This paper examines the dynamic linkages of green bond with the energy and crypto market. The S&P green bond index (RSPGB) is used as a proxy for the green bond market; S&P global clean energy index and ISE global wind energy (RIGW) are used as proxies for the renewable energy market, and; Bitcoin and Ethereum (RETHER) are used as the proxies of the crypto market. The daily prices of these constituent series are collected using Bloomberg from October 3, 2016 to February 23, 2021. We undertake an empirical analysis through the application of three key tests, namely: dynamic conditional correlation (DCC), Diebold and Yilmaz (Int J Forecast 28(1):57-66, 2012. 10.1016/j.ijforecast.2011.02.006), BarunĂk and KĹehlĂk (J Financ Econom 16(2):271-296, 2018. 10.1093/jjfinec/nby001) model. The DCC reveals no dynamic linkages of volatility from the green bond to the energy and crypto market in the short run. Referring to Diebold and Yilmaz (2012), it dictates that the green bond (RSPGB) is a net receiver while the energy market (RIGW) and cryptocurrency (RETHER) are the largest and least contributors to the transmission of the volatility. Additionally, the BarunĂk and KĹehlĂk (2018) model confirmed that the magnitude of the total spillover is high in more prolonged than shorter periods, suggesting reduced diversification opportunities. Overall, the present study exemplifies the significance of the green bond market as protection against risk.
This study investigates mean and volatility spillover effects among eight major cryptocurrencies; Bitcoin, Ethereum, Litecoin, Ripple, Stellar, Bitcoin Cash, Cardano and EOS utilizing VAR-BEKK-GARCH model. The results point out that there are bidirectional and unidirectional spillover effects among these major cryptocurrencies. Moreover, the findings indicate that some cryptocurrencies are the transmitter, while others act as a receiver and among all, Litecoin is the highest transmitter, and Stellar is the only one that acts as a receiver. The interdependence among cryptocurrencies supports that they are becoming more integrated and thereby, provides important investment strategies for investors and policy implications for regulators.
Adedeji Daniel Gbadebo, Joseph Olorunfemi Akande, Ahmed Oluwatobi Adekunle
Purpose: A major challenge traders, speculators and investors are grappling with is how to accurately forecast Bitcoin price in the cryptocurrency market. This study is aimed to uncover the best model for the forecasts of Bitcoin price as well as to verify the price series that offers the best predictions performance under different periodicity of datasets. Design/methodology/approach: The study adopts three different data periods to verify whether frequency matters in forecasting Bitcoin price. The Bitcoin price, from 01/01/15 to 11/01/2021, is trained and validated on selected forecast models, including the NaĂŻve, Linear, Exponential Smoothing Model, ARIMA, Neural Network, STL and Holt-Winters filters. Five forecast accuracy measures (RSME, MAE, MPE, MAPE and MASE) are applied to confirm the best performing model. The DieboldâMariano test is used to compare the forecasts based on the daily price with those based on the weekly and monthly. Findings: Based on the accuracy measures, the results indicate that the NaĂŻve model provides more accurate performance for the daily series, while the linear model outperforms others for the weekly and monthly series. Using the DieboldâMariano statistics, there is evidence that forecasting Bitcoin price is not sensitive to the data periodicity. Research limitations/implications: The study has a major limitation, which is the shared sentiment to apply actual Bitcoin price series, and not the returns or log transformation for the forecast models. Notably, actual data may sometimes be loud, hence increasing the possibility of over predictions. Originality/value: In forecasting, different approaches have been used, this paper compares outputs of both statistical and machine learning methods in order to arrive at the best option for the Bitcoin price forecasts. Hence, we investigate whether the machine learning tools offer better forecasts in terms of lower error and higher modelâs accuracy relative to the traditional models.
Manoel Fernando Alonso Gadi, MiguelâĂngel Sicilia
Cryptocurrency markets have experienced large growth in recent years, with an increase in the number and diversity of traded assets. Previous work has addressed the economic properties of Bitcoin with regards to its hedging or diversification properties. However, the surge of many alternatives, applications, and decentralized finance services on a variety of blockchain networks requires a re-examination of those properties, including indexes from outside the big economies and the inclusion of a variety of cryptocurrencies. In this paper, we report the results of studying the most representative cryptocurrency of each consensus mechanism by trading volume, forming a list of twenty-four cryptocurrencies from the 1st of January 2018 to the 30th of September 2022. Using the Baur and McDermott model, we examine hedge, safe haven, and diversifier properties of all assets for all G7 countryâs major indexes as well as all BRICS major indexes breaking it down by two attributes: kind of blockchain technology and pre/during COVID health crisis. Results show that both attributes play an important role in the hedge, safe haven, and diversifier properties associated with the asset. Concretely: stablecoins appear to be the only ones to maintain hedge property in most analyzed markets pre- and during-COVID; Bitcoin investment properties shifted after the COVID crisis started; China and Russia stopped being correlated with the cryptocurrency after the COVID crisis hit.
