Blockchain Papers

Jules Clément, Edson Pindza, Ur Koumba

No abstract is available for this record.

Szabolcs Majoros, András Zempléni

In this paper we extend the known methodology for fitting stable\ndistributions to the multivariate case and apply the suggested method to the\nmodelling of daily cryptocurrency-return data. The investigated time period is\ncut into 10 non-overlapping sections, thus the changes can also be observed. We\napply bootstrap tests for checking the models and compare our approach to the\nmore traditional extreme-value and copula models.\n

Victor Troster, Aviral Kumar Tiwari, Muhammad Shahbaz, Demian Macedo

No abstract is available for this record.

Josselin Garnier, Knut Sølna

The bitcoin price has surged in recent years and it has also exhibited phases of rapid decay. In this paper we address the question to what extent this novel cryptocurrency market can be viewed as a classic or semi-efficient market. Novel and robust tools for estimation of multi-fractal properties are used to show that the bitcoin price exhibits a very interesting multi-scale correlation structure. This structure can be described by a power-law behavior of the variances of the returns as functions of time increments and it can be characterized by two parameters, the volatility and the Hurst exponent. These power-law parameters, however, vary in time. A new notion of generalized Hurst exponent is introduced which allows us to check if the multi-fractal character of the underlying signal is well captured. It is moreover shown how the monitoring of the power-law parameters can be used to identify regime shifts for the bitcoin price. A novel technique for identifying the regimes switches based on a goodness of fit of the local power-law parameters is presented. It automatically detects dates associated with some known events in the bitcoin market place. A very surprising result is moreover that, despite the wild ride of the bitcoin price in recent years and its multi-fractal and non-stationary character, this price has both local power-law behaviors and a very orderly correlation structure when it is observed on its entire period of existence.

Yuanyuan Zhang, Stephen Chan, Jeffrey Chu, Saralees Nadarajah

No abstract is available for this record.

Irene Henriques, Perry Sadorsky

Bitcoin is an exciting new financial product that may be useful for inclusion in investment portfolios. This paper investigates the implications of replacing gold in an investment portfolio with bitcoin (“digital gold”). Our approach is to use several different multivariate GARCH models (dynamic conditional correlation (DCC), asymmetric DCC (ADCC), generalized orthogonal GARCH (GO-GARCH)) to estimate minimum variance equity portfolios. Both long and short portfolios are considered. An analysis of the economic value shows that risk-averse investors will be willing to pay a high performance fee to switch from a portfolio with gold to a portfolio with bitcoin. These results are robust to the inclusion of trading costs.

Antônio Carlos da Silva Filho, Natália Diniz Maganini, Eduardo Fonseca de Almeida

The recent emergence and use growth of cryptocurrencies based on Blockchain technology increased interest in the study of its economic dynamics and financial characteristics. Bitcoin is up to now the more widely known and disseminated cryptocurrency, with greater volume of transactions, market value and acceptance in exchange services. In order to contribute to the comprehension of the price behavior of the Bitcoin market, this study analyzes whether the historical series of prices of this currency, quoted every 12 h from September 14, 2011 to November 20, 2017 has multifractal behavior. The results of the research identified multifractal characteristics in the series and that both long-range correlations and fat tails distribution contribute to Bitcoin’s multifractal behavior. We compared the non-Gaussian properties and the multifractality degrees of Bitcoin series with the non-Gaussian properties and multifractality degrees of several stock market indices scattered around the world. In addition, we investigated the power of multifractal analysis in the study of volatility and forecast for this series, pointing to a possible use of multifractal parameters in Technical Analysis.

David Ardia, Keven Bluteau, Maxime Rüede

We test the presence of regime changes in the GARCH volatility dynamics of Bitcoin log–returns using Markov–switching GARCH (MSGARCH) models. We also compare MSGARCH to traditional single–regime GARCH specifications in predicting one–day ahead Value–at–Risk (VaR). The Bayesian approach is used to estimate the model parameters and to compute the VaR forecasts. We find strong evidence of regime changes in the GARCH process and show that MSGARCH models outperform single–regime specifications when predicting the VaR.

Hrishikesh Singh, Parul Agarwal

Crypto currencies are considered as the next model of economics and monetary exchange. In recent years, popular cryptocurrency such as Bitcoin and Ethereum witness an exponential growth in economic sphere. In this paper empirical testing of four conventional machine learning methods is performed to predict the bitcoin prices using last eight years of transactional data. Linear and polynomial regression is implemented using all the features individually. Polynomial regression, Support Vector regression and KNN regression are hyper tuned with grid search logic. Results depicted that KNN regression outperformed others models in attaining mean square error of 0.00021.

