This paper studies how to forecast daily closing price series of Bitcoin,\nusing data on prices and volumes of prior days. Bitcoin price behaviour is\nstill largely unexplored, presenting new opportunities. We compared our results\nwith two modern works on Bitcoin prices forecasting and with a well-known\nrecent paper that uses Intel, National Bank shares and Microsoft daily NASDAQ\nclosing prices spanning a 3-year interval. We followed different approaches in\nparallel, implementing both statistical techniques and machine learning\nalgorithms. The SLR model for univariate series forecast uses only closing\nprices, whereas the MLR model for multivariate series uses both price and\nvolume data. We applied the ADF -Test to these series, which resulted to be\nindistinguishable from a random walk. We also used two artificial neural\nnetworks: MLP and LSTM. We then partitioned the dataset into shorter sequences,\nrepresenting different price regimes, obtaining best result using more than one\nprevious price, thus confirming our regime hypothesis. All the models were\nevaluated in terms of MAPE and relativeRMSE. They performed well, and were\noverall better than those obtained in the benchmarks. Based on the results, it\nwas possible to demonstrate the efficacy of the proposed methodology and its\ncontribution to the state-of-the-art.\n
Ying Chen, Paolo Giudici, Branka Hadji Misheva, Simon Trimborn
We aim to understand the dynamics of Bitcoin blockchain trading volumes and, specifically, how different trading groups, in different geographic areas, interact with each other. To achieve this aim, we propose an extended Vector Autoregressive model, aimed at explaining the evolution of trading volumes, both in time and in space. The extension is based on network models, which improve pure autoregressive models, introducing a contemporaneous contagion component that describes contagion effects between trading volumes. Our empirical findings show that transactions activities in bitcoins is dominated by groups of network participants in Europe and in the United States, consistent with the expectation that market interactions primarily take place in developed economies.
Alternative assets, defined by their low correlation with classical financial assets, have become an important investment vehicle in times of negative interest rates and in the aftermath of the global economic and financial crisis. Hedge funds increasingly invest in physical assets such as fine art, wine, or diamonds. Although digital and not physical, cryptocurrencies share many features of alternative assets, but are hampered by high volatility, sluggish commercial acceptance, and regulatory uncertainties. This special issue covers a broad variety of topics in financial technology, and provides a state-of-the-art overview of cryptocurrencies from economic, financial, statistical and technical points of view.
Yuanyuan Zhang, Stephen Chan, Jeffrey Chu, Hana Sulieman
The market for cryptocurrencies has experienced extremely turbulent conditions in recent times, and we can clearly identify strong bull and bear market phenomena over the past year. In this paper, we utilise algorithms for detecting turnings points to identify both bull and bear phases in high-frequency markets for the three largest cryptocurrencies of Bitcoin, Ethereum, and Litecoin. We also examine the market efficiency and liquidity of the selected cryptocurrencies during these periods using high-frequency data. Our findings show that the hourly returns of the three cryptocurrencies during a bull market indicate market efficiency when using the detrended-fluctuation-analysis (DFA) method to analyse the Hurst exponent with a rolling window. However, when conditions turn and there is a bear-market period, we see signs of a more inefficient market. Furthermore, our results indicated differences between the cryptocurrencies in terms of their liquidity during the two market states. Moving from a bull to a bear market, Ethereum and Litecoin appear to become more illiquid, as opposed to Bitcoin, which appears to become more liquid. The motivation to study the high-frequency cryptocurrency market came from the increasing availability of higher-frequency cryptocurrency-pricing data. However, it also comes from a movement towards higher-frequency trading of cryptocurrency. In addition, the efficiency of cryptocurrency markets relates not only to whether prices are predictable and arbitrage opportunities exist, but, more widely, to topics such as testing the profitability of trading strategies and determining the maturity of cryptocurrency markets.
Purpose The purpose of this study is to test whether the price traders are prepared to pay for Bitcoin, over the market price, is related to a country’s level of corruption and lack of economic freedom, during Bitcoin’s most turbulent period, 2017-2018. Design/methodology/approach Bitcoin premiums (the excess over the market price) are calculated for 17 countries from April 2017 to September 2018, using daily weighted Bitcoin prices compared to the market price and testing against the Corruption Perception Index, the Index of Economic Freedom and changes in the Economic Uncertainty Index. Findings On testing Bitcoin premiums across 17 countries, it is found that the price paid for Bitcoin above the market price goes hand-in-hand with the level of corruption and decline in economic freedom. As corruption increases so does the premium paid for Bitcoin, and as the level of economic freedom declines, the premium increases. This relationship holds during very different market conditions: the increasing Bitcoin price from April to September 2017; the rise and fall in Bitcoin prices from October 2017 to March 2018; the declining market price from April to September 2018; and in December 2017 when Bitcoin reached over US$19,000. Originality/value Rather than focussing on daily returns which measure changes from day to day, this research uses all transactions during the day focussing on an intra-day consensus price measured in local currency. With such large price increases, percentage measures can hide the size of the monetary response. By focussing on the monetary impact, the differences between countries become more apparent.
