This study explores whether Islamic equities offer portfolio diversification benefits to cryptocurrency investors. It employs the Continuous Wavelet Transform model to examine the nature of coherence between major cryptocurrency asset classes and major Asian Islamic equity markets on different investment horizons. We consider a range of Islamic equity indices for multiple countries and a basket of three prominent cryptocurrencies: Bitcoin, Ethereum and Ripple. Findings suggest that Asian Islamic equities offer portfolio diversification opportunities. Our findings also imply that Asian Islamic equities are not efficient and are prone to short-term speculative activities.
Mohammad Ashraful Ferdous Chowdhury, Mohammad Abdullah, Mansur Masih
This paper makes an initial attempt to investigate the risk spillover of the Russia-Ukraine war and oil price on Asian Islamic Stocks and bitcoin. We apply quantile-based connectedness measures using daily return data covering four Asian Islamic stock indicesâoil, gold, bitcoin, and war panicâfrom February 1, 2022, to July 15, 2022. The results indicate higher connectedness in the upper and lower quantiles compared to the middle quantile, which implies that return shocks react more sharply during high war panic.
Yeguang Chi, Wenyan Hao, Jiangdong Hu, Zhenkai Ran
Abstract We investigate the crossâsection assetâpricing patterns of major cryptocurrencies from 2017 to 2021. We show that the basis, momentum, and basisâmomentum factors earn statistically significant excess returns, a result consistent with the findings reported in the commodity futures literature. The basis is the strongest signal predicting crossâsectional differences in cryptocurrency futures returns; the momentumâinduced risk premium is not statistically powerful, whereas the basis momentumâinduced risk premium disappears when accounting for the basisâinduced risk premium. Daily factor returns are statistically much stronger than weekly factor returns. Monthly factor returns are nonsignificant.
Abstract The purpose of the study is to examine higher moment connectedness among 12 cryptocurrencies using data sampled at the 1-minute high-frequency interval. We use methods that demonstrate the heterogeneity of agents from their distinct investing horizons. This includes wavelet multiple cross-correlations, CEEMDAN-based Diebold-Yilmaz (DY) connectedness index and the Barunik-Krehlik (BK) frequency connectedness index. First, our results show that higher moment multiple correlations among the sampled cryptocurrencies are higher at all time scales and the relationship strengthens at lower frequencies. Second, the wavelet cross-correlations show different cryptocurrencies with the potential to lead and lag in the transmission of higher moment shocks to the whole system at different frequencies. Again, the multiple wavelet cross-correlations increase with increasing time scales. The results from the CEEMDAN-based DY connectedness index as well as the BK framework also reveal cyclical connectedness and differences in connectedness across different frequencies. The results show more connectedness of higher moments than the connectedness empirically reported for returns and volatility. Cryptocurrency connectedness has mostly been examined using the first two moments. We extend this line of literature by examining the third and fourth moments, which might be more useful for risk management purposes.
<p><big>In this study, we examined the efficiency of cryptocurrencies Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Ripple (XRP), DASH, EOS, and MONERO from March 1, 2018, to March 1, 2023. We separated the sample into four subperiods for this purpose: a Tranquil period that includes the period from March 1, 2018, to December 31, 2019; a First Wave that includes the year 2020; a Second Wave that includes the year 2021; and a fourth subperiod that includes Russia&#39;s invasion of Ukraine in 2022-2023. The results are mixed, with some cryptocurrencies exhibiting equilibrium and others exhibiting autocorrelation and predictability in their pricing. When the sample is divided into subperiods, most digital currencies have long memories in their returns during the Tranquil period, BTC, LTC, and XRP exhibit efficiency during the First Wave of the pandemic, while BTC, ETH, and MONERO indicate efficiency during the Second Wave. Most assessed digital currencies showed equilibrium by 2022, with the exception of ETH and MONERO, which exhibit long memories, and LTC, which demonstrates anti-persistence. These results hold significance for investors in these alternative markets, as they suggest that some cryptocurrencies may be more predictable and therefore potentially profitable, whereas others may require greater caution and risk management strategies.</big></p>
Crude oil, Bitcoin, and carbon dioxide emissions are major issues that are significantly impacting the global economy and environment. These three issues are complexly interlinked, with profound economic and environmental implications. In this study, we explore the correlation among these three issues and attempt to understand the influence of crude oil and Bitcoin on carbon dioxide emissions. We created a novel approach, named quantile mediation analysis, which blends mediation regression with quantile regression, enabling us to explore the influence of Brent crude oil on carbon dioxide emissions by considering the mediating impact of Bitcoin. According to the findings from using our new approach, the impact of Brent crude oil on carbon dioxide emissions is partly mediated by Bitcoin, and the association between Brent crude oil and carbon dioxide emissions involves both direct and indirect effects. Since the carbon dioxide generated by the extraction of crude oil and Bitcoin has a great impact on the environment, accelerating the use of clean energy technologies to reduce our reliance on crude oil should be the direction that the cryptocurrency industry ought to pursue in the future.
