Blockchain Papers

Imtiaz Sifat

No abstract is available for this record.

Simon Trimborn, Yang Li

No abstract is available for this record.

N. Passalis, Solon Seficha, Avraam Tsantekidis, Anastasios Tefas

No abstract is available for this record.

Jianqin Hang, Xu Zhang

This study proposes a novel approach that incorporates rolling‐window estimation and a quantile causality test. Using this approach, Google Trends and Bitcoin price data are used to empirically investigate the time‐varying quantile causality between investor attention and Bitcoin returns. The results show that the parameters of the causality tests are unstable during the sample period. The results also show strong evidence of quantile‐ and time‐varying causality between investor attention and Bitcoin returns. Specifically, our results show that causality appears only in high volatility periods within the time domain, and causality presents various patterns across quantiles within the quantile domain.

Bruce Mizrach

Stable coins are not very stable. Cash collateralized coins are more stable, but the overall failure rate is similar to tokens that are not designed to be stable. USD Coin, Tether and Dai have the largest Ethereum market shares, and they have an average velocity nearly three times higher than M1. Centralized and decentralized exchanges are the most active nodes and largest holders on the blockchain. Four of the top ten tokens have Herfindahl indices higher than the U.S. banking system. Median gas fees for Tether rose more than twelve times over the last two years, and nearly twenty times for USD Coin. Transactions of under 50,000 USD can generally be done more cheaply offchain. 24 hour exchange turnover in Tether is nearly 60 billion USD. This is comparable to the daily volume at the NYSE and eight times the daily flow in money market mutual funds. Narrow bid-ask spreads and depth have attracted HFT participation approaching 50%

T. Kikuchi, Toranosuke Onishi, Kenichi Ueda

We present positive evidence of price stability of cryptocurrencies as a medium of exchange. For the sample years from 2016 to 2020, the prices of major cryptocurrencies are found to be stable, relative to major financial assets. Specifically, after filtering out the less-than-one-month cycles, we investigate the daily returns in US dollars of the major cryptocurrencies (i.e., Bitcoin, Ethereum, and Ripple) as well as their comparators (i.e., major legal tenders, the Euro and Japanese yen, and the major stock indexes, S&P 500 and MSCI World Index). We examine the stability of the filtered daily returns using three different measures. First, the Pearson correlations increased in later years in our sample. Second, based on the dynamic time-warping method that allows lags and leads in relations, the similarities in the daily returns of cryptocurrencies with their comparators have been present even since 2016. Third, we check whether the cumulative sum of errors to predict cryptocurrency prices, assuming stable relations with comparators' daily returns, does not exceeds the bounds implied by the Black-Scholes model. This test, in other words, does not reject the efficient market hypothesis.

Carol Alexander, Jun Deng, Bin Zou

We consider the hedging problem where a futures position can be automatically liquidated by the exchange without notice. We derive a semi-closed form for an optimal hedging strategy with dual objectives - to minimise both the variance of the hedged portfolio and the probability of liquidations due to insufficient collateral. The optimal solution depends on the statistical characteristics of the spot and futures extreme returns and parameters that characterise the hedger by loss aversion, choice of leverage and collateral management. An empirical analysis of bitcoin shows that the optimal strategy combines superior hedge effectiveness with a reduction in the probability of liquidation. We compare the performance of seven major direct and inverse hedging instruments traded on five different exchanges, based on minute-level data. We also link this performance to novel speculative trading metrics, which differ markedly between venues.

Zhiyong Cheng, Jun Deng, Tianyi Wang, Mei Yu

Using the generalized extreme value theory to characterize tail distributions, we address liquidation, leverage and optimal margins for bitcoin long and short futures positions. The empirical analysis of perpetual bitcoin futures on BitMEX shows that (1) daily forced liquidations to outstanding futures are substantial at 3.51% and 1.89% for long and short; (2) investors got forced liquidation do trade aggressively with average leverage of 60X; and (3) exchanges should elevate current 1% margin requirement to 33% (3X leverage) for long and 20% (5X leverage) for short to reduce the daily margin call probability to 1%. Our results further suggest that normality assumption on return significantly underestimates optimal margins. Policy implications are also discussed.

Maximilian Schulte, Mathias Eggert

No abstract is available for this record.

Thabo J. Gopane

No abstract is available for this record.

Diogo de Prince, Renan Gomes Mendes Diniz, Leandro Maciel

No abstract is available for this record.

Vasilios Plakandaras, Elie Bouri, Rangan Gupta

Previous studies have provided evidence that trade-related uncertainty tends to predict an increase in Bitcoin returns. In this paper, we extend the related literature by examining whether the information on the US–China trade war can be used to forecast the future path of Bitcoin returns, controlling for various explanatory variables. We apply ordinary least square (OLS) regression, support vector regression (SVR) and least absolute shrinkage and selection operator (LASSO) techniques that stem from the field of machine learning, and we find weak evidence of the role of the trade war in forecasting Bitcoin returns. Given that out-of-sample tests are more reliable than in-sample tests, our results tend to suggest that future Bitcoin returns are unaffected by trade-related uncertainties, and investors can use Bitcoin as a safe haven in this context.

