably, the low-to-mid quantiles of AI (0.15–0.50) and the mid-to-high quantiles of BITCOIN (0.30–0.80) show a continuously positive and substantial effect from BITCOIN on AI. When AI is in its low-to-mid quantiles (0.15–0.60), it has a large and favorable impact on BITCOIN, particularly in the mid-to-upper quantiles (0.35–0.95). The results are robust by Moment Quantile Regression and Quantile-on-Quantile KRLS methods. Based on these findings policies are suggested.
This paper examines cryptocurrency shock transmission to financial markets and the macroeconomy using a Bayesian structural VAR with Pandemic Priors from 2015 to 2024. By affecting overall risk appetite, cryptocurrency price shocks generate positive financial market spillovers, accounting for 18% of equity and 27% of commodity price fluctuations. Real economic effects are significant in driving investment but remain limited, contributing only 4% to unemployment and 6% to industrial production variance. However, cryptocurrency shocks explain 18% of price-level forecast error variance at long horizons. Narrative analysis reveals sentiment and technology as primary shock drivers. These findings demonstrate cryptocurrency’s deep financial system integration with important inflation implications for monetary policy.
Bitcoin has emerged as a promising addition to long-term investment portfolios due to its lack of correlation with traditional asset classes. Spot bitcoin exchange-traded funds (ETFs) provide a secure, familiar, and convenient way to invest in bitcoin. Since their launch on 11 January 2024, they have garnered more than $75 billion in new assets under management and their performance relative to bitcoin futures and futures-based bitcoin ETFs has been nothing short of extraordinary. This study examines the performance of spot bitcoin ETFs during their first year of trading. In doing so, it highlights the complexities and inconsistencies in US regulatory decision-making.
Inflows to the newly established bitcoin exchange-traded funds (ETFs) surpassed $20 billion in the first several weeks of trading and are considered record-high by ETF standards. In this article, we provide an early examination of the bitcoin spot ETFs listed on US exchanges and their effect on bitcoin price formation. We establish several empirical facts: 1) daily capital flows to new spot bitcoin ETFs exceed $500 million or roughly 10,000 bitcoins, and surpass bitcoin mining production by the factor of 5; 2) net flows to ETFs are a strong positive predictor of bitcoin price levels with the R-squared of 95%; 3) most bitcoin price changes occur outside ETF trading hours; 4) an increase in bitcoin price leads to abnormal ETF trading volume; 5) inflows to bitcoin ETFs correlate with outflows from gold ETFs. Overall, during the period studied, capital flows to spot-bitcoin-ETFs emerge as a dominant single factor predicting bitcoin valuation effects. "IBIT is the fastest-growing ETF in the history of ETFs.
Ch. V. Raghavendran, K. Chandra Mouli, Manu Hajari, A. Anil Kumar Reddy · 6 authors
Predictive modeling has emerged as a key focus for cryptocurrency market asset valuation due to its complex nature and high market volatility. The research looks into Ethereum price forecasting with the methods of autoregressive integrated moving average (ARIMA) and Facebook Prophet model and long short‐term memory (LSTM) networks. These models operate on historical Ethereum prices and show their efficiency regarding temporal pattern recognition and prediction accuracy. The ARIMA model helps reveal trends as well as seasonal patterns and irregularities within Ethereum price fluctuations. The Facebook Prophet model serves as a forecasting tool because it automatically handles peculiarities present within cryptocurrency price data. Time series forecasting with LSTMs becomes an advanced technique used to detect intricate patterns along with sustained dependency relationships between data points. The systematic process of preparing data and constructing models and assessing results enables proper utilization of LSTMs for predicting time series data with accuracy. Ethereum price datasets are applied to train the models which undergo performance evaluation using MPE alongside MAPE and RMSE along with MAE to reveal strengths and weaknesses during Ethereum price predictions. The evaluation shows that ARIMA and Facebook Prophet together with LSTM demonstrate success in modeling Ethereum price fluctuations. This research explores the effectiveness of time series forecasting methods for cryptocurrency price prediction yielding vital knowledge about reliable tools for financial market trend modeling. Current research findings will provide knowledge to investors and risk management professionals making decisions within the volatile digital asset space.
Elie Bouri, Amin Sokhanvar, Harald Kinateder, Serhan Çiftçioğlu
• Reveals significant positive predictability in the stock market–cryptocurrency nexus. • U.S. tech and semiconductor stocks and Nvidia predict cryptocurrency returns and vice versa. • Mutual returns predictability is significant across several quantiles and lags. • It generally holds when controlling for the U.S. dollar index and treasury market. • A trading strategy based on the cross-quantilogram outperforms a benchmark strategy. This study examines the directional return predictability between the technology sector of U.S. stock market and three major cryptocurrencies (Bitcoin, Ethereum, and Dogecoin). Using daily data from August 7, 2015, to February 8, 2024, and the cross-quantilogram approach in both static and dynamic settings, the results reveal significant positive predictability in the stock market–cryptocurrency nexus. The technology sector, semiconductors subsector, and Nvidia Corporation exert predictive power over cryptocurrency returns and vice versa across several quantiles and lags. When controlling for the impact of other financial variables, namely, U.S. dollar and U.S. treasury markets, the return predictability holds, especially for the two largest cryptocurrencies, Bitcoin and Ethereum, which reflects their importance and tighter connections with the U.S. technology sector. A trading strategy based on the results of the cross-quantilograms outperforms a benchmark strategy (i.e., always long position in either stocks or cryptocurrency), which underlines the practical implications of our main findings, particularly in terms of the significant return interactions between U.S. technology/semiconductors stocks and large cryptocurrencies.
