Abstract We study the fundamental differences that separate: Litecoin; Bitcoin Gold; Bitcoin Cash; Ethereum; and Zcash from Bitcoin, and draw some analysis to how these features are appreciated by the market, to ultimately make an inference as to how future successful cryptocurrencies may be invented and behave. We use Google Trend data, as well as price, volume and market capitalization data sourced from coinmarketcap.com to support this analysis. We find that Litecoin’s shorter block times offer benefits in commerce, but drawbacks in the mining process through orphaned blocks. Zcash holds a niche use for anonymous transactions, benefitting areas of the world lacking in economic freedom. Bitcoin Cash suffers from centralization in the mining process, while the greater decentralization of Bitcoin Gold has generally left it to stagnate. Ether’s greater functionality offers the greatest threat to Bitcoin’s dominance in the market. A coin that incorporates several of these features can be technically better than Bitcoin, but the first-to-market advantage of Bitcoin should keep its dominant position in the market.
Florin Aliu, Artor Nuhiu, Besni̇k A. Krasniqi, Gent Jusufi
Purpose This study aims to compare the diversification risk of the crypto portfolio with those of equity portfolios. For this purpose, the hypothetical index was constructed with 20 cryptocurrencies that hold the highest market capitalization in the Coin Market Cap database, named as the Crypto-Index 20. Design/methodology/approach The portfolio diversification techniques were used to identify risk linked with the six largest European equity indexes and compared with the Crypto-Index 20. Indexes were considered as an independent portfolio while analysis was completed separately for each of them. Data concerning stock prices and their trade volume were collected from the Thomson Reuters Eikon database while crypto prices and their trade volume from the Coin Market Cap database. The diversification risk of the stock indexes was measured separately for each portfolio with the same risk techniques and the same methodological process. Findings Research results indicate that Crypto-Index 20 on average was 76 times riskier than FTSE 100, 55 times riskier than FTSE MIB, 44 times riskier than IBEX 35, 10 times riskier than CAC 40 and 9 times riskier than DAX and MDAX. Crypto-Index 20 comprises a stronger positive correlation and is exposed to higher volatility than six selected European equity indexes. Originality/value This research provides practical implications for the investors on the diversification benefits and risks attached to the cryptocurrencies portfolio by comparing it with the traditional equity portfolios. From a policy perspective, regulators might obtain information on the risk properties involved into cryptocurrencies and the possibility of creating an optimal portfolio.
This paper analyzes the connectedness among bitcoin, gold, and crude oil between 3 January 2017 and 31 December 2019. The paper’s motivation is based upon the idea that bitcoin can be similar to gold in terms of its hedging properties and can be used for hedging for different assets. Moreover, although it is more metaphorical, bitcoin is also accepted because it is mined like crude oil, namely, a commodity. These similarities can be investigated by analyzing the connectedness among these financial assets. The connectedness results derived from both total connectedness and frequency connectedness methods indicate that volatility connectedness is higher than the return connectedness among these assets. Furthermore, connectedness in volatilities is mostly driven by medium frequency, although connectedness in returns mostly exists in high frequency. Therefore, these results suggest that investors should consider these financial assets for their diversification decisions. The results suggest that although diversification among these three assets is more difficult in the short- and medium-term, investors may benefit from diversification in the long-run.
Md. Nur Alam Siddik, Sajal Kabiraj, Md. Emran Hosen, Md. Firoze Miah
Blockchain technology, a distributed and decentralized ledger, has gained significant prominence in the sphere of information technology for introducing new innovations in international trade. It ensures safety and can reduce trade costs by removing the middleman from the trade process. Studies examining the effects of blockchain on international trade are scarce. This research aims to fill this research gap. By using time series world data for the period 2009–2018, this study empirically examines the link between blockchain and international trade. It uses a cointegration test and a generalized linear model (GLM) test to analyse the data. The robust findings of this research reveal that blockchain has positive effects on international trade. The findings further displays that blockchain accelerates and facilitates international trade and that there exists a unidirectional causality from blockchain to international trade. The research findings are of great significance for policymakers in developing policies to foster the use of blockchain applications as a facilitator of international trade.