Chuanhai Zhang, Huan Ma, Gideon Bruce Arkorful, Zhe Peng

No abstract is available for this record.

Jawad Saleemi

The cryptocurrency market is emerging as a new asset class for the investment. As the traditional asset prices are often noted to be influenced by the liquidity risk, this study links the cryptocurrency liquidity cost to its yields. Pre-pandemic uncertainty, the Bitcoin liquidity cost was found to be priced in its returns during the same trading session. Post-pandemic crisis, the relationship was changed. The liquidity cost was reported not to be priced in the Bitcoin returns at the time of same trading session. Post-pandemic crisis, however, the liquidity cost imposed by the liquidity supplier on day t − 1 was noted to be priced in the Bitcoin return of day t . In the cryptocurrency market, this study quantifies the effects on the Bitcoin returns of its liquidity cost, and if such effects vary pre- and post-pandemic uncertainty.

Xu Zhang, Zhijing Ding

Since the advent of Bitcoin, the cryptocurrency market has become an important financial market. However, due to the existence of the cryptocurrency bubble, investors face more difficulties in risk portfolios. We adopt wavelet packet decomposition, nonlinear Granger causality test, risk spillover network, and STVAR model; retain the mature research of multiscale systemic risk based on time and frequency; and thus extend systemic risk to different regimes. We found that when frequency is combined with regimes, the risk spillover center will undergo subversive changes in the long run. We also proposed that BTC will be more robust at extreme values (like longest and shortest periods), while cryptocurrencies with smaller market capitalization will be stronger in the medium term. At the same time, the recession period will also spur on it.

Tarek Sadraoui, Ahlem Nasr, Nidhal Mgadmi

Our innovative collaboration of our research work is to study and test the effects of exchange rates/USD and economic factors such as financial openness (Kopen), inflation rate, internet penetration and rate of economic growth on the bitcoin price of developing countries, during 2010-2017. The rise of the new phenomena of crypto currency in the world raises questions about the importance of bitcoin prices compared to the exchange rate and financial openness. Using panel data, we show that the exchange rate has a positive and statistically significant impact on the price of bitcoin and financial openness has exerted a negative and significant effect on bitcoin price. Specifically, we determine the causal relationship between the bitcoin price and its factors using Granger causality test.

Ester Félez‐Viñas, Sean Foley, Jonathan R. Karlsen, Jiří Švec

No abstract is available for this record.

Lai T. Hoang, Dirk G. Baur

No abstract is available for this record.

Min Dai, Steven Kou, Shuaijie Qian, Ling Qin

No abstract is available for this record.

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.

Cyprian Ondieki Omari, Anthony Ngunyi

This paper implements the analysis of volatility behaviour of the eight major cryptocurrencies (Bitcoin, Ethereum, Ripple, Litecoin, Monero, Stellar, Dash and Tether) for the period starting from October 13th 2015 to November 18th 2019. The GARCH-type models with heavy-tailed distributions are fitted to filter the conditional volatility exhibited by cryptocurrencies. Extreme value analysis based on the peak over threshold approach is then used to model the extreme tail behaviour of the cryptocurrencies. The predictive performance of the GARCH-EVT model in forecasting Value-at-Risk is evaluated at both 5% and 1% levels of significance. The backtesting results demonstrate the superiority of the GARCH-EVT model in both out-of-sample forecasts and goodness-of-fit properties to cryptocurrency returns and forecasting Value-at-Risk. Overall, the empirical results of this study recommend the heavy-tailed GARCH-EVT based model for modelling and forecasting the volatility of cryptocurrencies.

Cem Çağrı Dönmez, Doruk Şen, Ahmet Fatih Dereli, Muhammed Bilal Horasan · 6 authors

Recent developments in global financial markets revealed that cryptocurrencies experienced rapid growth due to the popularity of blockchain technology and its evolving position in the digital finance industry. The rise of cryptocurrencies led economists to question generally accepted financial practices. Particularly the interaction between two different types of financial markets arose as a hot research topic to discover specific relationships and differences between major cryptocurrencies and fiat currencies. Therefore, this article aims to examine analyze by attaching importance to the Bitcoin to investigate significant linkages and analyze critical direct and indirect connections. In this research, Bitcoin—which is known as the most prominent cryptocurrency on the market—and 50 different conventional currencies are taken into consideration by applying cross-correlation, HT (hierarchical tree), and MST (minimum spanning tree) methods. The results of this work can be utilized by academicians and economists for further research related to the subject.

Sayar Karmakar, Rıza Demirer, Rangan Gupta

No abstract is available for this record.