Purpose As cryptocurrencies continue to gain viability as an asset class, institutional investors and publicly traded firms have started taking investment positions in digital currencies. What firms may not be considering, however, is the effect these assets may have on their risk profiles. This study aims to (1) measure the effect of cryptocurrencies on the risk and return characteristics of publicly traded companies; (2) decipher the motives behind holding cryptocurrencies as an asset class; and (3) determine whether one reason for holding is more effective than another. To conduct this research, the four largest publicly traded holders of cryptocurrency as well as four of the most prominent cryptocurrencies are explored. Design/methodology/approach The cross-sectional analysis approach has been used to analyze the daily returns, volatility, betas and Sharpe Ratios of firms during periods without cryptocurrency strategies and during periods with cryptocurrency strategies. Findings The impact of the cryptocurrency asset class on common stock performance and corporate disclosures are documented. The importance of risk disclosures on cryptocurrency holdings is emphasized: Firms must better inform their stakeholders through comprehensive disclosures in financial statements. Firms utilize cryptocurrencies for various reasons such as treasury management tools or as direct sources of income. Consequently, the impact on returns and risks varies substantially. Originality/value To the best of the authorsâ knowledge, this is one of the first studies on cryptocurrency investments in the treasury departments of publicly traded companies. The study contributes to the literature by extracting relevant information regarding company risk reporting and cryptocurrency risk at firms. The conclusions also promote firm transparency with detailed reporting of cryptocurrency holding risks.
Abstract We test the hedge property of nonâfungible token (NFT) coins against equity market fluctuations and compare it with the hedge property of Bitcoin. We employ daily the returns of Bitcoin; three NFT coins, namely Theta, Enjin Coin and Decentraland, and three equity market indices: S&P 500, NASDAQ and CAC 40, ranging from 18 January 2018 to 12 January 2021. We estimate the hedge effectiveness of the three NFT coins and Bitcoin against stock market fluctuations. Our results suggest that NFT coins are a better hedge against equity market fluctuations than Bitcoin.
Abstract This paper studies timeâfrequency connectedness among carbon assets, Bitcoin, and global stock markets by using the Diebold and Yilmaz method and the BarunĂk and KĆehlĂk method, to investigate the hedging ability of carbon assets and Bitcoin in global stock markets. Our study finds that both carbon assets and Bitcoin play hedging roles in global stock markets. However, their strength of hedging is negatively correlated with the degree of economic uncertainty and tends to change in different frequency domains. We also show that carbon assets and Bitcoin can act as each other's hedging assets in a great majority of cases. Our results provide useful knowledge for investors to reduce risks and for regulators to regulate carbon assets and cryptocurrency speculation.