Abstract Through the application of the VAR-AGARCH model to intra-day data for three cryptocurrencies (Bitcoin, Ethereum, and Litecoin), this study examines the return and volatility spillover between these cryptocurrencies during the pre-COVID-19 period and the COVID-19 period. We also estimate the optimal weights, hedge ratios, and hedging effectiveness during both sample periods. We find that the return spillovers vary across the two periods for the Bitcoin-Ethereum, Bitcoin-Litecoin, and Ethereum-Litecoin pairs. However, the volatility transmissions are found to be different during the two sample periods for the Bitcoin-Ethereum and Bitcoin-Litecoin pairs. The constant conditional correlations between all pairs of cryptocurrencies are observed to be higher during the COVID-19 period compared to the pre-COVID-19 period. Based on optimal weights, investors are advised to decrease their investments (a) in Bitcoin for the portfolios of Bitcoin/Ethereum and Bitcoin/Litecoin and (b) in Ethereum for the portfolios of Ethereum/Litecoin during the COVID-19 period. All hedge ratios are found to be higher during the COVID-19 period, implying a higher hedging cost compared to the pre-COVID-19 period. Last, the hedging effectiveness is higher during the COVID-19 period compared to the pre-COVID-19 period. Overall, these findings provide useful information to portfolio managers and policymakers regarding portfolio diversification, hedging, forecasting, and risk management.
Md. Jamal Hossain, Mohd Tahir Ismail, Sadia Akter, Mohammad Raquibul Hossain
Gold is the most attractive precious metal and thus for the investor's first choice of an alternative investment. After the establishment of Bitcoin becomes one of the alternative choices of investment. Both Gold and Bitcoin have attractive features of risk management and portfolio risk. In this paper, we compared Gold and Bitcoin performances as hedge or diversifiers and tried to isolate the best one. For comparison purposes, we used the same methodology, which is the asymmetric GARCH model considering three different markets, such as the energy market, currency market, and stock market. We found Gold serves as a diversifier against crude oil WTI and hedge against S&P500, which is short-run. Bitcoin only offers diversifier benefits against crude oil WTI. For portfolio analysis, Gold is better than Bitcoin; therefore, it must be the first choice for investors.
Practical Applications In Cryptocurrencies as an Asset Class?An Empirical Assessment from the Fall 2020 issue of TheJournal of Alternative Investments, author Daniele Bianchi of Queen Mary University of London explores how cryptocurrencies (for example, Bitcoin) relate to traditional asset classes such as stocks and bonds. Bianchi posits that cryptocurrencies are global investments because they are not tied to any country. He therefore matches the performance and volume of cryptocurrency against that of global stock, bond, and other indexes. He also explores the driving factors behind cryptocurrency market activity. Bianchi finds no significant correlation between the performance of cryptocurrencies and that of traditional assets, except for a slight correlation with commodities, especially precious metals. He also finds no correlation between the volatility of cryptocurrencies and traditional assets. Finally, he finds that cryptocurrency trading volumes are not affected by macroeconomic events; their main driver is past performance—meaning investors trade cryptocurrency based on gut feelings rather than analysis. Like gold, cryptocurrencies can act as a hedge against stock market losses—but they do not have gold’s perceived intrinsic value, since their value is based only on the platforms and projects with which they are associated. TOPICS:Currency, exchanges/markets/clearinghouses
Tahmin teknikleri ve modelleri, doğru karar alma ve yatırım aşamasında kişiler ve kuruluşlar için son derece önemlidir. Tahminin doğruluğu başarılı kararlar alınmasını sağlar ve yatırımcıların fayda maksimizasyonuna ulaşmasına imkân tanır. Bu çalışmada, kripto para türlerinden en yaygın olarak kullanılan Bitcoin fiyatlarının yapay sinir ağları yöntemi ile tahmin edilmesi amaçlanmıştır. Girdi değişkenler olarak; Dow-Jones, S&P500, Nasdaq100, Eurostoxx Endeksleri, İsviçre Frangı, İngiliz Sterlini, Euro, Altın, Gümüş yatırım araçları alınmıştır. 2013-2018 tarihleri arasında günlük kapanış fiyatları verileri kullanılmıştır. Çalışmada geri beslemeli yapay sinir ağı modeli kullanılmıştır. 2019 Ocak ayı tahmini yapılarak model test edilmiştir ve modelin tahmin doğruluğu R2 değeri %99 başarı ile gerçekleşmiştir.
