Walid Mensi, Mobeen Ur Rehman, Muhammad Shafiullah, Khamis Hamed Al‐Yahyaee · 5 authors
This paper examines the high frequency multiscale relationships and nonlinear multiscale causality between Bitcoin, Ethereum, Monero, Dash, Ripple, and Litecoin. We apply nonlinear Granger causality and rolling window wavelet correlation (RWCC) to 15 min-data. Empirical RWCC results indicate mostly positive co-movements and long-term memory between the cryptocurrencies, especially between Bitcoin, Ethereum, and Monero. The nonlinear Granger causality tests reveal dual causation between most of the cryptocurrency pairs. We advance evidence to improve portfolio risk assessment, and hedging strategies.
Understanding the dependence and risk spillover among hedging assets is crucial for portfolio allocation and regulatory decision making. Using various copula and conditional Value-at-Risk (CoVaR) measures, this paper quantifies the dependence and risk spillover effects between three traditional and emerging hedging assets: Bitcoin, gold, and USD. Furthermore, we investigate these effects at various short- and long-term horizons using a variational model decomposition (VMD) method. The empirical results show that there is strong negative dependence between gold and USD, but Bitcoin and gold are weakly and positively connected. Secondly, risk spillovers exist only between Bitcoin and gold and between gold and USD. The risk spillover effect between Bitcoin and gold are not stable, that is, if Bitcoin or gold faces the downward or upward risk, both the downward and upward risk of another asset have the chance to increase. The negative risk spillover between gold and USD is stable, especially in long-term horizons. Finally, the risk spillover between Bitcoin and gold as well as between gold and USD are asymmetric at downward and upward market environment.
This paper studies the impact of investor sentiment on the Bitcoin returns and conditional volatility taking into account the Covid-19 outbreak by using different investor sentiment proxies and by employing the EGARCH model. Estimation results show that investor sentiment has a positive impact on the Bitcoin returns and their volatility, especially after the Covid-19 outbreak. The VAR model is employed to investigate whether investor sentiment and Bitcoin returns are related in a dynamic setting and to make distinguish between rational and irrational investor sentiments. The results from the VAR model show that both rational and irrational investor sentiments have an impact on Bitcoin returns indicating that the Bitcoin market is also driven by emotions and noise traders have an impact on the data generating process of Bitcoin returns. The positive impact of investor sentiment can be attributed to the fear of missing out (FOMO) behavior of speculative and irrational investors.
The high power consumption of Bitcoin transactions has raised environmental and sustainable concerns of green investors and regulatory bodies. We utilize the time-varying optimal copula (TVOC) approach to showcase the dependence structure between bitcoin and green financial assets. We find multiple tail-dependence regimes characterize the extreme dependence between bitcoin and green financial assets, and the dependence structure is mainly asymmetric and time-varying. Finally, the hedging effectiveness of green financial assets for bitcoin revealed that all green assets, especially clean energy, are effective hedges for bitcoin.
Due to the transparency, simplicity, and blockchain system, cryptocurrencies gained popularity in the modern world. This led to more use of cryptocurrencies for speculation and investment rather than a medium of exchange. It is crucial to analyse the nature of the crypto market before investing in such currencies. With this intention, the paper tried to know the extent of following (Followness) of altcoins to the bitcoin in the different dominance phases like High Dominance, Low Dominance, and Moderate Dominance. For this purpose, daily closing prices of the Bitcoin and five major altcoins (Ethereum, Litecoin, Namecoin, Doge, and Ripple) are collected for the last five years and analyse the relationship between bitcoin and altcoins. Pearson's correlation coefficient test is used to know the direction of the relationship, and Vector Error Correction Model is used to see the extent of the relation. In general, the empirical result of the study showed cointegration between bitcoin and Altcoin. It also depicted that Altcoin showed a high level of followness in the moderate dominance phase and low followness in the low dominance phase. The study developed a price estimation equation to predict the price of altcoins depending upon the price of bitcoin and its dominance in the crypto market. This paper concludes that the dominance of Bitcoin also has a significant role in the price movement of altcoins.
Cryptocurrency literature is increasing rapidly nowadays. Particularly, the role of the cryptocurrency market as a risk management avenue has got the attention of researchers. However, it is an immature asset class and requires gaps in current literature for future research directions. This research provides a systematic review of the vast range empirical literature based on the cryptocurrency market as a risk management avenue against economic policy uncertainty (EPU). The review discovers that cryptocurrencies have mixed connectedness patterns with all national EPU therefore, the risk mitigation ability varies from country to country. The review finds that heterogeneous correlation patterns are due to the dependence of EPU on the policies and decisions usually taken by regulatory authorities of a particular country. Additionally, heterogeneous EPU requires heterogeneous solutions to deal with stock market volatility and economic policy uncertainty in different economies. Likewise, the divergent protocol and administration of currencies in the crypto market consequently vicissitudes the hedging and diversification performance against each economy. Many research lines can benefit investors, policymakers, fund managers, or portfolio managers. Therefore, the authors suggested future research avenues in terms of topics, data frequency, and methodologies.
