Blockchain Papers

Andrew Phillip, Jennifer Chan, Shelton Peiris

No abstract is available for this record.

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. . . . . . . . . . . .

Wang Chun Wei

We examine the predictions of the resale option hypothesis (Scheinkman and Xiong, 2003) in cryptocurrency markets. The resale option hypothesis yields testable implications on the relationship between the level and volatility of mispricing, and the degree of heterogeneous beliefs. Using turnover as a proxy for heterogeneity, we find evidence supporting the resale option hypothesis. These findings are persistent across various types of cryptocurrencies, and support the notion that cryptocurrencies trade above intrinsic value. Futhermore, we conduct two backtests to show that portfolios with higher turnover or resale option characteristics underperform portfolios with lower turnover or resale option characteristics. This supports the theory that disagreement is negatively related to future returns for positive biased assets (see Atmaz and Basak, 2018).

Wang Chun Wei

No abstract is available for this record.

Aleš Kozubík

Aleš Kozubík University of Žilina – Faculty of Management Science and Informatics – Department of the Mathematical Methods and Operations Research, Univerzitná 8215/1, 010 26 Žilina, Slovak Republic DOI: https://doi.org/10.31410/ITEMA.2018.507 ​ 2nd International Scientific Conference on Recent Advances in Information Technology, Tourism, Economics, Management and Agriculture – ITEMA 2018 – Graz, Austria, November 8, […]

Anton Golub, Lidan Großmaß, Ser‐Huang Poon

No abstract is available for this record.

Nikolaus Hautsch, Christoph Scheuch, Stefan Voigt

No abstract is available for this record.

Alla Petukhina, Simon Trimborn, Wolfgang Karl Härdle, Hermann Elendner

No abstract is available for this record.

Cathy Yi‐Hsuan Chen, Wolfgang Karl Härdle, Ai Jun Hou, Ning Wang

The CRIX (CRyptocurrency IndeX) has been constructed based on a number of cryptos and provides a high coverage of market liquidity, hu.berlin/crix. The crypto currency market is a new asset market and attracts a lot of investors recently. Surprisingly a market for contingent claims hat not been built up yet. A reason is certainly the lack of pricing tools that are based on solid financial econometric tools. Here a first step towards pricing of derivatives of this new asset class is presented. After a careful econometric pre-analysis we motivate an affine jump diffusion model, i.e., the SVCJ (Stochastic Volatility with Correlated Jumps) model. We calibrate SVCJ by MCMC and obtain interpretable jump processes and then via simulation price options. The jumps present in the cryptocurrency fluctutations are an essential component. Concrete examples are given to establish an OCRIX exchange platform trading options on CRIX.

Tim Leung, Hung Cuong Nguyen

Purpose This paper aims to present a methodology for constructing cointegrated portfolios consisting of different cryptocurrencies and examines the performance of a number of trading strategies for the cryptocurrency portfolios. Design/methodology/approach The authors apply a series of statistical methods, including the Johansen test and Engle–Granger test, to derive a linear combination of cryptocurrencies that form a mean-reverting portfolio. Trading systems are designed and different trading strategies with stop-loss constraints are tested and compared according to a set of performance metrics. Findings The paper finds cointegrated portfolios involving four cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Bitcoin Cash (BCH) and Litecoin (LTC), and the corresponding trading strategies are shown to be profitable under different configurations. Originality/value The main contributions of the study are the use of multiple altcoins in addition to bitcoin to construct a cointegrated portfolio, and the detailed comparison of the performance of different trading strategies with and without stop-loss constraints.

Guglielmo Maria Caporale, Timur Zekokh

This paper aims to select the best model or set of models for modelling volatility of the four most popular cryptocurrencies, i.e. Bitcoin, Ethereum, Ripple and Litecoin. More than 1000 GARCH models are fitted to the log returns of the exchange rates of each of these cryptocurrencies to estimate a one-step ahead prediction of Value-at-Risk (VaR) and Expected Shortfall (ES) on a rolling window basis. The best model or superior set of models is then chosen by backtesting VaR and ES as well as using a Model Confidence Set (MCS) procedure for their loss functions. The results imply that using standard GARCH models may yield incorrect VaR and ES predictions, and hence result in ineffective risk-management, portfolio optimisation, pricing of derivative securities etc. These could be improved by using instead the model specifications allowing for asymmetries and regime switching suggested by our analysis, from which both investors and regulators can benefit.

Leopoldo Catania, Stefano Grassi

No abstract is available for this record.

