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1,505 results · page 55 of 63
Alexander Brauneis, Roland Mestel, Ryan Riordan, Erik Theissen
No abstract is available for this record.
Lennart Ante
No abstract is available for this record.
Niranjan Sapkota, Klaus Grobys
Cryptocurrencies employ different consensus protocols to verify transactions. While the Proof-of-Work consensus protocol is the most energy consuming protocol, Proof-of-Stake and Hybrid consensus protocols have been introduced which consume considerably less energy. We employ portfolio analysis to explore whether energy is a fundamental economic factor affecting cryptocurrency prices. Surprisingly, our results suggest that, on average, cryptocurrencies employing Proof-of-Work consensus protocols do not generate returns that are significantly different from those that incorporate Proof-of-Stake consensus protocols. Even more surprising is that our results show that cryptocurrencies that incorporate Hybrid consensus protocols generated significantly higher average return than the other groups. A possible explanation for that phenomenon may be that investors’ demand for cryptocurrencies that they perceive as offering more trust is larger than for those that carry potential risks of blockchain manipulation.
Tobias Burggraf
No abstract is available for this record.
Jun Deng, Huifeng Pan, Shuyu Zhang, Bin Zou
No abstract is available for this record.
Roberto Frota Décourt, Usman W. Chohan, Maria Letizia Perugini
No abstract is available for this record.
Zura Kakushadze, Willie Yu
We give an algorithm and source code for a cryptoasset statistical arbitrage alpha based on a mean-reversion effect driven by the leading momentum factor in cryptoasset returns discussed in https://ssrn.com/abstract=3245641. Using empirical data, we identify the cross-section of cryptoassets for which this altcoin-Bitcoin arbitrage alpha is significant and discuss it in the context of liquidity considerations as well as its implications for cryptoasset trading.
Simon Mayer
No abstract is available for this record.
Guglielmo Maria Caporale, Alex Plastun
Abstract This paper examines whether there exists a momentum effect after one-day abnormal returns in the cryptocurrency market. For this purpose, a number of hypotheses of interest are tested for the Bitcoin, Ethereum and Litecoin exchange rates vis-à-vis the US dollar over the period 01.01.2015–01.09.2019, specifically whether or not: (H1) the intraday behavior of hourly returns is different on abnormal days compared to normal days; (H2) there is a momentum effect on days with abnormal returns, and (H3) after one-day abnormal returns. The methods used for the analysis include various statistical methods as well as a trading simulation approach. The results suggest that hourly returns during the day of positive/negative abnormal returns are significantly higher/lower than those during the average positive/negative day. The presence of abnormal returns can usually be detected before the day ends by estimating specific timing parameters. Prices tend to move in the direction of the abnormal returns till the end of the day when it occurs, which implies the existence of a momentum effect on that day giving rise to exploitable profit opportunities. This effect (together with profit opportunities) is also observed on the following day. In two cases (BTCUSD positive abnormal returns and ETHUSD negative abnormal returns), a contrarian effect is detected instead.
Savva Shanaev, Satish Kumar Sharma, Subhakara Valluri, Arina Shuraeva
No abstract is available for this record.
Cathy Yi‐Hsuan Chen, Romeo Despres, Li Guo, Thomas Renault
No abstract is available for this record.
Marko Ogorevc
This paper is motivated by a hypothesis that the long term value of a cryptocurrency is determined by its future use as money. For a cryptocurrency to be used as a medium of payment, it has to fulfill three independent functions: medium of exchange, a unit of account, and store of value. Currently, cryptocurrencies are held for investment purposes rather than being used for transactions and thus as a medium of exchange. For cryptocurrency to become widely adopted as a means of payment, it first needs to go through a very volatile period because speculative traders see long-run future value in the cryptocurrency. In order to soften transition from speculative asset to medium of payment a trading strategy is proposed, which provides liquidity and reduces volatility. Similar to pairs trading strategy, the proposed solution is based on cointegration and performed in three steps. The main difference is that proposed solution does not include shorting, but holding cryptocurrencies, thus increasing the total available cash and adding to the equilibrium price. Results from an ongoing experiment suggest that the proposed trading strategy is appealing for about 40% of cryptocurrency investors, as the struggle against volatility problem is accompanied by significant financial gains.
Moinak Maiti, Darko Vuković, Victor Krakovich, Maneesh Kumar Pandey
The present study focuses on five cryptocurrencies co-movements physiognomies both in time and frequency domain. The present study highlighted several interesting facts related to cryptocurrencies co-movements both in time and frequency domain that have high policy and investment implications. Overall wavelet coherence diagrams clearly indicate about the very short and long contagion effect among the cryptocurrency pairs for the whole study period. The contagion effect is different at different time scales. Finally wavelet clustering diagram indicates that by investing only in XBP and BitCoin cryptocurrencies investors are not going to get any benefit from diversification. This predictable co-movements pattern among the cryptocurrencies could be the basic investment strategies to gain maximum profit by diversifying the risk in cryptocurrency investments.