The finite sample effect on the Hurst exponent (HE) of realized volatility time series is examined using Bitcoin data. This study finds that the HE decreases as the sampling period $Δ$ increases and a simple finite sample ansatz closely fits the HE data. We obtain values of the HE as $Δ\rightarrow 0$, which are smaller than 1/2, indicating rough volatility. The relative error is found to be $1\%$ for the widely used five-minute realized volatility. Performing a multifractal analysis, we find the multifractality in the realized volatility time series, smaller than that of the price-return time series.
Nowadays, financial markets are becoming more and more complex, and new portfolios need to be built to cope with them. This paper aims to build a Markowitz model for portfolio research based on new calibrations for nine different industries. Firstly, the weights and minimum variance combinations are calculated by using valid information such as mean, standard deviation, variance, and covariance. Second, this paper aims to maximize the return of the portfolio, diversify the investment risk of the selected portfolio, and finally determine the optimal portfolio. The portfolio can be adjusted to reduce risk or increase return by adjusting the percentage of Bitcoin. This paper further explores the portfolio using Bitcoin as a variable. This paper derives the volatility and return of the least risky portfolio to be 11.04% and -0.46%, respectively, when the portfolio is calibrated without Bitcoin, and the volatility and return of its Sharpe optimal portfolio are 14.61% and 7.11%, respectively. When the portfolio contains Bitcoin, the volatility and return of its risk-minimal portfolio are 9.45% and 0.6%, respectively, and the volatility and return of its Sharpe-optimal portfolio are 16.31% and 37.35%, respectively. Ultimately, it is concluded that Bitcoin has some risk-reducing and return-enhancing effects.
Purpose The purpose of this paper is to analyse the dynamic and evolving relationship between Bitcoin mining (BTC) and climate policy uncertainty. By using the newly developed U.S. Climate Policy Uncertainty (CPU) indicator by Gavriilidis (2021) as a proxy for global climate-related transition risk, this study aims to explore the complex bidirectional causality between these two critical phenomena in climate-related finance. Further, we explore how economic and market factors influence the cryptocurrency market, focusing on the relationship between CPU and Bitcoin mining. Design/methodology/approach We employ a linear and non-linear rolling window sub-sample Granger causality approach combined with a probit model to examine the time-varying causalities between Bitcoin mining and the U.S. Climate Policy Uncertainty (CPU) indicator. This method captures asymmetric effects and dynamic interactions that are often missed by linear and static models. It also allows for the endogenous determination of key drivers in the BTC–CPU nexus, ensuring that the results are not influenced by ad-hoc assumptions but are instead grounded in the data’s inherent properties. Findings The findings indicate that Bitcoin mining is negatively impacted by climate policy uncertainty during periods of increased environmental concern, while its energy-intensive nature contributes to increasing climate policy uncertainty. In addition to market factors, such as Bitcoin halving, and alternative assets, such as green equity, five main macroeconomic factors influence these relationships: financial instability, economic policy uncertainty, rising oil prices and increasing industrial production. Furthermore, two non-linear dynamics in the relationship between climate policy uncertainty and Bitcoin (CPU-BTC nexus) are identified: the “anticipatory regulatory decline effect”, when miners boost activity ahead of expected regulatory changes, but this increase is unsustainable due to stricter regulations, compliance costs, investor scrutiny and reputational risks linked to high energy use. Originality/value This study is the first in the literature to examine the time-varying and asymmetric relationships between Bitcoin mining and climate policy uncertainty, aspects often overlooked by static causality and average-based coefficient models used in previous research. It uncovers two previously unidentified non-linear effects in the BTC-CPU nexus: the “anticipatory regulatory decline effect” and the “mining-driven regulatory surge”, and identifies major market factors macro-determinants of this nexus. The implications are substantial, aiding policymakers in formulating effective regulatory frameworks, helping investors develop more sustainable investment strategies and enabling industry stakeholders to better manage the environmental challenges facing the Bitcoin mining sector.
Zia Ur Rehman, Wing‐Keung Wong, Naveed Khan, Hassan Zada · 5 authors
In recent years, the issue of worldwide uncertainty has gained more attention in academic literature. Therefore, the current study examines how the United States (U.S.) economic policy uncertainty (EPU) affects various stock indices, commodities and cryptocurrencies. This study takes data on stock indices and commodities from February 2005 to December 2023 and data on cryptocurrency from October 2017 to December 2023. For estimations, we employ the Quantile-on-Quantile regression (QQR) approach to investigate the impact and to understand how changes in EPU affect stock indices, commodities, and cryptocurrency returns at different levels of quantiles. The findings reveal that EPU has a negative impact on the stock indices and cryptocurrencies. For stocks, high uncertainty leads to more volatility, while EPU exhibits higher volatility for cryptocurrencies, indicating sensitivity to policy changes. Similarly, commodities react differently to the U.S. EPU, while gold tends to appreciate in uncertain times. Furthermore, we employ quantile regression for robustness check, and the findings validate the outcome of QQR at various levels of quantiles from lower to higher. Moreover, the findings of this study are helpful for investors, portfolio managers, and policymakers to develop better investment strategies and effectively manage risks across different asset classes.