Blockchain Papers

Jun Aoyagi, Takahiro Hattori

No abstract is available for this record.

Tijana Šoja, Chamil W. Senarathne

This paper examines whether it is advisable to include some portion of Bitcoin in a portfolio of traditional financial assets. The goal is to explore whether Bitcoin could be a good source of diversification from the perspective of a global investor. Two portfolios have been created for this purpose: a portfolio aimed at minimizing risk and a portfolio designated as "aggressive" that offers higher rates of daily return but also a higher risk. Portfolios were created using Markowitz's optimization theory and included traditional instruments (stocks, bonds, gold) and Bitcoin. In portfolio optimization, high-frequency data (daily data) were used. The analysed period is from the end of July 2010 to the end of June 2019, which is the period of active Bitcoin trading. The results show that Bitcoin could be a good source of diversification for a portfolio that consists of traditional financial instruments, for investors trading daily. It could be a good source of diversification for the risk-averse investor and those investors who have a higher risk appetite. Considering the high volatility of Bitcoin, the investors should be very careful when they decide to include Bitcoin in a portfolio.

Long Chen, Lin William Cong, Yizhou Xiao

We introduce economic research on blockchains and its recent advances. In particular, we highlight the (i) unifying concepts on blockchain as a decentralized consensus and its core benefits, (ii) equilibrium characterizations and allegedly irreducible tensions among consensus formation, decentralization, and scalability, (iii) major issues including network security, overconcentration, energy consumption and sustainability, adoption, multi-party computation and encryption, smart contracting, and information distribution and aggregation, and (iv) future directions concerning blockchains and their applications such as informational and agency issues, as well as game-theoretical and mechanism design approaches to blockchain protocols.

Stefano Bistarelli, Alessandra Cretarola, Gianna Figà‐Talamanca, Ivan Mercanti · 5 authors

No abstract is available for this record.

Célestin Coquidé, José Lages, Dima L. Shepelyansky

We construct the Google matrices of bitcoin transactions for all year quarters during the period of January 11, 2009 till April 10, 2013. During the last quarters the network size contains about 6 million users (nodes) with about 150 million transactions. From PageRank and CheiRank probabilities, analogous to trade import and export, we determine the dimensionless trade balance of each user and model the contagion propagation on the network assuming that a user goes bankrupt if its balance exceeds a certain dimensionless threshold $\kappa$. We find that the phase transition takes place for $\kappa 0.55$ almost all users remain safe. We find that even on a distance from the critical threshold $\kappa_c$ the top PageRank and CheiRank users, as a house of cards, rapidly drop to the bankruptcy. We attribute this effect to strong interconnections between these top users which we determine with the reduced Google matrix algorithm. This algorithm allows to establish efficiently the direct and indirect interactions between top PageRank users. We argue that this study models the contagion on real financial networks.

Alexander Brauneis, Roland Mestel, Ryan Riordan, Erik Theissen

No abstract is available for this record.

Shaen Corbet, Brian M. Lucey, Maurice Peat, Samuel A. Vigne

No abstract is available for this record.

Weili Chen, Zibin Zheng, Mingjie Ma, Jiajing Wu · 6 authors

Bitcoin is a decentralised digital currency which attracts growing interest over recent years. Much research from different subjects emerged as bitcoin is a multidisciplinary product. Among all these studies, the interpretation of the drastic fluctuation of bitcoin price attracts a great attention. Many influence factors of bitcoin price were found. However, seldom research reveals the dependence structure between price and its influence factors. By selecting ten interpretable influence factors from the bitcoin network and using copula theory, we find that the bitcoin price has different correlation structures with its influence factors. These findings provide new insights into the behaviour of miners, users, and coins in the bitcoin system, thus leading to meaningful implications for policymakers, investors and risk managers dealing with bitcoin and other cryptocurrencies.

Shaen Corbet, Douglas J. Cumming, Brian M. Lucey, Maurice Peat · 5 authors

No abstract is available for this record.

Michael L. Mark, Jan Šíla, Thomas A. Weber

We construct a ‘reflexivity’ index to measure the activity generated endogenously within a market for cryptocurrencies. For this purpose, we fit a univariate self-exciting Hawkes process with two classes of parametric kernels to high-frequency trading data. A parsimonious model of both endogenous and exogenous dynamics enables a direct comparison with exchanges for traditional asset classes, in terms of identified branching ratios. We also formulate a ‘Hawkes disorder problem,’ as generalization of the established Poisson disorder problem, and provide a simulation-based approach to determining an optimal observation horizon. Our analysis suggests that Bitcoin mid-price dynamics feature long-memory properties, well explained by the power-law kernel, at a level of criticality similar to fiat-currency markets.