The COVID-19 pandemic poses a serious threat to investors in the crude oil market. Furthermore, investors have an increasing need to find a safe haven in their investment portfolios when facing unprecedented risks in crude oil markets during the COVID-19 pandemic. According to a review of the literature, there are contradictory findings on which investment is the safer haven for the oil market. Therefore, this paper aims to evaluate whether bitcoin is a safer haven for the crude oil market than the commonly used gold during the COVID-19 pandemic. Three spillover measurements based on the time, and frequency domains, and a network framework are employed to quantify the return spillover effects among bitcoin, gold and three major crude oil futures markets. We divide the sample into two periods, pre-COVID-19 and post-COVID-19. The results show that bitcoin has a weak safe-haven effect on the crude oil market only over a short period, while gold maintains a good safe-haven ability for crude oil futures across various time horizons (frequencies), both before and after the outbreak of the COVID-19 pandemic. The findings of this study have important implications for policy-makers, crude oil producers and global investors. In particularly, investors cannot ignore the importance of bitcoin and gold in selecting more profitable portfolio policies when searching for safe-haven assets.
Blanka ĹÄt, Konrad SobaĹski, Wojciech Ĺwider, Katarzyna WĹosik
Abstract This article sheds new light on the informational efficiency of the cryptocurrency market by analyzing investment strategies based on structural factors related to on-chain data. The study aims to verify whether investors in the cryptocurrency market can outperform passive investment strategies by applying active strategies based on selected fundamental factors. The research uses daily data from 2015 to 2022 for the two major cryptocurrencies: Bitcoin (BTC) and Ethereum (ETH). The study applies statistical tests for differences. The findings indicate informational inefficiency of the BTC and ETH markets. They seem consistent over time and are confirmed during the COVID-19 pandemic. The research shows that the net unrealized profit/loss and percent of addresses in profit indicators are useful in designing active investment strategies in the cryptocurrency market. The factor-based strategies perform consistently better in terms of mean/median returns and Sharpe ratio than the passive âbuy-and-holdâ strategy. Moreover, the rate of success is close to 100%.
This study analyses and compares the behavior of the gold-backed, conventional cryptocurrency, and gold markets capable of detecting the existence of herding and deducing the efficiency degree. In addition, this empirical work tried to examine the COVID-19 pandemic's influence on both cryptocurrency performances. This work developed a new method that discloses herding biases using persistence and efficiency metrics. Besides, this paper investigated the nonlinear dynamic properties of the gold-backed, conventional cryptocurrencies and Gold by estimating the Multifractal Detrended Fluctuation Analysis (MFDFA). It also assessed the inefficiency of these markets through an efficiency index (IEI) and tested the effect of COVID-19 on their dynamics. The findings of this investigation indicate that the gold-backed cryptocurrency (X8X) is the most efficient market in the long-term trading market. However, the conventional cryptocurrency market (Bitcoin) is the most efficient on the short trade horizon. Besides, gold-backed cryptocurrency markets present a smaller level of herding behavior than conventional cryptocurrencies on tall scales. Nevertheless, we noted the positive and negative effects of the pandemic on each cryptocurrency market dynamics. To the best of the authors' knowledge, this study is the first investigation that uses multifractal analysis to quantify the impact of the COVID-19 spread on gold-backed cryptocurrencies and detects the presence of herding behavior.