Emmanouil Platanakis, Charles Sutcliffe, Andrew Urquhart

This paper contributes to the literature on cryptocurrencies by examining the performance of naïve (1/N) and optimal (Markowitz) diversification in a portfolio of four popular cryptocurrencies. We employ weekly data with weekly rebalancing and show there is very little to select between naïve diversification and optimal diversification. Our results hold for different levels of risk-aversion and an alternative estimation window.

Danai Likitratcharoen, Teerasak Na Ranong, Ratikorn Chuensuksomboon, Norrasate Sritanee · 5 authors

Due to conclusion could not rely on only one test, in this study, we apply various approaches to verify the actuary of VaR model to find out whether VaR model, especially historical VaR and delta normal VaR model, can provide the accurate risk measurement results for cryptocurrencies risk, especially CRIX, BTC, ETH and XRP. We use Kupiec’s POF test, Independence Test - Christoffersen (1998) and Joint Test that widely use for backtesting VaR model. Performance test results for risk measurement by historical VaR provide a fairly accurate over delta normal VaR when we use Kupiec’s POF-test for the accuracy of VaR model. Christoffersen (1998) independence test, the exceptions (failures) of historical VaR and delta normal VaR model show independence exceptions in accordance with an only high confidence level of critical values (0.99). Otherwise, the low confidence level of critical values (0.90 and 0.95) appears dependence exceptions. For the Joint test, we combine POF-test and independence test because each model has different advantages and disadvantages. The results show that historical VaR model is suitable for measuring cryptocurrency risk over delta normal VaR only high confidence level of critical values.

Zhang We, Pengfei Wang, Xiao Li, Dehua Shen

No abstract is available for this record.

Wenjun Feng, Yiming Wang, Zhengjun Zhang

Cryptocurrencies are one of the most promising financial innovations of the last decade. Different from major stock indices and the commodities of gold and crude oil, the cryptocurrencies exhibit some characteristics of immature market assets, such as auto-correlated and non-stationary return series, higher volatility, and higher tail risks measured by conditional Value at Risk (VaR) and conditional expected shortfall (ES). Using an extreme-value-theory-based method, we evaluate the extreme characteristics of seven representative cryptocurrencies during 08 August 2015–01 August 2017. We find that during the sub-period of 01 August 2016–01 August 2017, there are finite loss boundaries for most of the selected cryptocurrencies, which are similar to the commodities, and different from the stock indices. Meanwhile, we find that left tail correlations are much stronger than right tail correlations among the cryptocurrencies, and tail correlations increased after August 2016, suggesting high and growing systematic extreme risks. We also find that cryptocurrencies to be both left tail independent, and cross tail independent with four selected stock indices, which implies part of the safe-haven function of the cryptocurrencies, indicating their ability to be a great diversifier for the stock market as gold, but not enough to be a tail hedging tool like gold.

Andrew Phillip, Jennifer Chan, Shelton Peiris

No abstract is available for this record.

Khaled Guesmi, Samir Saadi, Ilyes Abid, Zied Ftiti

No abstract is available for this record.

Leopoldo Catania, Stefano Grassi, Francesco Ravazzolo

This paper studies the predictability of cryptocurrencies time series. We compare several alternative univariate and multivariate models in point and density forecasting of four of the most capitalized series: Bitcoin, Litecoin, Ripple and Ethereum. We apply a set of crypto–predictors and rely on Dynamic Model Averaging to combine a large set of univariate Dynamic Linear Models and several multivariate Vector Autoregressive models with different forms of time variation. We find statistical significant improvements in point forecasting when using combinations of univariate models and in density forecasting when relying on selection of multivariate models.

Stjepan Begušić, Zvonko Kostanjčar, H. Eugene Stanley, Boris Podobnik

Detection of power-law behavior and studies of scaling exponents uncover the characteristics of complexity in many real world phenomena. The complexity of financial markets has always presented challenging issues and provided interesting findings, such as the inverse cubic law in the tails of stock price fluctuation distributions. Motivated by the rise of novel digital assets based on blockchain technology, we study the distributions of cryptocurrency price fluctuations. We consider Bitcoin returns over various time intervals and from multiple digital exchanges, in order to investigate the existence of universal scaling behavior in the tails, and ascertain whether the scaling exponent supports the presence of a finite second moment. We provide empirical evidence on slowly decaying tails in the distributions of returns over multiple time intervals and different exchanges, corresponding to a power-law. We estimate the scaling exponent and find an asymptotic power-law behavior with 2 < α < 2.5 suggesting that Bitcoin returns, in addition to being more volatile, also exhibit heavier tails than stocks, which are known to be around 3. Our results also imply the existence of a finite second moment, thus providing a fundamental basis for the usage of standard financial theories and covariance-based techniques in risk management and portfolio optimization scenarios.