This study proposes a method to enhance cryptocurrency portfolios constructed by forecast models. This study forecasts returns on four liquid cryptocurrencies (Bitcoin, Litecoin, Ripple, and Dash) and determines the weights on the cryptocurrencies based upon a dynamic allocation framework. We assess the performances of the portfolios using the performance fee measure. Our results present that the proposed portfolios outperform the benchmark portfolio with the conventional level of the risk aversion parameter. The economic gain for an investor is equivalent to 12% per week. The economic gain is sensitive to a change in the risk aversion parameter, which contrasts with the studies of exchange rates which is due to the high volatility on the cryptocurrencies. Our predictors are related to the price momentum effects and they outperform widely used network factors.
Bitcoin has been described as digital gold. Bitcoin is exactly like gold except when it isn’t. Over millennia, gold has gained a questionable reputation as an inflation hedge, a store of value and a safe haven. Gold’s price can arguably be decomposed into a “golden constant” fair price and a fair price deviation. Bitcoin has no track record as an inflation hedge, a store of value and a safe haven. Bitcoin’s price can arguably be decomposed into a questionable “bitcoin network” fair price and a fair price deviation. Both bitcoin and gold are about 50% above their “fair prices”.
Guglielmo Maria Caporale, Alex Plastun, Viktor Oliinyk
This paper investigates the relationship between Bitcoin returns and the frequency of daily abnormal returns over the period from June 2013 to February 2020 using a number of regression techniques and model specifications including standard OLS, weighted least squares (WLS), ARMA and ARMAX models, quantile regressions, Logit and Probit regressions, piecewise linear regressions, and non-linear regressions. Both the in sample and out-of-sample performance of the various models are compared by means of appropriate selection criteria and statistical tests. These suggest that, on the whole, the piecewise linear models are the best, but in terms of forecasting accuracy they are outperformed by a model that combines the top five to produce “consensus” forecasts. The finding that there exist price patterns that can be exploited to predict future price movements and design profitable trading strategies is of interest both to academics (since it represents evidence against the EMH) and to practitioners (who can use this information for their investment decisions).
Afees A. Salisu, Ahamuefula E. Ogbonna, Tirimisiyu F. Oloko
This study examines the effect of a pandemic-induced uncertainty on cryptocurrencies (specifically, Bitcoin, Ethereum and Ripple). It employs a predictive model by Westerlund and Narayan (2012, 2015) to examine the predictability of a pandemic-induced uncertainty as a predictor, as well as the forecast performance of our predictive model for cryptocurrency returns. We examine the role of asymmetry in uncertainty and the sensitivity of our results to alternative measures of uncertainty due to pandemics, using the recently developed Global Fear Index (GFI) by Salisu and Akanni (2020). Our results indicate that cryptocurrencies could act as hedge against uncertainty due to pandemics, albeit with reduced hedging effectiveness in the COVID-19 period. Accounting for asymmetry is found to improve the predictability and forecast performance of the model, which indicates that failure to account for asymmetry in modeling the effect of a pandemic-induced uncertainty on cryptocurrency may lead to incorrect conclusion. The results seem to be sensitive to the choice of measure of pandemic-induced uncertainty.
The aim of the paper is to check if cryptocurrency Bitcoin – a new investable asset class representative – is able to improve the performance of an optimal portfolio. Using two Markowitz criteria of optimization – expected return maximization and expected shortfall (CVaR) minimization – we test the investment opportunities after adding Bitcoin to the portfolio of 10 traditional assets (among them equity, fixed income, money, commodities and money market indices). Using daily observations from 1.05.2013 till 24.05.2019, we examine the behavior of the portfolios without and with Bitcoin and check if the return-risk ratio improves for the latter. Discussing the results, we conduct the sensitivity analysis by changing the lookback window (LB) and rebalancing frequency (RB) parameters. Empirical analysis suggests that Bitcoin-inclusive portfolios provide an investor with wider diversification opportunities. Robustness check confirms the findings and also advocates for the cryptocurrency to be added to the portfolio.