This study examines how the COVID-19 pandemic crisis affects the interactions between the stock, oil, gold, currency, and cryptocurrency markets. The impacts of the COVID-19 pandemic crisis on the optimal asset allocation and optimal hedged strategy are also discussed. Empirical results show that the volatility spillover significantly exists in most of the ten paired markets whereas the return spillover and correlation are significant only for the few paired markets. Moreover, the impact of the COVID-19 pandemic on the return spillover is the greatest followed by the correlation whereas the volatility spillover is not affected by the COVID-19 pandemic. Furthermore, the Quantitative easing (QE) implemented after the COVID-19 pandemic crisis increases the risk-adjusted return for each asset and minimum variance portfolio (MVP) and raises the correlation between two assets. In addition, most of the pairs of assets are not suitable to hedge each other except for a few pairs of assets. Regarding these few pairs of assets, the optimal hedge asset with the fewer hedge cost is accompanied by less risk reduction and vice versa. Finally, the investors should choose the euro to construct a portfolio to achieve risk diversification and to hedge gold or WTI to get the risk reduction. The above findings can help investors and fund managers make a useful investment strategy, optimal asset allocation, and effective hedged strategy. For example, the investors can use the volatility of one market to predict the volatility of another market and they can take a long position during the post-COVID-19 period but they should withdraw capital from the market when the QE tapering is executed. JEL classification: C52; C53; G15.
The development of hedging strategies using commodity and cryptocurrency has been a topic of academic and practical interest. An optimal strategy increases the efficiency of risk management and minimizes the costs of hedging. This paper examines time-varying optimal hedging ratios for the ASEAN-5 stock market, hedged with gold and bitcoin. The best hedging instrument was determined using regression and DCC-GARCH model. The analyses resulted in hedge effectiveness criteria. The daily data covered the period from January 1, 2019 to December 31, 2021. The findings were robust to the distribution assumption and to the use of DCC-GARCH model in examining different refit. Finally, this study provides an invaluable starting point to examine the dynamic hedging.
Blockchain technology and its applications in various business domains have attracted great attention from researchers and practitioners in recent years. Finance, which is arguably the most promising and well-known application domain, has been significantly transformed into digital finance by various novel and open technological and business innovations rooted in blockchain technology, such as decentralized finance and cryptocurrency. Digital finance innovations like digital payments, crowdfunding, supply chain finance, and robo-advising have made significant progress. The main goal of this special issue is to deepen and broaden our understanding of the impacts, values, and challenges brought by blockchain technology and digital finance.
The existing studies rarely reveal the reasons for the digital currency price fluctuation from the perspective of internal interaction and contagion. Therefore, to fill this research gap, this paper comprehensively adopts the dynamic conditional correlation (DCC-) GARCH model and wavelet coherence analysis (WTC) to reveal the internal correlation and formation reasons of digital currency price fluctuations. Our research has the following findings: (1) the price fluctuations of digital currency are highly related. Through the observation of the dynamic conditional correlation coefficient graph, it is found that the price fluctuations have a strong time-varying trend, manifested as a âcontagiousâ characteristic. (2) During the outbreak of COVID-19, most digital currencies have shown positive resonance in the short, medium, and long term, suggesting that the COVID-19 pandemic has increased the correlation and contagion of digital currency price fluctuations. (3) In the short term, Bitcoin is the main âcontagious sourceâ of digital currency price fluctuation. But in the medium and long term, Ethereum and Ripple, which are closely related to the real economy, have a greater impact and become the new âcontagious sourceâ. Generally speaking, Bitcoin, Ethereum, and Ripple are the internal causes of instability in the digital currency market. Finally, based on the empirical conclusion, this paper proposes that the digital currency portfolio should be optimized to meet the investment demand; strengthen digital currency regulatory cooperation, and improve regulatory efficiency. Let the digital currency return to the âcurrencyâ attribute and serve the real economy.
In 2022, with the implementation of tightening monetary policies by FOMC, US dollar is experiencing a dramatic appreciation in a very short period. Though numerous studies have demonstrated the connection between the traditional currency market, cryptocurrency market, and precious metal market, rare studies are exploring the relationships between the three markets under a special political environment. This paper selects USDCNY exchange rate, gold and silver, and bitcoin as the representatives of three markets and then tests the volatility response of return on gold & silver and return on bitcoin to the change of return on USDCNY exchange rate. By employing impulse response function and ARMA-GARCHX model, the paper verifies the change of exchange rate will exacerbate the volatility of returns on gold & silver and bitcoin significantly, which suggests high risk and uncertainty of the cryptocurrency market and precious metal market in a complex and extreme political environment. Investors and speculators should take prudent investment strategies in such environment.