Tian Guo, Albert Bifet, Nino Antulov-Fantulin

Bitcoin is one of the most prominent decentralized digital cryptocurrencies. Ability to understand which factors drive the fluctuations of the Bitcoin price and to what extent they are predictable is interesting both from the theoretical and practical perspective. In this paper, we study the problem of the Bitcoin short-term volatility forecasting based on volatility history and order book data. Order book, consisting of buy and sell orders over time, reflects the intention of the market and is closely related to the evolution of volatility. We propose temporal mixture models capable of adaptively exploiting both volatility history and order book features. By leveraging rolling and incremental learning and evaluation procedures, we demonstrate the prediction performance of our model as well as studying the robustness, in comparison to a variety of statistical and machine learning baselines. Meanwhile, our temporal mixture model enables to decipher the time-varying effect of order book features on volatility. It demonstrates the prospect of our temporal mixture model as an interpretable forecasting framework over heterogeneous Bitcoin data.

Li Guo, Yubo Tao, Wolfgang Karl Härdle

Cryptocurrencies return cross-predictability yields information on risk propagation and market segmentation. To explore these effects, we build a dynamic network of cryptocurrencies based on the evolution of return cross-predictability and develop a dynamic covariate-assisted spectral clustering method to consistently estimate the latent group membership of cryptocurrencies. We show that return cross-predictability and cryptocurrencies' characteristics, including hashing algorithms and proof types, jointly determine the cryptocurrencies market segmentation. Portfolio analysis reveals that more centred cryptocurrencies in the network earn higher risk premiums.

Elie Bouri, Mahamitra Das, Rangan Gupta, David Roubaud

This paper contributes to the embryonic literature on the relations between Bitcoin and conventional investments by studying return and volatility spillovers between this largest cryptocurrency and four asset classes (equities, stocks, commodities, currencies, and bonds) in bear and bull market conditions. We conducted empirical analyses based on a smooth transition VAR GARCH-in-mean model covering daily data from July 19, 2010 to October 31, 2017. We found significant evidence that Bitcoin returns are related quite closely to those of most of the other assets studies, particularly commodities, and therefore, the Bitcoin market is not isolated completely. The significance and sign of the spillovers exhibited some differences in the two market conditions and in the direction of the spillovers, with greater evidence that Bitcoin receives more volatility than it transmits. Our findings have implications for investors and fund managers who are considering Bitcoin as part of their investment strategies and for policymakers concerned about the vulnerability that Bitcoin represents to the stability of the global financial system.

Stavros Stavroyiannis

Purpose The purpose of this paper is to examine the value-at-risk and related measures for the Bitcoin and to compare the findings with Standard and Poor’s SP500 Index, and the gold spot price time series. Design/methodology/approach A GJR-GARCH model has been implemented, in which the residuals follow the standardized Pearson type-IV distribution. A large variety of value-at-risk measures and backtesting criteria are implemented. Findings Bitcoin is a highly volatile currency violating the value-at-risk measures more than the other assets. With respect to the Basel Committee on Banking Supervision Accords, a Bitcoin investor is subjected to higher capital requirements and capital allocation ratio. Practical implications The risk of an investor holding Bitcoins is measured and quantified via the regulatory framework practices. Originality/value This paper is the first comprehensive approach to the risk properties of Bitcoin.

Shou Hsing Shih

Cryptocurrencies are known as unpredictable due to their highly volatility. In time series, the forecasting accuracy is strongly affected by the methodologies that are used in identifying the pattern of a nonstationary stochastic realization. The purpose of the present study is to develop an algorithm that is capable of efficiently identifying the pattern of cryptocurrencies. A brief summary of the algorithm is given. To illustrate the quality of our proposed algorithm, we study the pattern of ten different reputable cryptocurrencies and use their daily closing prices to constitute a time series. The comparison between our proposed forecasting algorithm versus the autoregressive integrated moving average (ARIMA) process will be demonstrated.