Timothy Peterson

No abstract is available for this record.

Gülüzar Kurt Gümüş, Yusuf Gümüş, Ayşegül Çimen

No abstract is available for this record.

Zeeshan Muzammal, Muhammad Umar Janjua, Waseem Abbas, Falak Sher

No abstract is available for this record.

Argimiro Arratia, Albert X. López-Barrantes

In early 2018 prices peaked at USD 20,000 and, almost two years later, we still continue debating if cryptocurrencies can actually become a currency for the everyday life or not. From the economic point of view, and playing in the field of behavioral finance, this paper analyses the relation between prices and the search interest on Bitcoin since 2014. We questioned the forecasting ability of Google Trends for the behavior of price by performing linear and nonlinear dependency tests, and exploring performance of ARIMA and Neural Network models enhanced with this social sentiment indicator. Our analyses and models are founded upon a set of statistical properties common to financial returns that we establish for Bitcoin, Ethereum, Ripple and Litecoin.

Theodore Panagiotidis, Thanasis Stengos, Orestis Vravosinos

We examine the significance of fourty-one potential covariates of bitcoin returns for the period 2010–2018 (2872 daily observations). The recently introduced principal component-guided sparse regression is employed. We reveal that economic policy uncertainty and stock market volatility are among the most important variables for bitcoin. We also trace strong evidence of bubbly bitcoin behavior in the 2017–2018 period.

Jun Deng, Huifeng Pan, Shuyu Zhang, Bin Zou

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.

Nikolai Zaitsev

Report presents analysis of empirical distribution of future returns of bitcoin (BTC) from BTUSD inverse option prices. Logistic pdf is chosen as underlying distribution to fit option prices. The result is satisfactory and suggests that these prices can be described with just three or even one parameter. Fitted Logistic pdf matches forward price movements upto a scaling factor. Nevertheless, this observation stands alone and does not allow stochastic description of underlying prices with logistic pdf in similar fashion as it is done within Black-Scholes modelling framework. Put-call parity relationship is derived connecting prices of vanilla inverse options and futures.

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.

Cho‐Hoi Hui, Chi‐Fai Lo, Po-Hon Chau, Andrew L. Wong

No abstract is available for this record.

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.

Rick Bohte, Luca Rossini

This paper studies the forecasting ability of cryptocurrency time series. This study is about the four most capitalized cryptocurrencies: Bitcoin, Ethereum, Litecoin and Ripple. Different Bayesian models are compared, including models with constant and time-varying volatility, such as stochastic volatility and GARCH. Moreover, some crypto-predictors are included in the analysis, such as S\&P 500 and Nikkei 225. In this paper the results show that stochastic volatility is significantly outperforming the benchmark of VAR in both point and density forecasting. Using a different type of distribution, for the errors of the stochastic volatility the student-t distribution came out to be outperforming the standard normal approach.

Anthony Ngunyi, Simon Mundia, Cyprian Ondieki Omari

Cryptocurrencies have become increasingly popular in recent years attracting the attention of the media, academia, investors, speculators, regulators, and governments worldwide. This paper focuses on modelling the volatility dynamics of eight most popular cryptocurrencies in terms of their market capitalization for the period starting from 7th August 2015 to 1st August 2018. In particular, we consider the following cryptocurrencies; Bitcoin, Ethereum, Litecoin, Ripple, Moreno, Dash, Stellar and NEM. The GARCH-type models assuming different distributions for the innovations term are fitted to cryptocurrencies data and their adequacy is evaluated using diagnostic tests. The selected optimal GARCH-type models are then used to simulate out-of-sample volatility forecasts which are in turn utilized to estimate the one-day-ahead VaR forecasts. The empirical results demonstrate that the optimal in-sample GARCH-type specifications vary from the selected out-of-sample VaR forecasts models for all cryptocurrencies. Whilst the empirical results do not guarantee a straightforward preference among GARCH-type models, the asymmetric GARCH models with long memory property and heavy-tailed innovations distributions overall perform better for all cryptocurrencies.