Cryptocurrency is the most secure, traceable, and reliable intangible currency because it uses blockchain technology. It eliminates the middle layer of financial institutes in the traditional economic system. Because of high returns in cryptocurrencies, investors and other firms invest a lot of money. But the prices of the cryptocurrencies are uncertain. Prices of cryptocurrencies are influenced by many factors like sentiments, trading volume, and similar. Researchers are doing plenty of work to predict the accurate prices of various cryptocurrencies. However, many of these methods cannot be used in real-time. Several deep learning models such as Neural networks (NN), Long short-term memory (LSTM), and Gated recurrent unit (GRU) have been utilized by researchers for predicting the price of cryptocurrencies and yet, are unable to achieve significant results. This work combines LSTM and GRU with sentiment analysis to precisely estimate bitcoin values. We have used Root means square error (RMSE) to evaluate the model performance with and without sentiments. Empirically, we have compared the results with the other state-of-the-art models and found better results using the proposed hybrid model incorporated with sentiments.
The aim of this research is to investigate the causality between Global Economic Political Uncertainty (GEPU) and Geopolitical Risk (GPRT) and Bitcoin Energy Consumption (BTCE). In order to test the stationarity of the variables, the Lee-Strazich unit root test, which takes into account the structural breaks, was used, and the causality relationship between the variables was analyzed with the Hatemi-J (2012) causality test. Monthly data between May 2011 and February 2022 were used in the research. According to the results obtained from the research, geopolitical risk and global economic policy uncertainity are effective on bitcoin energy consumption. In addition, it has been determined that the negative effects of geopolitical risk and global uncertainties are more dominant. The results show that the demand for bitcoin, which is considered an alternative financial asset class, and accordingly bitcoin energy consumption, increases in case of global risks and economic uncertainties.
Ămit Cali, Murat Kuzlu, D. Jonathan Sebastian-Cardenas, Onur Elma ¡ 6 authors
Decarbonization of energy systems has been a recent trend during the last two decades where large-scale renewable energy sources (RES) are integrated into the modern power systems. Various countries have developed new energy policy instruments, such as Renewable Energy Certificates (RECs), to promote the growth of RES. RECs are tradable, non-tangible assets, which have a monetary value. Tracking and certification of the origin of an energy resource regardless of its type (e.g., a conventional power plant or RES) are a critical operation. In addition to the certification of origin, trading transactions must be performed using a secure method. Energy industry participants need to secure the data and applications related to RECs. Digitalization technologies such as artificial intelligence (AI) and distributed ledger technology (DLT) are among the most popular and promising options. DLT is a perfect framework that can support such REC functionalities. This paper addresses the cybersecurity aspects in REC trading using blockchain and distributed ledger technology, considering detailed cybersecurity perspectives and aspects of adopting technology from an organizational perspective.
Yosra Ghabri, Luu Duc Toan Huynh, Muhammad Ali Nasir
Abstract In the context of the COVIDâ19's outbreak and its implications for the financial sector, this study analyses the aspect of hedging and safeâhaven under the pandemic. Drawing on the daily data from 02 August 2019 to 17 April 2020, our key findings suggest that the contagious effects in financial assets' returns significantly increased under COVIDâ19, indicating exacerbated market risk. The connectedness spiked in the middle of March, consistent with lockdown timings in major economies. The effect became severe with the WHO's declaration of a pandemic, confirming negative news effects. The return connectedness suggests that COVIDâ19 has been a catalyst of contagious effects on the financial markets. The crude oil and the government bonds are however not as much affected by the spillovers as their endogenous innovation. In terms of spillovers, we do find the safeâhaven function of Gold and Bitcoin. Comparatively, the safeâhaven effectiveness of Bitcoin is unstable over the pandemic. Whereas, GOLD is the most promising hedge and safeâhaven asset, as it remains robust during the current crisis of COVIDâ19 and thus exhibits superiority over Bitcoin and Tether. Our findings are useful for investors, portfolio managers and policymakers interested in spillovers and safe havens during the current pandemic.
Dora Almeida, Andreia DionĂsio, Isabel Vieira, Paulo Ferreira
Cryptocurrency investments are often perceived as uncertain and risky. In this study, we assessed if this is indeed the case, using a sample of seven cryptocurrencies and considered a period that encompassed the first real global shock in the life of these relatively new financial assets, the COVID-19 pandemic. Uncertainty was evaluated using Shannonâs symbolic entropy. To measure risk, we use value-at-risk and conditional value-at-risk. The results indicate that, except for Tether, the analyzed cryptocurrenciesâ returns exhibited similar patterns of uncertainty and risk. Levels of uncertainty were close to the maximum values, but high uncertainty is not always associated with high risk. During the pandemic crisis, uncertainty increased while risk decreased, suggesting that the considered assets may have safe haven properties.
