Blockchain Papers

Carol Alexander, Daniel F. Heck

No abstract is available for this record.

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

No abstract is available for this record.

Darko Blazevic, Fredrik Marcusson

This study examines and compares the volatility in sample fit and out of sample forecast of four different heteroscedasticity models, namely ARCH, GARCH, EGARCH and GJR-GARCH applied to Bitcoin, Ethereum and Ripple. The models are fitted over the period from 2016-01-01 to 2019-01-01 and then used to obtain one day rolling forecasts during the period from 2018-01-01 to 2019-01-01. The study investigates three different themes consisting of the modelling framework structure, complexity of models and the relation between a good in sample fit and good out of sample forecast. AIC and BIC are used to evaluate the in sample fit while MSE, MAE and R2LOG are used as loss functions when evaluating the out of sample forecast against the chosen Parkinson volatility proxy. The results show that a heavier tailed reference distribution than the normal distribution generally improves the in sample fit, while this generality is not found for the out of sample forecast. Furthermore, it is shown that GARCH type models clearly outperform ARCH models in both in sample fit and out of sample forecast. For Ethereum, it is shown that the best fitted models also result in the best out of sample forecast for all loss functions, while for Bitcoin non of the best fitted models result in the best out of sample forecast. Finally, for Ripple, no generality between in sample fit and out of sample forecast is found.

Tobias Burggraf

No abstract is available for this record.

Dirk G. Baur, Josua Oll

No abstract is available for this record.

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.

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.

William J. Luther

No abstract is available for this record.

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

No abstract is available for this record.

Shaen Corbet, Brian M. Lucey, Larisa Yarovaya

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.

Svetlana Saksonova, Irina Kuzmina-Merlino

This paper considers the development of attractive strategies featuring cryptocurrency assets, considering their costs and potential risks. The object of analysis in this paper is cryptocurrency as an investment instrument. The main hypothesis of the research is that modern portfolio theory can be applied to cryptocurrency investments to design an investment portfolio with appropriate risk and profitability characteristics. The authors of the paper: (i) place cryptocurrencies in the context of modern financial market and financial technology development;

Hélène Syed Zwick, Sarfaraz Ali Shah Syed

This study applies threshold regression model in a bivariate framework to explore the nonlinear and long-term relationship among daily Bitcoin and gold prices over the period April 2010 to December 2018. Our empirical results are threefold: first, we show that gold is a significant predictor of Bitcoin prices. Second, we find evidence of a non-linear relationship between Bitcoin and gold prices characterized rather by a two-regime relationship with a structural break occurring in October 2017. Third, we explain the existence at before the break, there is statistically significant, negative but weak causality indicating that Bitcoin is a speculative asset. However, after the break, the relationship becomes positive and strong revealing the diversifier and hedge properties of Bitcoin.

John Taskinsoy

No abstract is available for this record.

Klaus Grobys

A total of 1.1 million bitcoins were stolen in the 2013–2017 period. Noting that the average price for a Bitcoin in 2018 was $7572 the corresponding monetary equivalent of losses is $8.9 billion highlighting the societal impact of this criminal activity. Investigating the response of the uncertainty of Bitcoin returns when hacking incidents occur, the results of this study point toward two different responses. After experiencing a contemporaneous effect at day t=0, the volatility increases significantly again at day t+5. Hacking incidents that occur in the Bitcoin market also affect the uncertainty in the Ethereum market with a time delay of five days. Notably, neither Bitcoin nor Ethereum appear to exhibit asymmetric responses to negative innovations.

Julián Andrada Félix, Adrián Fernández-Pérez, Simón Sosvilla‐Rivero

No abstract is available for this record.

Saketh Aleti, Bruce Mizrach

Abstract We study Bitcoin (BTC) trading at the Chicago Mercantile Exchange (CME) and four settlement spot exchanges that transact $146 million per day in the BTC/USD pair. Spot market median trade sizes are under $1,300 but exceed $18,000 on the CME. Bid‐ask spreads average 0.0298%. Trade sizes of over $1 million move markets by less than 1%. 2.5% of trades and 15.5% of cancellations on Coinbase take place within 50 ms. Bid‐ask spreads exceed 0.8% for only 226 s. Most executions trade‐through better quotes, with estimated losses of $36 million. The CME leads price discovery. BTC leads Ethereum price adjustment.

