Blockchain Papers

James Yae, George Zhe Tian

No abstract is available for this record.

Lai T. Hoang, Dirk G. Baur

No abstract is available for this record.

Hubert Anciaux, Christophe Desagre, Nicolas Nicaise, Mikaël Petitjean

No abstract is available for this record.

Ilias Filippou, David E. Rapach, Christoffer Thimsen

No abstract is available for this record.

Yizhou Cao, Min Dai, Steven Kou, Lewei Li · 5 authors

Abstract Existing cryptocurrencies are too volatile to be used as currencies for daily payments. Stablecoins, which are cryptocurrencies pegged to other stable financial assets such as the US dollar, are desirable for payments within blockchain networks, whereby being often called the “Holy Grail of cryptocurrency.” By using the option pricing theory and the Ethereum platform that allows running smart contracts, we design several dual‐class structures that are written on the ETH cryptocurrency and offer a fixed‐income crypto asset (Class A coin), a stablecoin (Class A′ coin) pegged to a traditional currency, and leveraged investment instruments (Class B and B′ coins). Our investigation of the values of stablecoins in the presence of jump risk and black swan‐type events shows the robustness of the design. The design has been implemented on the Ethereum platform.

Roman Matkovskyy, Akanksha Jalan

Dans cette étude, nous quantifions et analysons la dépendance dynamique entre les rendements du marché des bitcoins aux États-Unis, dans la zone euro, au Royaume-Uni et au Japon et l’inflation réalisée et inattendue, sous réserve de différents états du marché et de diverses nuances d’inflation. En utilisant une régression quantile sur quantile, nous étudions les propriétés de couverture du bitcoin contre l’inflation, offrant ainsi un nouveau regard sur le puzzle du retour de l’inflation du point de vue des investissements alternatifs. Nous constatons que tandis que les marchés haussiers du Royaume-Uni, de l’euro et du bitcoin japonais facilitent la couverture contre l’inflation en offrant des rendements plus élevés, le marché du bitcoin USD se comporte moins bien avec l’inflation. En général, nos résultats indiquent une relation asymétrique entre l’inflation, à la fois réalisée et inattendue, et les investissements alternatifs tels que le marché du bitcoin.

Konstantin Häusler, Hongyu Xia

Abstract Several cryptocurrency (CC) indices track the dynamics of the rising CC sector, and soon ETFs will be issued on them. We conduct a qualitative and quantitative evaluation of the currently existing CC indices. As the CC sector is not yet consolidated, index issuers face the challenge of tracking the dynamics of a fast-growing sector that is under continuous transformation. We propose several criteria and various measures to compare the indices under review. Major differences between the indices lie in their weighting schemes, their coverage of CCs and the number of constituents, the level of transparency, and thus, their accuracy in mapping the dynamics of the CC sector. Our analysis reveals that simple market cap-weighted indices outperform their competitors. Interestingly, increasing the number of constituents does not automatically lead to a better fit of the CC sector. All codes are available on "Image missing".

Daniele Bianchi, Mykola Babiak

We investigate the dynamics of daily realised returns and risk premiums for a large cross-section of cryptocurrency pairs through the lens of an Instrumented Principal Component Analysis (IPCA) (see Kelly et al., 2019). We show that a model with three latent factors and time-varying factor loadings significantly outperforms a benchmark model with observable risk factors: the total (predictive) R2 from the IPCA is 17.2% (2.9%) for individual returns, against a benchmark 9.6% (-0.02%) obtained from a model with six observable risk factors explored in previous literature. By looking at the characteristics that significantly matter for the dynamics of risk premiums, we provide robust evidence that liquidity, size, reversal, and both market and downside risks represent the main driving factors behind expected returns. These results hold for both individual assets and characteristic-based portfolios, pre and post the Covid-19 outbreak, and for weekly individual and portfolio returns.

Savva Shanaev, Binam Ghimire

No abstract is available for this record.