The cryptocurrency market is understood as being more volatile than traditional asset classes. Therefore, modeling the volatility of cryptocurrencies is important for making investment decisions. However, large swings in the market might be normal for cryptocurrencies due to their inherent volatility. Deviations, along with correlations of asset returns, must be considered for measuring the degree of market anomaly. This paper demonstrates the use of robust Mahalanobis distances based on shrinkage estimators and minimum covariance determinant for observing anomaly scores of cryptocurrencies. Our analysis shows that anomaly scores are a critical complement to volatility measures for understanding the cryptocurrency market. The use of anomaly scores is further demonstrated through portfolio optimization and scenario analysis.
Haruna Umar Yahaya, John Sunday Oyinloye, Samuel Olorunfemi Adams
The future of e-money is crypocurrencies, it is the decentralize digital and virtual currency that is secured by cryptography. It has become increasingly popular in recent years attracting the attention of the individual, investor, media, academia and governments worldwide. This study aims to model and forecast the volatilities and returns of three top cryptocurrencies, namely; Bitcoin, Ethereum and Binance Coin. The data utilized in the study was extracted from the higher market capitalization at 31st December, 2021 and the data for the period starting from 9th November, 2017 to 31st December 2021. The Generalised Autoregressive conditional heteroscedasticity (GARCH) type models with several distributions were fitted to the three cryptocurrencies dataset with their performances assessed using some model criteria. The result shows that the mean of all the returns are positive indicating the fact that the price of this three crptocurrencies increase throughout the period of study. The ARCH-LM test shows that there is no ARCH effect in volatility of Bitcoin and Ethereum but present in Binance Coin. The GARCH model was fitted on Binance Coin, the AIC and log L shows that the CGARCH is the best model for Binance Coin. Automatic forecasting was perform based on the selected ARIMA (2,0,1), ARIMA (0,1,2) and the random walk model which has the lowest AIC for ETH-USD, BNB-USD and BTC-USD respectively. This finding could aid investors in determining a cryptocurrency's unique risk-reward characteristics. The study contributes to a better deployment of investorâs resources and prediction of the future prices the three cryptocurrencies.
In recent years, digital currencies have flourished on a considerable scale, and the markets of digital currencies have generated a nonnegligible impact on the whole financial system. Under this background, the accurate prediction of cryptocurrency prices could be a prerequisite for managing the risk of both cryptocurrency markets and financial systems. Considering the multiscale attributes of cryptocurrency price, we match the different machine learning algorithms to corresponding multiscale components and construct the ensemble prediction models based on machine learning and multiscale analysis. The Bitcoin price series, respectively, from 2017/11/24 to 2020/4/21 and 2020/4/22 to 2020/11/27, is selected as the training and prediction datasets. The empirical results show that the ensemble models can achieve a prediction accuracy of 95.12%, with better performance than the benchmark models, and the proposed models are robust in upward and downward market conditions. Meanwhile, the different algorithms are applicable for components with varying time scales.
Azeez A. Oyedele, Anuoluwapo Ajayi, Lukumon O. Oyedele, Sururah A. Bello ¡ 5 authors
The emergence of cryptocurrencies has drawn significant investment capital in recent years with an exponential increase in market capitalization and trade volume. However, the cryptocurrency market is highly volatile and burdened with substantial heterogeneous datasets characterized by complex interactions between predictors, which may be difficult for conventional techniques to achieve optimal results. In addition, volatility significantly impacts investment decisions; thus, investors are confronted with how to determine the price and assess their financial investment risks reasonably. This study investigates the performance evaluation of a genetic algorithm tuned Deep Learning (DL) and boosted tree-based techniques to predict several cryptocurrencies' closing prices. The DL models include Convolutional Neural Networks (CNN), Deep Forward Neural Networks, and Gated Recurrent Units. The study assesses the performance of the DL models with boosted tree-based models on six cryptocurrency datasets from multiple data sources using relevant performance metrics. The results reveal that the CNN model has the least mean average percentage error of 0.08 and produces a consistent and highest explained variance score of 0.96 (on average) compared to other models. Hence, CNN is more reliable with limited training data and easily generalizable for predicting several cryptocurrencies' daily closing prices. Also, the results will help practitioners obtain a better understanding of crypto market challenges and offer practical strategies to lower risks.