The overall performance of a portfolio is the utmost measure of success for the skills of the portfolio manager. The Sharpe ratio and the modified Sharpe ratio have been some of the most referenced standards used in finance, to evaluate the efficiency of funds and hedge fund managers, however; such an ordering should be accompanied by proper statistical inference. In this work we examine whether Bitcoin can diversify significantly a reference portfolio composed from the five best performers of the Dow Jones industrial average in 2017 that is, Apple, Boeing, Caterpillar, Visa, and Walmart. The portfolios are constructed via analytical solutions in the mean-variance framework, constrained optimisation for the cases of long-only and risk-parity portfolios, and an equal weight strategy. The statistical significance of the Sharpe and modified Sharpe ratio differences is examined via a variety of tests. The results indicate that Bitcoin can significantly improve only the Sharpe and modified Sharpe ratios of the minimum variance and risk-parity portfolios. On the efficient frontier, the tangent portfolios are dominated by the traditional stocks.
We investigate similarities and differences between stock and cryptocurrency networks obtained from log-return and volatility time series. We constructed correlation and Fast Fourier Transform based graphs and minimum spanning trees from a set of 100 highly capitalized cryptocurrencies and 100 highly capitalized NASDAQ stocks over a time window of fixed length. Our analysis is based on comparison between both economies in terms of network properties. We also examined distributions of node degrees and edge weights. Our results show that cryptocurrencies and companies with high capitalization tend to correspond to central and densely connected nodes. Network topologies for both economies and node degree distributions are rather similar. Nevertheless, the crypto-economy is more correlated and more strongly linked to important nodes, unlike the graphs of NASDAQ stocks, where we observed clusters of nodes having small dissimilarities.
Shaista Bibi, Shahid Hussain, Muhammad Imran Faisal
Cryptocurrency is one of the emerging online currency of the modern era. Big companies are investing in this technology. However, some established companies still hesitate to use it. According to them, it is a volatile trend which will fade up eventually. There is no such authority which will provide them feasibility information. So, investors can be helped by providing them feasibility information about locations for cryptocurrency investment around the world. This paper aims to provide the aforementioned information to the investors. The proposed methodology is based on Topic modeling along with public opinion mining about cryptocurrencies, blockchain network, bitcoin, litecoin, and ethereum. The crawled data for other cryptocurrencies are much insufficient, so that are excluded from the study. In the proposed methodology, the top locations where cryptocurrency is widely used are identified, then in that particular locations' users concerns along with their sentiment analyses are investigated. Top locations are identified such as Australia, Denmark, Netherlands, and the USA etc. Almost 83.7% tweets of Sweden show positive sentiment for cryptocurrency investment which ranks as the highest having friendly environment for cryptocurrency investment. Similarly, the UK shows the least positive perception of cryptocurrency and blockchain technology usage. Some of the noteworthy terms found are legitimacy, authorization rules, volatility, profit, investment, and fluctuations. Which describe the users' concerns/ interests' about cryptocurrency. Investors can focus on all these areas during business. These subtopics can help business experts to evolve their businesses' and to make them more sustainable on the basis of public perception.
The evolution of the energy production and distribution towards innovative decentralized models, dictates the introduction of emerging technologies to transform the conventional energy sector into smart
Integration patterns between five leading conventional currencies after the US dollar and Bitcoin boost the investment potential of the latter relative to its hedging potential. We document that conditional Bitcoin volatility does not influence its dynamic pairwise correlations whereas the change in volatility of conventional currencies do affect the forex market integration patterns.
This paper proposes a bitcoin-based triangular arbitrage, combining foreign exchanges in the bitcoin market and reverse foreign exchange spot transactions. An FX futures contract is used to reduce exposure to risk as a hedging instrument. The returns of the portfolio are jointly modeled using a bivariate DCC-GARCH model with multivariate standardized student's t disturbances due to the presence of leptokurtosis and fat tails observed. Based on the time-dependent covariance matrix, a dynamic optimal hedge ratio is formed, with a conditional correlation series as a by-product. Empirical results are obtained using Euros and U.S. dollars over the period from 21 April 2014 to 21 September 2018. Multiple rolling one-step-ahead forecasts are generated. The empirical results present bitcoin-based currency strategies dominate bitcoin trading in terms of risk management.
This paper examines whether it is advisable to include some portion of Bitcoin in a portfolio of traditional financial assets. The goal is to explore whether Bitcoin could be a good source of diversification from the perspective of a global investor. Two portfolios have been created for this purpose: a portfolio aimed at minimizing risk and a portfolio designated as "aggressive" that offers higher rates of daily return but also a higher risk. Portfolios were created using Markowitz's optimization theory and included traditional instruments (stocks, bonds, gold) and Bitcoin. In portfolio optimization, high-frequency data (daily data) were used. The analysed period is from the end of July 2010 to the end of June 2019, which is the period of active Bitcoin trading. The results show that Bitcoin could be a good source of diversification for a portfolio that consists of traditional financial instruments, for investors trading daily. It could be a good source of diversification for the risk-averse investor and those investors who have a higher risk appetite. Considering the high volatility of Bitcoin, the investors should be very careful when they decide to include Bitcoin in a portfolio.