Ever since the invention of Bitcoin by the pseudonymous Satashi Nakamoto, cryptocurrency has provoked debate in banking and finance sectors, and is sometimes considered a potential successor to fiat currency. Blockchain, the new technology underpinning decentralised and immutable databases, has seen much discussion as a potentially game-changing development. Although many industries are exploring its value, the technology has thus far made only minor impacts. A rapidly expanding base of research has emerged on blockchain's role as a potential disruptor in the electrical energy industry. However, it may be difficult to distinguish hype from more imminently plausible impacts. This paper attempts to serve as a guide for engineering management wishing to make sense of blockchain's potential in electricity. This is accomplished by formulating a novel blockchain industry disruption framework, which exists across three tiers. These tiers extend from ideas with the least effect on an industry to total revolutionary concepts that could completely transform an industry. This taxonomy is constructed by examining existing research into disruption hierarchies and blockchain classification methods. Through the lens of this taxonomy, a literature review is performed on blockchain's role in energy to draw out themes and ideas characterising each tier. The potential likelihood of real-world application of various ideas are discussed, giving consideration to how established industries may be affected or disrupted. The authors provide some conjecture here. Finally, courses of action are suggested for those whose sector may be affected by blockchain.
Bitcoin market capitalization has recently surpassed $1 trillion. According to the popular belief one of the key characteristics of bitcoin is its excessive volatility. This paper provides evidence that high volatility of bitcoin is largely a misperception. We show that bitcoin return fluctuations are lower than those of roughly 900 different stocks in the S&P1500 and 190 stocks in the S&P500. Moreover, we find that bitcoin is less volatile than commodities such as oil and silver, US Treasuries, AAA-rated corporate bonds, EU carbon credits, and some of the most popular technology and media stocks: Apple, Twitter, and Netflix. Equally important, we find that during the March 2020 stock market crash triggered by COVID-19, bitcoin volatility was lower than most of the above-mentioned asset classes. Significant decline in bitcoin volatility over the last decade renders it more “investable” by conservative investors.
The cryptocurrency market is represented by more than 6,099 different cryptocurrencies with a total market capitalization of USD 354,316 million with Bitcoin dominance over 60%. Despite the increasing amount of scientific research, a comprehensive analysis of factors influencing the price of cryptocurrency is still needed. Previous studies have focused on the Bitcoin capitalization changes, rather than relationships and dependencies between the price of different cryptocurrencies and other factors. The author proposed a multiple linear regression model, which can be used for the cryptocurrency price forecast. The author tested the hypothesis, that Bitcoin's closing price changes likely in response to changes in altcoin prices and Google search index as well. According to the conducted research, the price of Bitcoin depends significantly on Google's search index on the specific cryptocurrency name. The revealed multiple regression equation can be further used for creating operational analytical programs for forecasting the price movement of Bitcoin.
Cryptography is a type of computer technology that is used for ensuring security, hiding information, and more. Bitcoins is the most recognized cryptocurrency. It is a person to person virtual currency which is used for online transactions. In this paper, an attempt has been made to analyses the price volatility of bitcoins. Further, a detailed content analysis has been presented in this paper. This study considers a study period of 8 and half years ranging from 19th June 2010 to 31st December 2018. Bitcoins prices were considered as variable to study the volatility of Bitcoins prices. Unit Root analysis is employed to check the Stationarity of the data series of bitcoins prices. ARCH Test, Volatility Clustering, ARCH, GARCH, TARCH and EGARCH have been used to analyze the price volatility. It was found that good news has more effect on the volatility of Bitcoins price returns than the bad news.
This paper analyzes the stability of stablecoins and proposes a framework to test for absolute and relative stability of stablecoins. Based on high-frequency data, we find strong evidence of excess price variations. While Bitcoin is a likely source of this excess volatility because stablecoin returns, volatility and volumes are highly correlated with corresponding Bitcoin time-series, we also demonstrate through a quasi-natural experiment that stablecoins increase the trading volume of Bitcoin. The findings suggest stablecoins play a key role in cryptocurrency markets.
The aim of this study is to determine whether successful predictions for cryptocurrencies such as Bitcoin can be obtained with different methods. The reason why Bitcoin prices (Bitcoin / $) are used in the study is that this cryptocurrency is still the most widely used cryptocurrency in the market, and the idea that it will successfully represent the overall state of the cryptocurrencies market. Financial market series may contain fluctuations for some reason, such as speculations. It also usually includes nonlinear changes. Such features lead to failures in obtaining forecasts for financial time series. In this study, with the GARCH model, one of the classicial time series models and LS -SVM method, a machine learning method, predictions of the Bitcoin price series were obtained, and model performances were compared. In the study, between January 01, 2017 and February 29, 2020, 1155 daily Bitcoin price series ( ) was used. In both models, the Bitcoin price series and the volatilities of this series were used, and external variables were not included in the models. For both models, forecasts were obtained for periods of 1 month, 2 months and 3 months. For GARCH and LS -SVM models, out of sample successful forecasting rates according to MAPE ratios were 98,0347% -95,3423% for 1 month; 97,9544% -96,1307% for 2 months and 98,1272% -91,4874% for 3 months, respectively. The GARCH model has provided more successful results for all three periods. The finding of the study is that the GARCH model can be used to obtain forecasts for the crypto price series.