Raja Nabeel‐Ud‐Din Jalal, Massimo SARGIACOMO, Najam Us Sahar
The study investigates the role of commodity prices and tax purpose recognition on bitcoin prices. Since the introduction of bitcoin in 2008, emphasis has focused on economists, policy-makers and analysts drastically increasing bitcoin's accessibility and commodity values (Dumitrescu & Firică, 2014). This study employs GARCH and EGARCH from ARCH/GARCH family on daily nature data. We measure the volatile behavior of bitcoin by employing auto-regressive conditional heteroscedasticity model with the aim to explore the relationship between major commodities and bitcoin volatility. We focus on major commodities like gold, silver, platinum, and crude oil to be regressed with bitcoin. The daily prices of commodities were retrieved from www.investing.com and bitcoin prices from www.coindesk.com for the period from 29April 2013 to 16 October 2018. Results confirmed the currency's long-term volatile behavior, which is due to its composition and market dynamics, whereas the existence of asymmetric information effect is not confirmed. Tax recognition by other countries may in future help in controlling the volatility as bitcoin is not a country-specific security. But, only silver impacts on volatility in comparison to oil prices and platinum, which is due to its similar features with gold. Eventually, bitcoin can be used for risk diversification and money making.
Jeremy Eng‐Tuck Cheah, Di Luo, Zhuang Zhang, Ming‐Chien Sung
This paper comprehensively examines the performance of a host of popular variables to predict Bitcoin returns. We show that time-series momentum, economic policy uncertainty, and financial uncertainty outperform other predictors in all in-sample, out-of-sample, and asset allocation tests. Bitcoin returns have no exposure to common stock and bond market factors but rather are affected by Bitcoin-specific and external uncertainty factors.
Abstract This study aims to explain price movements in the two largest cryptocurrencies that represent the majority of cryptocurrency market capitalization—Bitcoin and Ethereum. A VAR‐GARCH‐BEKK model is estimated to analyze how Google search interest, number of tweets and active addresses on the blockchain impact prices of Bitcoin and Ethereum over time. We find solid evidence that the amount of active addresses is the most significant variable among others influencing price movements in Bitcoin and Ethereum. Based on spillover effects and GIRFs, Google searches and tweets, to a certain extent, have impacts on the Bitcoin and Ethereum prices, but the impacts are weaker than that of active addresses in terms of magnitude and significance.
Kevin E. Martin, Izzat Alsmadi, Mohamed Rahouti, Moussa Ayyash
Blockchain is an emerging technology that enables a vital framework for various cryptocurrency operations such as bitcoin. Notably, without any involvement from third party authorities, blockchain offers a decentralized consensus scheme to process user transactions, fund transfer, and various data records in a secure and reliable way. Furthermore, bitcoin price forecasting has been a vital research trend, where machine learning techniques play a substantial role. A sophisticated and appropriately trained model can be useless if the features being tested are unreliable. Independently, one of the most desirable aspects of a system that utilizes the blockchain is the concrete objectiveness by which each entry is cataloged. Any data collected and reported on the blockchain is unambiguous, and therefore, extremely suitable for a machine learning algorithm. To efficiently forecast bitcoin price movements, in this work, we propose and examine various lenses by which to view this union, each with varying degrees of success.
This essay brings insights from the academic literature on foreign exchange rate determination to the analysis of cryptocurrency markets. We present a simple framework to summarize the factors that determine exchange rates. To the extent that cryptocurrencies are like national currencies issued by central banks, their pricing can be analyzed using these models of exchange rates…
In recent years, bitcoin has been at the center of a debate: is it a financial asset or not? If it is an asset, what kind of asset is it? Is it a tradable, speculative asset, or is it a long-term investment? The debate has largely unfolded around the real nature of Bitcoin and its correlation with other asset classes. Some scholars have discussed the correlation coefficient of bitcoin using the S& P 500 Index, the Volatility Index, and gold. However, the debate is now sufficiently mature to discuss the reasons determining its correlation with other assets. We study the correlations of Bitcoin and one benchmark, a stock market index-specifically, the Nasdaq 100 Index. Our focus is on the correlation between Bitcoin and the Nasdaq 100 as a proxy for the most representative technology stocks. We show that the correlation between the two has grown substantially since 2018, and has increased more since the beginning of the COVID-19 pandemic.