Abstract The recently developed Bitcoin futures and options contracts in cryptocurrency derivatives exchanges mark the beginning of a new era in Bitcoin price risk hedging. The need for these tools dates back to the market crash of 1987, when investors needed better ways to protect their portfolios through option insurance. These tools provide greater flexibility to trade and hedge volatile swings in Bitcoin prices effectively. The violation of constant volatility and the log-normality assumption of the Black–Scholes option pricing model led to the discovery of the volatility smile, smirk, or skew in options markets. These stylized facts; that is, the volatility smile and implied volatilities implied by the option prices, are well documented in the option literature for almost all financial markets. These are expected to be true for Bitcoin options as well. The data sets for the study are based on short-dated Bitcoin options (14-day maturity) of two time periods traded on Deribit Bitcoin Futures and Options Exchange, a Netherlands-based cryptocurrency derivative exchange. The estimated results are compared with benchmark Black–Scholes implied volatility values for accuracy and efficiency analysis. This study has two aims: (1) to provide insights into the volatility smile in Bitcoin options and (2) to estimate the implied volatility of Bitcoin options through numerical approximation techniques, specifically the Newton Raphson and Bisection methods. The experimental results show that Bitcoin options belong to the commodity class of assets based on the presence of a volatility forward skew in Bitcoin option data. Moreover, the Newton Raphson and Bisection methods are effective in estimating the implied volatility of Bitcoin options. However, the Newton Raphson forecasting technique converges faster than does the Bisection method.
Cryptocurrency and blockchain are one of the most beautiful digital transformations occurring around the world. They have changed the orthodox meaning and working of currency as we know it. It is interesting to note how it excites and worries some. The main reason for the popularity of cryptocurrencies is tremendous returns in very little time. Social media platforms like twitter, provide a safe-place where individuals’ can share their thoughts as well as mindsets, which then can be heard and be reciprocated by others. This paper aims to draw a correlation between the hyped tweets and the prices of cryptocurrencies like Bitcoin - The Crypto King and Dogecoin - The Memecoin during those times. We also aim to predict the future price values of Bitcoin using its past values. By using cryptocurrencies’ financial data, twitter data, RAPIDS and cuml, a fine line can be drawn between the amount of impact tweets have on people as well as on the market. The tweets on cryptocurrency were segregated and price forecasting was done using augmented dickey fuller test and ARIMA models, 10 future values of bitcoin were predicted with 96% accuracy and 0.0395 average error.Besides, from the investigations above of the authentic cost of BTC, it is perfectly clear that there have been way more steep falls in the history of Cryptocurrencies even before Elon started tweeting about it. Thus, it can clearly be stated that no one person can control the utter volatile world of cryptocurrencies! And the decentralized system ledger of cryptocurrency remains unharmed.
The purpose of this paper is to contribute insights into the existing body of literature on Cryptocurrencies by systematizing existing knowledge and deriving specific implications for the scope of future work. Looking specifically on purpose of creation of cryptocurrency and answer the questions related to the usefulness of cryptocurrency as a tool for hedging purpose. Understanding cryptocurrency as an opportunity for valuable investments or considering them as merely speculative bubbles. The literature has showed the major risks associated with the cryptocurrencies and the prevailing huge price volatility in cryptocurrency market. Also, paper discussed the most fundamental issues pertaining to cryptocurrencies.
Cryptocurrency can be a relatively new combination of cryptocurrency and currency in the cash space and is widely used around the globe. However, the entity of the blockchain program area is expected to reshape the renewable energy market for the usability of modern virtual currencies. Therefore, this study makes a specialty of various experiments on mining potency, especially for dogecoin mining. A targeted assessment of carried out a mathematics evaluation of information in an exceptional benchmark and test outcomes of Dogecoin mining is conducted. The goal of this paper is to illustrate the energy consumption and performance of the mining of various cryptocurrencies. The renewable electricity zone could be restructured in blockchain applications. However, research on the use of electricity in digital currencies is lacking. Therefore, nine types of cryptocurrencies and 10 algorithms were examined in this take a look at for mining effectiveness. A comparison was made of statistical data analysis in a Dogecoin mining benchmark and experimental results. This research subsequently provided an estimate of the Dogecoin mining activity's gross electricity consumption. The results show that the hashing algorithm defines primarily the mining performance.