Lee A. Smales

Purpose Motivated by the lure of cryptocurrencies for retail investors, whose concentrated holdings are particularly exposed to price crash risk, this paper aims to study the relationship between investor attention and crash risk for a range of cryptocurrencies. Design/methodology/approach This study adopts a quantile regression approach to determine the effect of investor attention on crash risk. Crash risk is measured using the negative coefficient of skewness and down up volatility. Findings This study finds that the connection is concentrated in the tails of the crash risk distribution. Investor attention has a positive relationship with crash risk when crash risk is low (below-median quantiles) and negative when crash risk is high (above-median). The findings are consistent for different measures of crash risk, for alternate internet searches and for a panel of large cryptocurrencies in addition to Bitcoin. This study also notes seasonality in crash risk, with higher crash risk during the June–August period and lower crash risk in the Halloween period that runs from November to April. Originality/value The results provide insights that are not apparent in previous analyses of cryptocurrency price crash risk. The results are particularly important for retail investors, who constitute a large portion of the cryptocurrency market, as they tend to hold concentrated investments and so a price crash of a single asset may have a large bearing on their wealth.

Yosef Bonaparte

No abstract is available for this record.

Walid Ben Omrane, Khaled Guesmi, Qianru Qi, Samir Saadi

No abstract is available for this record.

Winston Wei Dou, Xiang Fang, Andrew W. Lo, Harald Uhlig

No abstract is available for this record.

Jéfferson Augusto Colombo, Fernando Cruz, Luis H. Z. Paese, Renan Xavier Cortes

No abstract is available for this record.

Andria van der Merwe

No abstract is available for this record.

Li Guo, Bo Sang, Jun Tu, Yu Wang

No abstract is available for this record.

Christine A. Parlour

No abstract is available for this record.

Sean Foley, Simeng Li, Hamish Malloch, Jiří Švec

No abstract is available for this record.

Abdollah Ah Mand

This study investigates how uncertainty surrounding cryptocurrency affects cryptocurrency return (CR) by employing various wavelet techniques. To this end, we concentrate on the recently published cryptocurrency uncertainty index (UCRY) and the top eight cryptocurrencies by virtue of market capitalization for the period from December 30, 2013, until February 21, 2021. Our results show that the UCRY index strongly predicts CR. In particular, the UCRY index has a leading position in all the frequencies for all cryptocurrencies in our sample. Additionally, when the impacts of economic policy uncertainty and the volatility index are eliminated, the significant co-movement of UCRY-CR stays unchanged for short-, medium-, and long-term investment horizons. Thus, we conclude that the UCRY-CR relationships are both persistent and pervasive. Our study contributes to the literature on the relationships between cryptocurrency and market uncertainties as well as to investors who use uncertainty indices to design their investment strategies for their portfolios.

Victoria Dobrynskaya

This article considers a variety of highly diversified cross-sectional momentum and reversal strategies, with sorting and holding periods from one week up to two years. In a sample of the 2,000 largest cryptocurrencies during the period 2014–2020, we identify positive momentum on short horizons up to two to four weeks and a significant reversal on longer horizons beyond one month. The reversal effect becomes more pronounced once we expand the sorting and/or holding periods. Momentum and, particularly, reversal returns are economically large, statistically significant, and generally not exposed to standard cryptocurrency risk factors. The main drivers of the reversal effect are “past loser” cryptocurrencies. The switching of momentum into reversal occurs after approximately one month—much quicker than the equity market, and evidence of the “faster metabolism of cryptocurrencies.”

Zhuoming Gu, Dan Lin, Jiatao Zheng, Jiajing Wu · 5 authors

No abstract is available for this record.

Alex Ferko, Amani Moin, Esen Onur, Michael A. Penick

No abstract is available for this record.

George Milunovich, Seung Ah Lee

Abstract About 99% of cryptocurrency trades occur on organized exchanges with many investors subsequently keeping their digital assets in accounts with cryptocurrency markets. This generates exposure to the risk of exchange closures. We construct a database containing eight key characteristics on 238 cryptocurrency exchanges and employ machine learning techniques to predict whether a cryptocurrency market will remain active or whether it will go out of business. Both in‐sample and out‐of‐sample measures of forecasting performance are computed and ranked for four popular machine learning algorithms. Although all four models produce satisfactory classification accuracy, our best model is a random forest classifier. It reaches accuracy of 90.4% on training data and 86.1% on a test dataset. From the list of predictors, we find that exchange lifetime, transacted volume, and cyber‐security measures such as security audit, cold storage, and bug bounty programs rank high in terms of feature importance across multiple algorithms. On the other hand, whether an exchange has previously experienced a security breach does not rank highly according to its contribution to classification accuracy.

Zehua Zhang, Ran Zhao

No abstract is available for this record.