Bitcoin is defined as digital money within a decentralized peer-to-peer payment network. It is a hybrid between fiat currency and commodity currency without intrinsic value and independent of any ...government or monetary authority. This paper analyses the question of whether Bitcoin is a medium of exchange or an asset and more specifically, what is its current usage and what usage will prevail in the future given its characteristics. We analyse the statistical properties of Bitcoin and find that it is uncorrelated with traditional asset classes such as stocks, bonds and commodities both in normal times and in periods of financial turmoil. The analysis of transaction data of Bitcoin accounts shows that Bitcoins are mainly used as a speculative investment and not as an alternative currency and medium of exchange.
Many papers in recent years have examined the benefits of adding alternative assets to traditional portfolios containing stocks and bonds. Bitcoin has emerged as a new alternative investment for ...investors which has attracted much attention from the media and investors alike. However relatively little is known about the investment benefits of Bitcoin and therefore this paper examines the benefit of including Bitcoin in a traditional benchmark portfolio of stocks and bonds. Specially, we employ data up to June 2018 and analyse the potential out-of-sample portfolio benefits resulting from including Bitcoin in a stock-bond portfolio for a range of eight popular asset allocation strategies. The out-of-sample analysis shows that, across all different asset allocation strategies and risk aversions, the benefits of Bitcoin are quite considerable with substantially higher risk-adjusted returns. Our results are robust to rolling estimation windows, the incorporation of transaction costs, the inclusion of a commodity portfolio, alternative indices, short-selling as well as two additional optimization techniques including higher moments with (and without) variance-based constraints (VBCs). Therefore, our results suggest that investors should include Bitcoin in their portfolio as it generates substantial higher risk-adjusted returns.
Abstract
The Bitcoin lightning network (BLN), a so-called ‘second layer’ payment protocol, was launched in 2018 to scale up the number of transactions between Bitcoin owners. In this paper, we ...analyse the structure of the BLN over a period of 18 months, ranging from 12th January 2018 to 17th July 2019, at the end of which the network has reached 8.216 users, 122.517 active channels and 2.732,5 transacted Bitcoins. Here, we consider three representations of the BLN: the
daily snapshot
one, the
weekly snapshot
one and the
daily-block snapshot
one. By studying the topological properties of the binary and weighted versions of the three representations above, we find that the total volume of transacted Bitcoins approximately grows as the square of the network size; however, despite the huge activity characterising the BLN, the Bitcoins distribution is very unequal: the average Gini coefficient of the node strengths (computed across the entire history of the Bitcoin lightning network) is, in fact, ≃0.88 causing the 10% (50%) of the nodes to hold the 80% (99%) of the Bitcoins at stake in the BLN (on average, across the entire period). This concentration brings up the question of which minimalist network model allows us to explain the network topological structure. Like for other economic systems, we hypothesise that local properties of nodes, like the degree, ultimately determine part of its characteristics. Therefore, we have tested the goodness of the undirected binary configuration model (UBCM) in reproducing the structural features of the BLN: the UBCM recovers the disassortative and the hierarchical character of the BLN but underestimates the centrality of nodes; this suggests that the BLN is becoming an increasingly centralised network, more and more compatible with a core-periphery structure. Further inspection of the resilience of the BLN shows that removing hubs leads to the collapse of the network into many components, an evidence suggesting that this network may be a target for the so-called
split attacks
.
This paper investigates the predictive power of information contained in social media tweets on bitcoin market dynamics. Using Valence Aware Dictionary for Sentiment Reasoning (VADER), we extract ...useful information from tweets and construct two factors – sentiment dispersion (SD) and investor attention (IA) – to test their predictive power. We show that investors face greater return volatility for rising sentiment dispersion associated with more significant market uncertainty. Further, IA is found to predict bitcoin trading volume but not returns and volatility. Finally, we design an IA-induced trading strategy that yields superior performance to the passive buy-and-hold strategy in 2018. However, it does not deliver superior performance in other years during the sample period suggesting that investor attention alone as a trading parameter does not produce superior performance over the long term.
The aim of the article is to verify whether bitcoin can act as a hedge, diversifier or safe haven on various stock markets, depending on the economic situation in the countries. To diversify the ...sample, we include five very different countries in our study: Japan, Venezuela, China, Estonia, and Sweden. Using daily data over the period 2014–2017, we estimate the dynamic conditional correlation model between main stock indices and bitcoin price in local currencies (Bitflyer — in the case of the yen, Kraken — in the case of the euro, Huobi in the case of yuan and LocalBitcoins in all the remaining cases), as well as between main stock indices and the bitcoin price in the US dollar (Bitfinex exchange). We apply the Stochastic Volatility Model with the Dynamic Conditional Correlation. We add binary variables into the dynamic correlation equation, indicating the occurrence of extreme return on the stock-exchange index in the lower 1%, 5% and 10% quantile. The conclusions vary, depending whether we consider trade on the local bitcoin exchanges or in the global one. We conclude that bitcoin was treated as a safe haven asset only in the case of Venezuela and investments in bolivars. In the case of local investments in Japan and China bitcoin behaved as a diversifier. In the bitcoin-friendly economies of Sweden and Estonia it acted as a weak hedge. In the case of the USD trade, the results suggest that bitcoin is a weak hedge with respect to all of the analyzed markets.
•We study different properties of bitcoin depending on economic situation of the country and currency of trade.•Venezuela, Japan, China, Sweden and Estonia are taken into account.•We utilize multivariate stochastic volatility model with dynamic conditional correlation.•Bitcoin was treated as a weak hedge in all markets when investment in US dollars is considered.•Bitcoin was treated as safe haven in Venezuela and investment in bolivars.