Fahad Mostafa, Pritam Saha, Mohammad Rafiqul Islam, Nguyet Nguyen
Cryptocurrencies are currently traded worldwide, with hundreds of different currencies in existence and even more on the way. This study implements some statistical and machine learning approaches for cryptocurrency investments. First, we implement GJR-GARCH over the GARCH model to estimate the volatility of ten popular cryptocurrencies based on market capitalization: Bitcoin, Bitcoin Cash, Bitcoin SV, Chainlink, EOS, Ethereum, Litecoin, TETHER, Tezos, and XRP. Then, we use Monte Carlo simulations to generate the conditional variance of the cryptocurrencies using the GJR-GARCH model, and calculate the value at risk (VaR) of the simulations. We also estimate the tail-risk using VaR backtesting. Finally, we use an artificial neural network (ANN) for predicting the prices of the ten cryptocurrencies. The graphical analysis and mean square errors (MSEs) from the ANN models confirmed that the predicted prices are close to the market prices. For some cryptocurrencies, the ANN models perform better than traditional ARIMA models.
Bitcoin has attracted attention from different market participants due to unpredictable price patterns. Sometimes, the price has exhibited big jumps. Bitcoin prices have also had extreme, unexpected crashes. We test the predictive power of a wide range of determinants on bitcoins’ price direction under the continuous transfer entropy approach as a feature selection criterion. Accordingly, the statistically significant assets in the sense of permutation test on the nearest neighbour estimation of local transfer entropy are used as features or explanatory variables in a deep learning classification model to predict the price direction of bitcoin. The proposed variable selection do not find significative the explanatory power of NASDAQ and Tesla. Under different scenarios and metrics, the best results are obtained using the significant drivers during the pandemic as validation. In the test, the accuracy increased in the post-pandemic scenario of July 2020 to January 2021 without drivers. In other words, our results indicate that in times of high volatility, Bitcoin seems to self-regulate and does not need additional drivers to improve the accuracy of the price direction.
Abstract Technological developments have always led to changes in all aspects of our lives. Crypto currency is one of those changes. As a result of those changes, thousands of currencies such as bitcoin, ripple, litecoin and ethereum have evolved and have found a use in business. The present study focuses upon Ripple and tries to explain its effects on banks and business theoretically. It has been stated that the money transfer performed through Ripple is faster and more economical when compared to present systems. Additionally, it has been realised that the present SWIFT system has been influenced by that speed and economy, and therefore taken considerable technologic steps with an effort to improve its system.
Abstract Subject and purpose of work: Cryptocurrencies are a phenomenon that has been strengthening its place in the world of finance for over ten years and which is becoming a frequent investment tool. The aim of this study is to compare the level of risk measures of investments in the cryptocurrency market with investments in global capital markets in 2011-2020. Materials and methods: The study used the quotations of the analysed instruments. The level of risk was estimated using standard deviation and semi-standard deviation of daily logarithmic rates of return. Results: Investment in cryptocurrencies is more risky than in shares of the largest international companies. The level of risk decreases with the duration of the cryptocurrency presence on the market. Conclusions: Achieving extraordinary rates of return generates an increased demand and volatility of cryptocurrencies’ quotations. The level of risk of investing in cryptocurrencies is much higher than in the indexes of global capital exchanges.
AbdulQuddoos AbdulBasith, Mohammed Elgammal, Bana Abuzayed
Cryptocurrency (CCY) as a new key player in the currency system that has drawn the attention of scholars to examine its influence, relations and the opportunities that it may provide. However, a financial theoretical framework to connect CCY with financial theory is missing. This paper fills this gap by providing a review for the theoretical framework introduced in the literature to position CCY in investment and finance theories. This is done by studying the CCY literature and providing a critical feedback on the overall contributions in the area and possible venues for improvement. We report a need for a long-term analysis for CCY as this asset class is fairly new and sufficient data may not be available. Moreover, a better connection and linking with finance theories is required as it is significantly deficient. The promising potential of blockchain/ CCY stresses the need for interdisciplinary research including business, legal and information technology disciplines. In addition, the Covid-19 pandemic opens the door for further research to investigate the role of CCY as a hedge in the times of crises. Keywords: digital ledger technology, cryptocurrency bitcoin, finance theory, investment, fintech
The issuance of a Central Bank Digital Currency (CBDC) is a very important step towards a fully digital economic environment and the consequences of such a direction are under debate by many policymakers around the world. There is a clear interest within the space as governments around the world are exploring the viability of a digital currency and according to the latest Bank for International Settlements (2021) report: 86% of the world's central banks have begun to conceptualize and research the potential of CBDC, 60% are developing Proof-of-Concepts (PoC) and 14% are implementing pilot projects. This paper provides a comprehensive overview for finance and investment participants about the topic of Central Bank Digital Currencies. The recent international exploration into the future of Central Bank money is complex as it is interconnected with two equally dynamic entities: Digital Currencies and Blockchain/Distributed Ledger Technology.