Blockchain Papers

Simon Trimborn, Yang Li

No abstract is available for this record.

Victoria Dobrynskaya, Mikhail Dubrovskiy

The authors consider a variety of cryptocurrency and equity risk factors as potential forces that drive cryptocurrency returns and carry risk premiums. In a cross-section of 2,000 biggest cryptocurrencies during 2014–2020, only downside market risk, cryptocurrency size and cryptocurrency policy uncertainty factors are systematically priced with significant premiums. Cryptocurrencies, which have greater exposures to these factors, yield higher returns subsequently. Equity market risk, particularly equity downside market risk, appears to be more important than cryptocurrency market risk, suggesting greater linkages between cryptocurrency and equity markets than we used to think. Global and the US equity factors are more relevant for the cryptocurrency market than local factors from other markets. However, there is no evidence that exposure to momentum, volatility and Fama–French factors is compensated by higher returns.

Alexander Barrett

This work is a comparative study of different univariate and multivariate time series predictive models as applied to Bitcoin, other cryptocurrencies, and other related financial time series data. ARIMA models, long regarded as the gold standard of univariate financial time series prediction due to both its flexibility and simplicity, are used a baseline for prediction. Given the highly correlative nature amongst different cryptocurrencies, this work aims to show the benefit of forecasting with multivariate time series models—primarily focusing on a novel parameter optimization of VARIMA models outlined in this paper. These models are trained on 3 years of historical data, aggregated from different cryptocurrency exchanges by Coinmarketcap.com, which includes: daily average prices and trading volume. Historical time series data of traditional market data, including the stock Nvidia, the de facto leading manufacture of gaming GPU’s, is also analyzed in conjunction with cryptocurrency prices, as gaming GPU’s have played a significant role in solving the profitable SHA256 hashing problems associated with cryptocurrency mining and have seen equivalently correlated investor attention as a result. Models are trained on this historical data using moving window subsets, with window lengths of 100, 200, and 300 days and forecasting 1 day into the future. Validation of this prediction against the actually price from that day are done with following metrics: Directional Forecasting (DF), Mean Absolute Error (MAE), and Mean Squared Error (MSE).

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.

Victor von Wachter, Johannes Rude Jensen, Omri Ross

Decentralized financial (DeFi) applications on the Ethereum blockchain are highly interoperable because they share a single state in a deterministic computational environment. Stakeholders can deposit claims on assets, referred to as 'liquidity shares', across applications producing effects equivalent to rehypothecation in traditional financial systems. We seek to understand the degree to which this practice may contribute to financial integration on Ethereum by examining transactions in 'composed' derivatives for the assets DAI, USDC, USDT, ETH and tokenized BTC for the full set of 344.8 million Ethereum transactions computed in 2020. We identify a salient trend for 'composing' assets in multiple sequential generations of derivatives and comment on potential systemic implications for the Ethereum network.

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.

Ndeye Fatou Sene, Mamadou Abdoulaye Konté, Jane Aduda

The objective of this study is, to show the importance of incorporating jumps in both returns and volatility dynamics for Bitcoin. For that purpose, we introduce the Double Exponential Jump-Diffusion model with Stochastic Volatility (DEJDSVJ) that contains asymmetric jumps. The use of the Markov Chain Monte Carlo methods for estimation has proved the meaningful presence of jumps in Bitcoin price and volatility. Moreover, based on the Bitcoin options market, a comparison between the underlying model, the Double Exponential Jump Diffusion model (DEJD) with Stochastic Volatility (no Jumps) and the Stochastic Volatility (SV) shows the goodness of the DEJDSVJ model’s calibration over others for pricing Bitcoin options.

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.

William J. Luther, Nikhil Sridhar

No abstract is available for this record.

Bo Tang, Yang You

Cryptocurrency prices differ across countries, and these price deviations fluctuate widely. Our paper provides evidence that distrust toward domestic authorities can explain the dynamics of local cryptocurrency prices relative to the U.S. dollar price. The price deviation rises after an outbreak of a financial crisis, political scandal, or socioeconomic event that undermines confidence in the domestic government or economy. With panel regressions, we show that Bitcoin price deviations increase by 1.8% when the institutional failure index rises by one standard deviation. These price responses are much stronger in countries with lower trust levels and during periods with tighter capital controls.

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

No abstract is available for this record.

Chika A. Anisiuba, Obiamaka P. Egbo, Felix C. Alio, Chuka Uzoma Ifediora · 7 authors

We analyzed cryptocurrency dynamics in the global U.S. dollar–denominated market and the emerging market economies (EMEs) with a view to ascertaining whether activities in these markets are predominantly shaped by reinforcement or substitution effect. Cryptocurrencies analyzed include the Bitcoins, Ethereum, Litecoin, Steller, Bitcoin Cash, and USD Tether. The results suggest that, on average, correlation between digital assets in the cryptocurrencies’ ecosystem is positive. However, there is evidence of an outlier with respect to the USD Tether (USDT) in the global market, revealing that the USDT is negatively associated with all other cryptocurrencies. This is supported by the dynamic regression results that provided evidence of reinforcement effect in favor of the USDT in the global crypto market, thus confirming the status of the USDT as “Stablecoin” as it is pegged 1:1 to USD. In the global market context, the results also revealed that USDT/USD returns had identical outliers that could portend lesser chances of extreme gains or losses compared with suggestions of extreme gains or losses in the EMEs. Furthermore, USDT did not seem to have similar evolution in the EMEs where it had relatively marginal influence in the markets. The vector error correction (VEC) estimate showed mixed results between Altcoins in all the markets; moreover, our finding showed that reinforcement effects hold in favor of Steller (XLM) both in the Russian ruble and Indian rupee crypto markets, whereas the Chinese yuan crypto market was predominantly characterized by substitution effect in favor of Bitcoin.

Ahmed Saied El-Berawi, Mohamed Belal, Mahmoud Mahmoud Abd Ellatif

This paper proposes a deep learning based predictive model for forecasting and classifying the price of cryptocurrency and the direction of its movement. These two tasks are challenging to address since cryptocurrencies prices fluctuate with extremely high volatile behavior. However, it has been proven that cryptocurrency trading market doesn’t show a perfect market property, i.e., price is not totally a random walk phenomenon. Based upon this, this study proves that the price value forecast and price movement direction classification is both predictable. A recurrent neural networks based predictive model is built to regress and classify prices. With adaptive dynamic features selection and the use of external dependable factors with a potential degree of predictability, the proposed model achieves unprecedented performance in terms of movement classification. A naïve simulation of a trading scenario is developed and it shows a 69% profitability score a cross a six months trading period for bitcoin.

Jiarui Zhang

The cryptocurrency market recently gained a lot of attention from investors. But, its volatility has been acting as a disincentive to investment. Volatility plays an important role in shaping market riskiness and investment behavior. We study the volatility of the Ethereum (ETH) cryptocurrency from the following perspectives. The first goal of this study is to identify risk-seeking behavior in the ETH cryptocurrency market. We examine this propensity by measuring the effect of the volatility of Ethereum on the total ETH assets. This investigation also takes the form of a case-study of an unexpected ETH fund-stolen event, DAO Hack, and the hard fork treatment. We also forecast a downward volatility trend in the near future based on Autoregressive models. This is the first study to analyze DAO Hack with empirical methods and marks the starting point for more rigorous models to predict the volatility of Ethereum.

Kwok Ping Tsang, Zichao Yang

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.

Hubert Anciaux, Christophe Desagre, Nicolas Nicaise, Mikaël Petitjean

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.

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.

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.