Investors are looking for objects in which they invest their funds successfully, evaluating the effectiveness of alternative markets and their instruments. Historically, cash flow indicators most effectively reflected the mood of the masses in relation to any financial asset, both in the short-and long-term. This article examines in detail the queue of already completed, but not confirmed transactions in the bitcoin network. The mempool is able to timely display the growth in the number of transactions awaiting confirmation, which makes it a leading indicator of future cash flows that could affect the trading volumes and market prices of bitcoin. This study evaluates bitcoin mempool priorities and two different analyses have been conducted for this purpose. Firstly, the mempool periods are examined through a statistical analysis. Secondly, the performance determinants of mempool are assessed with q-ROF Multi-SWARA. In addition to q-ROF sets, weights are computed with IFS and PFS. Demonstrated here is that the results of all fuzzy sets are identical. This outcome explains the reliability of the findings and they indicate that a transaction is the most important determinant of the bitcoin mempool. It emerged that the adjusted mempool data (+16.7%) for 7-day and 30-day moving averages was able, with a time lag of 24â48 h, to indicate significant volatility of future bitcoin trading volumes (+1.6%) on average. The obtained values confirm the empirical conclusion reached here that the mempool growth leads to cash flow growth. An increase in future cash flows results in a substantial rise in future trading volumes. The key takeaway from the analysis is that mempool is able to effectively predict future increases in trading volumes based on the prior cash flow growth projected into mempool growth. However, as a price indicator, mempool does show mixed results with mostly uncertainty in the direction of price movement.
Abstract This paper examines the impact of market related events and investor base on the spread of Bitcoin prices between two exchange platforms, Coinbase and Binance. Based on highâfrequency data samples collected from 2019 to 2021, we show how investors from different bases react differently to market related events, which create the price spreads between exchange platforms. We also identify the arbitrage opportunities these spreads create and establish arbitrage strategies for all identified events to exploit the variations in Bitcoin prices traded on both platforms. Findings indicate arbitrage offers profits that are higher overall than holding Bitcoin on either platform.
In this article, we discuss the central banksâ attitude to cryptocurrencies and focus more on European Central Bank. First, based on the analysis of scientific literature, we show that cryptocurrency is money and performs all of the functions of money, such as the exchange medium, value storage, and accounts unit. We found a positive correlation between the level of economic development of a country and the level of regulation and the integration of crypto cryptocurrencies into the economic system. We discuss not only the approach of the ECB to cryptocurrencies, but also how it developed. According to the study data, the ECB only began to respond to cryptocurrency as an equivalent monetary instrument in 2021, established regulatory mechanisms, and developed the digital euro project.
Abstract This study introduces a novel pairs trading strategy based on copulas for cointegrated pairs of cryptocurrencies. To identify the most suitable pairs and generate trading signals formulated from a reference asset for analyzing the mispricing index, the study employs linear and nonlinear cointegration tests, a correlation coefficient measure, and fits different copula families, respectively. The strategyâs performance is then evaluated by conducting back-testing for various triggers of opening positions, assessing its returns and risks. The findings indicate that the proposed method outperforms previously examined trading strategies of pairs based on cointegration or copulas in terms of profitability and risk-adjusted returns.
In this paper we predict Bitcoin movements by utilizing a machine-learning framework. We compile a dataset of 24 potential explanatory variables that are often employed in the finance literature. Using daily data from 2nd of December 2014 to July 8th 2019, we build forecasting models that utilize past Bitcoin values, other cryptocurrencies, exchange rates and other macroeconomic variables. Our empirical results suggest that the traditional logistic regression model outperforms the linear support vector machine and the random forest algorithm, reaching an accuracy of 66%. Moreover, based on the results, we provide evidence that points to the rejection of weak form efficiency in the Bitcoin market.
Abstract Anomalies, which are incompatible with the efficient market hypothesis and mean a deviation from normality, have attracted the attention of both financial investors and researchers. A salient research topic is the existence of anomalies in cryptocurrencies, which have a different financial structure from that of traditional financial markets. This study expands the literature by focusing on artificial neural networks to compare different currencies of the cryptocurrency market, which is hard to predict. It aims to investigate the existence of the day-of-the-week anomaly in cryptocurrencies with feedforward artificial neural networks as an alternative to traditional methods. An artificial neural network is an effective approach that can model the nonlinear and complex behavior of cryptocurrencies. On October 6, 2021, Bitcoin (BTC), Ethereum (ETH), and Cardano (ADA), which are the top three cryptocurrencies in terms of market value, were selected for this study. The data for the analysis, consisting of the daily closing prices for BTC, ETH, and ADA, were obtained from the Coinmarket.com website from January 1, 2018 to May 31, 2022. The effectiveness of the established models was tested with mean squared error, root mean squared error, mean absolute error, and Theilâs U1, and $${R}_{OOS}^{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mi>R</mml:mi> <mml:mrow> <mml:mi>OOS</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msubsup> </mml:math> was used for out-of-sample. The DieboldâMariano test was used to statistically reveal the difference between the out-of-sample prediction accuracies of the models. When the models created with feedforward artificial neural networks are examined, the existence of the day-of-the-week anomaly is established for BTC, but no day-of-the-week anomaly for ETH and ADA was found.