Investors are still worried about the variables brought about by COVID-19 and the presidential election because of the recent growth trend of the stock market despite the macroeconomic downturn. This article speculated whether the portfolio of biotech owned stocks and bitcoin can cope with these uncertainties. At the same time, investors need to pay attention to the companies that are studying the COVID-19 vaccine and whether they have state financial support. Also, Python and Excel were used to explore the daily return, annual volatility, and portfolio performance of biotechnology stocks. Besides, a multiple linear regression model on CPI and GDP focusing on whether macroeconomic factors affect bitcoin was established. These results support the conclusion that currently adding biotech supported by the federal government and bitcoin into the portfolio could respond to the risks from COVID-19.
Abstract From the past two years with increasing geopolitical and economic issues, global currency values have been falling and stock markets have been having a poor run & investors losing wealth. This has led to a renewal of interest in digital currencies. Cryptocurrency one of the most prominent digital currency has found itself in spotlight with investors wanting a piece of it and business establishments accepting it as a source of payment due to its stable performance in the last few years. This research has been done on predicting cryptocurrency prices using machine learning based neural network which has a lowest the model loss over 100 epochs during training and Technical Trade Indicators (TTI) graphs depicts a real BTC value 5 to 10 times in 300-days of current fiscal year has further supported this increasing trader confidence and a shift in global cryptocurrency graph by predicted BTC values. On the same lines, we are analyzing bitcoin prices using Machine Learning and Sentiment Analysis. We also study stock market trends in order to better predict bitcoin prices quantitively. In this work we analyze the impact of global currencies like US Dollar, foreign exchanges on Bitcoin prices and whether Bitcoin has the stability to dethrone global currencies and become the single medium of transaction. This work is adequate enough to aid in predicting price and with results obtained from predicting Bitcoin prices using machine learning based neural network achieving an accuracy of 94.89% under all circumstances of technical trade indication thereby bringing down its price prediction by over 13.7% in April 2020 itself during evaluation.
The authors acquire a unique dataset of high-frequency traded prices for bitcoin call and put options from the Deribit cryptocurrency derivatives exchange, by 15-minute sampling via the application programming interface. They use these prices to construct a term structure of bitcoin implied volatility indices using a variance swap fair-value formula that is employed by the CBOE for the VIX, an index commonly referred to as the “investor fear gauge” for the US stock market. Employing over seven million option prices, they construct the bitcoin implied volatility indices with maturities from one week to three months, sampled every 15 minutes from March 2019 to March 2020. They discuss the features of the index and the associated bitcoin variance risk premia, with three different regular time partitions for realized variance, viz. 15-minutes, hourly, and daily. They also examine the relationship between bitcoin’s 30-day realized variance, volatility index, and variance risk premium, with their equivalent for US equities, oil, gold, the USD/EUR exchange rate, and the 10-year US Treasury note. <b>TOPICS:</b>Currency, mutual funds/passive investing/indexing, statistical methods, performance measurement <b>Key Findings</b> • The authors’ novel dataset of bitcoin option prices from Deribit is applied to quote bitcoin VIX indices with maturities from one-week to three months. The short-maturity bitcoin VIX exceeded 200% after the global outbreak of Covid-19. • The indices provide indicative fair values for bitcoin variance swaps, which are traded on-chain. Using realized volatility monitored at 15-minute, hourly, and daily frequencies, they examine the bitcoin variance risk premium at different maturities. • Bitcoin’s 30-day realized volatility, volatility index, and variance risk premium are correlated with their equivalent for US equities, oil, gold, the USD/EUR exchange rate, and the 10-year US Treasury note. Diversification potential decreased dramatically after the outbreak of Covid-19.
In this paper, we discuss the behavior of stock market returns in Tunisia during the COVID-19 outbreak. Using the OLS regression, we find that Bitcoin act as a hedge and Ethereum as a diversifier for Tunisia’s stock market before the COVID-19 outbreak; however, Bitcoin and Ethereum cannot generate benefits from portfolio diversification and hedging strategies for financial investors during the COVID-19. Moreover, Dash, Monero, and Ripple act as hedges before the COVID-19 outbreak and as diversifiers during this pandemic. Our results reveal that gold acts as a hedge and diversifier before the pandemic, but it's neither hedge nor a haven during the COVID-19 pandemic. Besides, the results indicated that the expected volatility of the US stock market has an impact on the Tunisian stock market. Finally, our results indicate that the growth rate of the COVID-19 confirmed cases and deaths harms Tunisia's stock market.