We take this question to be isomorphic to, "What Keeps Fixed Exchange Rates Fixed?" and address it with analysis familiar in exchange-rate economics. Stablecoins solve the volatility problem by pegging to a national currency, typically the US dollar, and are used as vehicles for exchanging national currencies into non-stable cryptocurrencies, with some stablecoins having a ratio of trading volume to outstanding supply exceeding one daily. Using a rich dataset of signed trades and order books on multiple exchanges, we examine how peg-sustaining arbitrage stabilizes the price of the largest stablecoin, Tether. We find that stablecoin issuance, the closest analogue to central-bank intervention, plays only a limited role in stabilization, pointing instead to stabilizing forces on the demand side. Following Tether's introduction to the Ethereum blockchain in 2019, we find increased investor access to arbitrage trades, and a decline in arbitrage spreads from 70 to 30 basis points. We also pin down which fundamentals drive the two-sided distribution of peg-price deviations: Premiums are due to stablecoins' role as a safe haven, exhibiting, for example, premiums greater than 100 basis points during the COVID-19 crisis of March 2020; discounts derive from liquidity effects and collateral concerns.
Cryptocurrencies have emerged about ten years ago as a new form of currency and have attracted much attention since they depend on a fully decentralized system, and so their transactions are very fast and have zero transaction cost. Therefore, character of cryptocurrencies and their volatility have been discussed widely by investors, policymakers and economists in recent years. From this point of view, this study aims to explain the price volatility of cryptocurrencies with macro-financial indicators, and thereby, the effects of S&P 500 stock market index, gold price, oil price, 2-year benchmark US Bond interest rate and US Dollar index on the prices of four major cryptocurrencies, Bitcoin, Litecoin, Ethereum, and Ripple, are investigated. The study comprises a panel data analysis applied to daily data over the period of August 2016 – April 2019, and analysis results show that increases in gold price, oil price and S&P 500 index raise the prices of cryptocurrencies, while increases in 2-year benchmark US Bond interest rate and US Dollar index cause to a fall. This adverse effects of the US Dollar index and US Bond interest rate on the prices of cryptocurrencies indicates that when the value of US Dollar and US Bond yield decrease investors prefer to invest in cryptocurrencies as alternative investment instruments. On the other hand, cryptocurrencies move with a similar trend of stock market index, gold price and oil price which are overall market indicators. Thereby, findings of this study show that cryptocurrencies behave more like an investment instrument than a currency, and prices of these financial assets interact with significant macro-financial indicators.
Bitcoin is a decentralised digital currency which attracts growing interest over recent years. Much research from different subjects emerged as bitcoin is a multidisciplinary product. Among all these studies, the interpretation of the drastic fluctuation of bitcoin price attracts a great attention. Many influence factors of bitcoin price were found. However, seldom research reveals the dependence structure between price and its influence factors. By selecting ten interpretable influence factors from the bitcoin network and using copula theory, we find that the bitcoin price has different correlation structures with its influence factors. These findings provide new insights into the behaviour of miners, users, and coins in the bitcoin system, thus leading to meaningful implications for policymakers, investors and risk managers dealing with bitcoin and other cryptocurrencies.
This study examines and compares the volatility in sample fit and out of sample forecast of four different heteroscedasticity models, namely ARCH, GARCH, EGARCH and GJR-GARCH applied to Bitcoin, Ethereum and Ripple. The models are fitted over the period from 2016-01-01 to 2019-01-01 and then used to obtain one day rolling forecasts during the period from 2018-01-01 to 2019-01-01. The study investigates three different themes consisting of the modelling framework structure, complexity of models and the relation between a good in sample fit and good out of sample forecast. AIC and BIC are used to evaluate the in sample fit while MSE, MAE and R2LOG are used as loss functions when evaluating the out of sample forecast against the chosen Parkinson volatility proxy. The results show that a heavier tailed reference distribution than the normal distribution generally improves the in sample fit, while this generality is not found for the out of sample forecast. Furthermore, it is shown that GARCH type models clearly outperform ARCH models in both in sample fit and out of sample forecast. For Ethereum, it is shown that the best fitted models also result in the best out of sample forecast for all loss functions, while for Bitcoin non of the best fitted models result in the best out of sample forecast. Finally, for Ripple, no generality between in sample fit and out of sample forecast is found.
We examine the significance of fourty-one potential covariates of bitcoin returns for the period 2010–2018 (2872 daily observations). The recently introduced principal component-guided sparse regression is employed. We reveal that economic policy uncertainty and stock market volatility are among the most important variables for bitcoin. We also trace strong evidence of bubbly bitcoin behavior in the 2017–2018 period.