Stephanie Riedl

Cryptocurrencies as an investment have received increasing attention by media and international governments over the last years.However, little is known yet about the dynamics that drive these highly volatile alternative assets.This thesis studies the dynamic interdependencies between the volatility of Bitcoin, Litecoin, Ripple, Dogecoin and Feathercoin via the Dynamic Conditional Correlation model by Engle (2002) with the multivariate Student-t distribution.The main question is whether a multivariate approach improves the Value at Risk forecasting accuracy for the conditional heteroscedasticity in comparison to univariate GARCH-type models.Results show that there is a high interconnectedness between the volatility of the currencies.However, the Dynamic Conditional Correlation model can not deliver better forecasting results than the univariate GARCH-type models for the individual cryptocurrency return series. Contents List of Figures iv List of Tables vList of Tables 1 Summary Statistics for daily log returns 100 of cryptocurrencies.Log returns are calculated using: r t = 100ln(P t /P t-1 ).Returns are observed until 14 th of March 2018.Market cap is captured at 14 th of March 2018.Jarque-Bera-Test checks for deviation from normality (skewness S different from zero and kurtosis K different from 3): JB = T (S/6 + (k -3) 2 /24), is distributed as X 2 (2) with 2 degrees of freedom.Its critical value at the five-percent level is 5.99 and at the one-percent it is 9.21. . . . . . . . . . . . . . . . . . . . . . . . . 2 AIC and BIC for the estimated GARCH-type models. t is modelled via an ARMA-(1,1) process. t is modelled via a GARCH-type process of order (1,1).T=1544.Lowest AICs and BICs per group are written in bold letters. . . . . . . . . . . . . . . . . . . . . . . . . . 3 1%-and 5%-Value at Risk results for the univariate GARCH-type models.1-day-ahead rolling forecast with recursive window, model parameters refitted every 300 observations.Model is built on a training data set of 800 observations, which leaves 744 out-of-sample forecasts.% Viol: Percentage of VaR violations at = 1% and = 5%.L uc : p-value for test of unconditional coverage; L cc : p-value for test of conditional coverage.Values printed bold if p < 0.05. . . . . . . 4 Model parameters of the selected GARCH models. t is modelled via an ARMA-(1,1) process.T=1544.*** p-value < 0.001; ** pvalue < 0.01; * p-value < 0.05.Q(10): p-value of Ljung-Box test on squared standardized residuals for lag = 10; ARCH(5): p-value for weighted ARCH LM test for lag = 5. . . . . . . . . . . . . . . . . 5 Lag = 0 sample correlation matrix 0 (Pearson) of the five crypto currency log return series.T = 1554. . . . . . . . . . . . . . . . . .6 Model parameters for the estimated DCC models.T=1544, k=5, *** p-value < 0.001; ** p-value < 0.01; * p-value < 0.05.Model parameters for univariate volatility series are listed in table (4). . .7 Mean and (standard deviation) of the lag = 0 correlations in the multivariate volatility of the currencies estimated by the DCC model in equation (57).T=1544. . . . . . . . . . . .

Artur Paździor, Grzegorz Kłosowski

Cel – Celem artykułu jest prezentacja koncepcji systemu informatycznego umożliwiającego prognozowanie kursu kryptowaluty bitcoin (BTC) w odniesieniu do waluty euro. Metodologia badania – Na potrzeby realizacji tak sformułowanego celu opracowano model sztucznej sieci neuronowej – perceptronu wielowarstwowego. W ramach badań dobrano zmienne wejściowe, od których uzależniono kurs BTC. Pozyskano także odpowiednie dane, pochodzące z dziennych notowań kursów wybranych walut i metali. Dane poddano stosownej obróbce matematycznej w celu ich dostosowania do wykorzystania podczas uczenia, walidacji i testowania sztucznej sieci neuronowej. Oryginalność/wartość – Oryginalny był dobór wektora zmiennych wejściowych, umożliwiających prognozowanie kursu BTC. Wyniki przeprowadzonych eksperymentów potwierdziły wysoką skuteczność prognozowania w perspektywie jedno- i dwudniowej. Wysokie wartości współczynnika regresji (R) i mały błąd średniokwadratowy (MSE) świadczą o tym, że opracowany system predykcyjny prawidłowo przewiduje kursy analizowanej kryptowaluty nie tylko w odniesieniu do danych historycznych, lecz także dla wartości bieżących i przyszłych.