The cryptocurrency market surpassed the barrier of $100 billion market capitalization in June 2017, after months of steady growth. Despite its increasing relevance in the financial world, a ...comprehensive analysis of the whole system is still lacking, as most studies have focused exclusively on the behaviour of one (Bitcoin) or few cryptocurrencies. Here, we consider the history of the entire market and analyse the behaviour of 1469 cryptocurrencies introduced between April 2013 and May 2017. We reveal that, while new cryptocurrencies appear and disappear continuously and their market capitalization is increasing (super-)exponentially, several statistical properties of the market have been stable for years. These include the number of active cryptocurrencies, market share distribution and the turnover of cryptocurrencies. Adopting an ecological perspective, we show that the so-called neutral model of evolution is able to reproduce a number of key empirical observations, despite its simplicity and the assumption of no selective advantage of one cryptocurrency over another. Our results shed light on the properties of the cryptocurrency market and establish a first formal link between ecological modelling and the study of this growing system. We anticipate they will spark further research in this direction.
•We examine the conditional dynamic volatility dynamics and conditional correlations between large cryptocurrencies.•We find evidence of bi-directional shock transmission effects between Bitcoin and ...both Ether and Litecoin.•We identify bi-directional volatility spillovers between all analysed pairwise relationships.•Evidence is presented that time-varying conditional correlations exist and are mostly positive.
Through the application of three pair-wise bivariate BEKK models, this paper examines the conditional volatility dynamics along with interlinkages and conditional correlations between three pairs of cryptocurrencies, namely Bitcoin-Ether, Bitcoin-Litecoin, and Ether-Litecoin. While cryptocurrency price volatility is found to be dependent on its own past shocks and past volatility, we find evidence of bi-directional shock transmission effects between Bitcoin and both Ether and Litecoin, and uni-directional shock spillovers from Ether to Litecoin. Finally, we identify bi-directional volatility spillover effects between all the three pairs and provide evidence that time-varying conditional correlations exist and are mostly positive.
Blockchain technologies are benefiting from significant interest in both societal and business contexts. Cryptocurrencies like Bitcoin have grown rapidly in user adoption over the past 8 years. ...However, blockchain technologies, which fuel cryptocurrencies, have the potential to extend to other business applications even more profoundly. Blockchain can be leveraged to drive innovation and increase efficiencies in new domains—including digital arts management, supply chains, and healthcare—but there remain technical, organizational, and regulatory headwinds that must be overcome before mass adoption can occur. In this article, we provide a brief history of blockchain and identify some of the key features that have enabled its popular uptake in the world of cryptocurrencies. We discuss how blockchain technologies have evolved from traditional software and web technologies and then examine their underlying strengths and evaluate new, noncryptocurrency use cases. We conclude with a look at the limitations of blockchain and present several important factors for managers considering blockchain implementation within their organizations.
•In this paper, we examine whether gold or Bitcoin is a safe haven against economic policy uncertainty (EPU).•We combine the GARCH model and quantile regression with dummy variables, which include ...the extreme situations of average condition and different quantiles.•Hedge and safe-haven roles of gold and Bitcoin against EPU shocks are correlated with their bearish or bullish market situation.•Bitcoin reacts more responsive to EPU shocks and gold maintains stability with smaller hedge and safe-haven coefficients.
Calculating the hedge and safe-haven properties of gold and Bitcoin via GARCH model and quantile regression with dummy variables. We find that: (1) Neither gold nor Bitcoin can serve as a strong hedge or safe-haven for economic policy uncertainty (EPU) at the average condition. (2) Bitcoin is more responsive to EPU shocks, while gold maintains stability with smaller hedge and safe-haven coefficients. (3) In most cases, both gold and Bitcoin can act as the weak hedge and weak safe-haven against EPU during the extreme bearish and bullish markets, which two can be considered for portfolio diversification during the normal market.
•Several machine-learning models have been tested for this Up/Down binary-classification problem.•A comparison of the state-of-art strategies in predicting the movement direction for bitcoin, ...including Random Guessing and a Momentum-Based Strategy is provided.•The tested models include Autoregressive Integrated Moving Average (ARIMA), Prophet (by Facebook), Random Forest, Random Forest Lagged-Auto-Regression, and Multi-Layer Perceptron (MLP) Neural Networks.•The MLP deep neural network has achieved the highest accuracy of 54% compared to other time-series prediction models.•Various data transformation and feature engineering have been applied in the comparison are introduced.
Many traders participate in activities known as "day-trading", trading Bitcoin against the dollar bill as the United States Dollar (USD) on very short timeframes to squeeze out profits from small market fluctuations. This paper aims to help traders decide how to best act by creating a model that can predict price movement's direction for the next 5-min time frame. Several machine-learning models have been tested for this Up/Down binary-classification problem. In this paper, we provide a comparison of the state-of-art strategies in predicting the movement direction for bitcoin, including Random Guessing and a Momentum-Based Strategy. The tested models include Autoregressive Integrated Moving Average (ARIMA), Prophet (by Facebook), Random Forest, Random Forest Lagged-Auto-Regression, and Multi-Layer Perceptron (MLP) Neural Networks. The MLP deep neural network has achieved the highest accuracy of 54% compared to other time-series prediction models. Also, in this paper, various data transformation and feature engineering have been applied in the comparison.
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