Gas is the transaction-fee metering system of the Ethereum network. Users of the network are required to select a gas price for submission with their transaction, creating a risk of overpaying or delayed/unprocessed transactions involved in this selection. In this work, we investigate data in the aftermath of the London Hard Fork and shed insight into the transaction dynamics of the network after this major fork. As such, this paper provides an update on work previous to 2019 on the link between EthUSD/BitUSD and gas price. For forecasting, we compare a novel combination of machine learning methods such as Direct-Recursive Hybrid LSTM, CNN-LSTM, and Attention-LSTM. These are combined with wavelet threshold denoising and matrix profile data processing toward the forecasting of block minimum gas price, on a 5-min timescale, over multiple lookaheads. As the first application of the matrix profile being applied to gas price data and forecasting that we are aware of, this study demonstrates that matrix profile data can enhance attention-based models; however, given the hardware constraints, hybrid models outperformed attention and CNN-LSTM models. The wavelet coherence of inputs demonstrates correlation in multiple variables on a 1-day timescale, which is a deviation of base free from gas price. A Direct-Recursive Hybrid LSTM strategy is found to outperform other models, with an average RMSE of 26.08 and R2 of 0.54 over a 50-min lookahead window compared to an RMSE of 26.78 and R2 of 0.452 in the best-performing attention model. Hybrid models are shown to have favorable performance up to a 20-min lookahead with performance being comparable to attention models when forecasting 25â50-min ahead. Forecasts over a range of lookaheads allow users to make an informed decision on gas price selection and the optimal window to submit their transaction in without fear of their transaction being rejected. This, in turn, gives more detailed insight into gas price dynamics than existing recommenders, oracles and forecasting approaches, which provide simple heuristics or limited lookahead horizons.
Abstract This study examines the connectedness in high-order moments between cryptocurrency, major stock (U.S., U.K., Eurozone, and Japan), and commodity (gold and oil) markets. Using intraday data from 2020 to 2022 and the time and frequency connectedness models of Diebold and Yilmaz (Int J Forecast 28(1):57â66, 2012) and BarunĂk and KĆehlĂk (J Financ Econom 16(2):271â296, 2018), we investigate spillovers among the markets in realized volatility, the jump component of realized volatility, realized skewness, and realized kurtosis. These higher-order moments allow us to identify the unique characteristics of financial returns, such as asymmetry and fat tails, thereby capturing various market risks such as downside risk and tail risk. Our results show that the cryptocurrency, stock, and commodity markets are highly connected in terms of volatility and in the jump component of volatility, while their connectedness in skewness and kurtosis is smaller. Moreover, jump and volatility connectedness are more persistent than that of skewness and kurtosis connectedness. Our rolling-window analysis of the connectedness models shows that connectedness varies over time across all moments, and tends to increase during periods of high uncertainty. Finally, we show the potential of gold and oil as hedging and safe-haven investments for other markets given that they are the least connected to other markets across all moments and investment horizons. Our findings provide useful information for designing effective portfolio management and cryptocurrency regulations.