Sana Guizani, Ines Kahloul Nafti

The emergence of Bitcoin (BTC) has triggered intense discussions. Despite the particular interest of the public, the theoretical understanding of the value of this crypto currency is limited. This is why current research is trying to find better leads to evaluate a complex phenomenon: the BTC price. The volatility of its price presents a certain specificity compared to the traditional currencies. In order to understand the reasons for this volatility, we try to identify and to analyze the main determinants of the BTC price and to estimate their influence. We apply time series to daily data for the period from 19/12/2011 to 06/02/2018. We used several approaches, including the Auto Regressive Distributed Lag ARDL model, the cointegration test at Pesaran et al. (2001) and the Granger causality test in the sense of Toda and Yamamoto (1995). Our estimated results suggest that the number of addresses, the attractiveness indicator and the mining difficulty have a significant impact on the BTC price with variations over time. On the other hand, the transaction volume, the stock, the EUR/USD exchange rate and the macroeconomic and financial development do not determine the price of the BTC in the short term as well as in the long term.

Leopoldo Catania, Mads Sandholdt

This paper studies the behaviour of Bitcoin returns at different sample frequencies. We consider high frequency returns starting from tick-by-tick price changes traded at the Bitstamp and Coinbase exchanges. We find evidence of a smooth intra-daily seasonality pattern, and an abnormal trade- and volatility intensity at Thursdays and Fridays. We find no predictability for Bitcoin returns at or above one day, though, we find predictability for sample frequencies up to 6 h. Predictability of Bitcoin returns is also found to be time–varying. We also study the behaviour of the realized volatility of Bitcoin. We document a remarkable high percentage of jumps above 80 % . We also find that realized volatility exhibits: (i) long memory; (ii) leverage effect; and (iii) no impact from lagged jumps. A forecast study shows that: (i) Bitcoin volatility has become more easy to predict after 2017; (ii) including a leverage component helps in volatility prediction; and (iii) prediction accuracy depends on the length of the forecast horizon.

Carlos Trucíos, Aviral Kumar Tiwari, Faisal Alqahtani

Risk management is an important and helpful process for investors, hedge funds, traders and market makers. One of its key points is the appropriate estimation of risk measures which can improve the investment decisions and trading strategies. The high volatility of cryptocurrencies turns them a really risky investment and consequently, appropriate risk measures estimation is extremely necessary. In this article, we deal with the estimation of two widely used risk measures such as Value-at-Risk and Expected Shortfall in a cryptocurrency context. To face the presence of outliers and the correlation between cryptocurrencies, we propose a methodology based on vine copulas and robust volatility models. Our procedure is illustrated in a seven-dimensional equal-weight cryptocurrency portfolio and displays good performance.

Canh Phuc Nguyen, Nguyen Quang Binh, Thanh Dinh Su

The study examines the diversification capability of seven cryptocurrencies with the largest market size against risks from economic factors as oil price, gold price, interest rate, USD strength, and S&P500. Using the weekly data of Bitcoin, Litecoin, Ripple, Stellar, Monero, Dash, and Bytecoin in the period Aug/2014-Jun/2018, the study finds that there are structural breaks and ARCH disturbance in each cryptocurrency, suggesting a systematic risk within the cryptocurrency market. However, the causality between cryptocurrencies and economic factors is undirected. Interestingly, our findings show that cryptocurrencies are insignificant correlations with economic factors. The result implies that cryptocurrencies can not be assumed as financial assets to hedge systematic risks from economic factors.

Vasily Derbentsev, Natalia Datsenko, Olga Stepanenko, Vitaly Bezkorovainyi

This paper describes the construction of the short-term forecasting model of cryptocurrencies’ prices using machine learning approach. The modified model of Binary Auto Regressive Tree (BART) is adapted from the standard models of regression trees and the data of the time series. BART combines the classic algorithm classification and regression trees (C&RT) and autoregressive models ARIMA. Using the BART model, we made a short-term forecast (from 5 to 30 days) for the 3 most capitalized cryptocurrencies: Bitcoin, Ethereum and Ripple. We found that the proposed approach was more accurate than the ARIMA-ARFIMA models in forecasting cryptocurrencies time series both in the periods of slow rising (falling) and in the periods of transition dynamics (change of trend).