Dominique Lammer, Tobin Hanspal, Andreas Hackethal
No abstract is available for this record.
Julián Andrada Félix, Adrián Fernández-Pérez, Simón Sosvilla‐Rivero
No abstract is available for this record.
Klaus Grobys, Niranjan Sapkota
We examine all available 146 Proof-of-Work-based cryptocurrencies that started trading prior to the end of 2014 and track their performance until December 2018. We find that about 60% of those cryptocurrencies were eventually in default. The substantial sums of money involved mean those bankruptcies will have an enormous societal impact. Employing cryptocurrency-specific data, we estimate a model based on linear discriminant analysis to predict such defaults. Our model is capable of explaining 87% of cryptocurrency bankruptcies after only one month of trading and could serve as a screening tool for investors keen to boost overall portfolio performance and avoid investing in unreliable cryptocurrencies.
Arash Aloosh, Samuel Ouzan
No abstract is available for this record.
A. Can Inci, Rachel Lagasse
Purpose - This study investigates the role of cryptocurrencies in enhancing the performance of portfolios constructed from traditional asset classes. Using a long sample period covering not only the large value increases but also the dramatic declines during the beginning of 2018, the purpose of this paper is to provide a more complete analysis of the dynamic nature of cryptocurrencies as individual investment opportunities, and as components of optimal portfolios. Design/methodology/approach - The mean-variance optimization technique of Merton (1990) is applied to develop the risk and return characteristics of the efficient portfolios, along with the optimal weights of the asset class components in the portfolios. Findings - The authors provide evidence that as a single investment, the best cryptocurrency is Ripple, followed by Bitcoin and Litecoin. Furthermore, cryptocurrencies have a useful role in the optimal portfolio construction and in investments, in addition to their original purposes for which they were created. Bitcoin is the best cryptocurrency enhancing the characteristics of the optimal portfolio. Ripple and Litecoin follow in terms of their usefulness in an optimal portfolio as single cryptocurrencies. Including all these cryptocurrencies in a portfolio generates the best (most optimal) results. Contributions of the cryptocurrencies to the optimal portfolio evolve over time. Therefore, the results and conclusions of this study have no guarantee for continuation in an exact manner in the future. However, the increasing popularity and the unique characteristics of cryptocurrencies will assist their future presence in investment portfolios. Originality/value - This is one of the first studies that examine the role of popular cryptocurrencies in enhancing a portfolio composed of traditional asset classes. The sample period is the largest that has been used in this strand of the literature, and allows to compare optimal portfolios in early/recent subsamples, and during the pre-/post-cryptocurrency crisis periods.
Cathy Yi‐Hsuan Chen, Christian Hafner
Cryptocurrencies lack clear measures of fundamental values and are often associated with speculative bubbles. This paper introduces a new way of testing for speculative bubbles based on StockTwits sentiment, which is used as the transition variable in a smooth transition autoregression. The model allows for conditional heteroskedasticity and fat tails of the conditional distribution of the error term, and volatility may depend on the constructed sentiment index. We apply the model to the CRIX index, for which several bubble periods are identified. The detected locally explosive price dynamics, given the specified bubble regime controlled by a smooth transition function, are more akin to the notion of speculative bubble that is driven by exuberant sentiment. Furthermore, we find that volatility increases as the sentiment index decreases, which is analogous to the commonly called leverage effect.
Shaen Corbet, Veysel Eraslan, Brian M. Lucey, Ahmet Şensoy
No abstract is available for this record.
Nicola Borri, Kirill Shakhnov
No abstract is available for this record.
Yukun Liu, Aleh Tsyvinski, Xi Wu
ABSTRACT We find that three factors—cryptocurrency market, size, and momentum—capture the cross‐sectional expected cryptocurrency returns. We consider a comprehensive list of price‐ and market‐related return predictors in the stock market and construct their cryptocurrency counterparts. Ten cryptocurrency characteristics form successful long‐short strategies that generate sizable and statistically significant excess returns, and we show that all of these strategies are accounted for by the cryptocurrency three‐factor model. Lastly, we examine potential underlying mechanisms of the cryptocurrency size and momentum effects.
Weiyi Liu, Xuan Liang, Guowei Cui
No abstract is available for this record.
Wolfgang Karl Härdle, Campbell R. Harvey, Raphael Constantin Georg Reule
Abstract Cryptocurrency refers to a type of digital asset that uses distributed ledger, or blockchain, technology to enable a secure transaction. Although the technology is widely misunderstood, many central banks are considering launching their own national cryptocurrency. In contrast to most data in financial economics, detailed data on the history of every transaction in the cryptocurrency complex are freely available. Furthermore, empirically oriented research is only now beginning, presenting an extraordinary research opportunity for academia. We provide some insights into the mechanics of cryptocurrencies, describing summary statistics and focusing on potential future research avenues in financial economics.