Walid Mensi, Mariya Gubareva, Hee-Un Ko, Xuan Vinh Vo · 5 authors
This study investigates tail dependence among five major cryptocurrencies, namely Bitcoin, Ethereum, Litecoin, Ripple, and Bitcoin Cash, and uncertainties in the gold, oil, and equity markets. Using the cross-quantilogram method and quantile connectedness approach, we identify cross-quantile interdependence between the analyzed variables. Our results show that the spillover between cryptocurrencies and volatility indices for the major traditional markets varies substantially across quantiles, implying that diversification benefits for these assets may differ widely across normal and extreme market conditions. Under normal market conditions, the total connectedness index is moderate and falls below the elevated values observed under bearish and bullish market conditions. Moreover, we show that under all market conditions, cryptocurrencies have a leadership influence over the volatility indices. Our results have important policy implications for enhancing financial stability and deliver valuable insights for deploying volatility-based financial instruments that can potentially provide cryptocurrency investors with suitable hedges, as we show that cryptocurrency and volatility markets are insignificantly (weakly) connected under normal (extreme) market conditions.
Investigating the essential impact of the cryptocurrency market on carbon emissions is significant for the U.S. to realize carbon neutrality. This exploration employs low-frequency vector auto-regression (LF-VAR) and mixed-frequency VAR (MF-VAR) models to capture the complicated interrelationship between cryptocurrency policy uncertainty (CPU) and carbon emission (CE) and to answer the question of whether cryptocurrency policy uncertainty could facilitate U.S. carbon neutrality. By comparison, the MF-VAR model possesses a higher explanatory power than the LF-VAR model; the formerâs impulse response indicates a negative CPU effect on CE, suggesting that cryptocurrency policy uncertainty is a promoter for the U.S. to realize the goal of carbon neutrality. In turn, CE positively impacts CPU, revealing that mass carbon emissions would raise public and national concerns about the environmental damages caused by cryptocurrency transactions and mining. Furthermore, CPU also has a mediation effect on CE; that is, CPU could affect CE through the oil price (OP). In the context of a more uncertain cryptocurrency market, valuable insights for the U.S. could be offered to realize carbon neutrality by reducing the traditional energy consumption and carbon emissions of cryptocurrency trading and mining.
Despite the growing interest in Blockchain Innovation (BI), there is a lack of research on its predictors. This study draws on the policy uncertainty literature to hypothesize the positive influence of economic policy uncertainty (EPU) and cryptocurrency policy uncertainty (UCRY Policy) on country-level BI, determined by the total number of blockchain patents in a country. We tested our hypotheses using a two-level sample of 126 quarterly observations nested in five countries: Australia, China, Japan, Korea, and the United States. The results confirm our expectation that the EPU and UCRY Policy lead to an enhanced BI. Moreover, we found that the UCRY Policy is more impactful on BI than EPU, and that when examining the two policy uncertainty indicators simultaneously, the effect of EPU on BI becomes insignificant. This study has important implications for policymakers and investors.
Maruf Yakubu Ahmed, Samuel Asumadu Sarkodie, Thomas Leirvik
We examine the relationship between the top five cryptos and the U.S. S&P500 index from January 2018 to December 2021. We use the novel General-to-specific Vector Autoregression (GETS VAR) and traditional Vector Autoregression (VAR) model to analyze the short- and long-run, cumulative impulse-response, and Granger causality test between S&P500 returns and the returns of Bitcoin, Ethereum, Ripple, Binance and Tether. Additionally, we used the Diebold and Yilmaz (DY) spillover index of variance decomposition to validate our findings. Evidence from the analysis suggests positive short- and long-run effects of historical S&P500 returns on Bitcoin, Ethereum, Ripple, and Tether returns--and negative short- and long-run effects of the historical returns of Bitcoin, Ethereum, Ripple, Binance, and Tether on S&P500 returns. Alternatively, evidence suggests a negative short- and long-run effect of historical S&P500 returns on Binance returns. The cumulative test of impulse-response indicates a shock in historical S&P500 returns stimulates a positive response from cryptocurrency returns while a shock in historical crypto returns triggers a negative response from S&P500 returns. Empirical evidence of bi-directional causality between S&P500 returns and crypto returns suggest the mutual coupling of these market. Although, S&P500 returns have high-intensity spillover effects on crypto returns than crypto returns have on S&P500. This contradicts the fundamental attribute of cryptocurrencies for hedging and diversification of assets to reduce risk exposure. Our findings demonstrate the need to monitor and implement appropriate regulatory policies in the crypto market to mitigate the potential risks of financial contagion.