Andreas Bjordal, Espen Opdahl

In this paper, we rigorously investigate the benefit of utilizing an active investment strategy\nbased on momentum when investing in cryptocurrencies. We also examine how including\ncryptocurrencies in a more traditional asset allocation can optimize an investment portfolio.\nFirst, we create strategies with the use of exponential moving averages and simple average\nfilters to generate a trading signal. Second, we provide evidence that the active strategies\nreceive positive return, but significantly less than the passive buy-and-hold\nalternative/benchmark. Third, we find evidence that including a portion of cryptocurrency in\na portfolio with more traditional assets will improve the risk-adjusted return, due to low\nhistorical correlation. And fourth, we look at and evaluate the extreme volatility and risk\nrelated to cryptocurrencies and the suggested cryptocurrency bubble. Our results have\nimportant implications for portfolio managers and first-time investors alike.

Janick Rohrbach, Silvan Suremann

No abstract is available for this record.

Alessandra Cretarola, Gianna Figg-Talamanca, Marco Patacca

In recent literature it is claimed that BitCoin price behaves more likely to a volatile stock asset than a currency and that changes in its price are influenced by sentiment about the BitCoin system itself; in Kristoufek [10] the author analyses transaction based as well as popularity based potential drivers of the BitCoin price finding positive evidence. Here, we endorse this finding and consider a bivariate model in continuous time to describe the price dynamics of one BitCoin as well as a second factor, affecting the price itself, which represents a sentiment indicator. We prove that the suggested model is arbitrage-free under a mild condition and, based on risk-neutral evaluation, we obtain a closed formula to approximate the price of European style derivatives on the BitCoin. By applying the same approximation technique to the joint likelihood of a discrete sample of the bivariate process, we are also able to fit the model to market data. This is done by using both the Volume and the number of Google searches as possible proxies for the sentiment factor. Further, the performance of the pricing formula is assessed on a sample of market option prices obtained by the website deribit.com.

Alessandra Cretarola, Gianna Figà‐Talamanca, Marco Patacca

In recent literature it is claimed that BitCoin price behaves more likely to a volatile stock asset than a currency and that changes in its price are influenced by sentiment about the BitCoin system itself; in Kristoufek [10] the author analyses transaction based as well as popularity based potential drivers of the BitCoin price finding positive evidence. Here, we endorse this finding and consider a bivariate model in continuous time to describe the price dynamics of one BitCoin as well as a second factor, affecting the price itself, which represents a sentiment indicator. We prove that the suggested model is arbitrage-free under a mild condition and, based on risk-neutral evaluation, we obtain a closed formula to approximate the price of European style derivatives on the BitCoin. By applying the same approximation technique to the joint likelihood of a discrete sample of the bivariate process, we are also able to fit the model to market data. This is done by using both the Volume and the number of Google searches as possible proxies for the sentiment factor. Further, the performance of the pricing formula is assessed on a sample of market option prices obtained by the website deribit.com.

Simon Trimborn, Mingyang Li, Wolfgang Karl Härdle

Abstract Cryptocurrencies have left the dark side of the finance universe and become an object of study for asset and portfolio management. Since they have low liquidity compared to traditional assets, one needs to take into account liquidity issues when adding them to a portfolio. We propose a Liquidity Bounded Risk-return Optimization (LIBRO) approach, which is a combination of risk-return portfolio optimization under liquidity constraints. Cryptocurrencies are included in portfolios formed with stocks of the S&amp;P 100, US Bonds, and commodities. We illustrate the importance of the liquidity constraints in an in-sample and out-of-sample study. LIBRO improves the weight optimization in the sense that it only adds cryptocurrencies in tradable amounts depending on the intended investment amount. The returns greatly increase compared to portfolios consisting only of traditional assets. We show that including cryptocurrencies in a portfolio can indeed improve its risk–return trade-off.

Joerg Osterrieder, Stephen Chan, Jeffrey Chu, Saralees Nadarajah

We analyze statistical properties of the largest cryptocurrencies (determined by market capitalization), of which Bitcoin is the most prominent example. We characterize their exchange rates versus the U.S. Dollar by fitting parametric distributions to them. It is shown that returns are clearly non-normal, however, no single distribution fits well jointly to all the cryptocurrencies analysed. We find that for the most popular currencies, such as Bitcoin and Litecoin, the generalized hyperbolic distribution gives the best fit, while for the smaller cryptocurrencies the normal inverse Gaussian distribution, generalized t distribution, and Laplace distribution give good fits. The results are important for investment and risk management purposes.

P. S. Lintilhac, Agnès Tourin

We propose an optimal dynamic pairs trading strategy model for a portfolio of cointegrated assets. Using stochastic control techniques, we compute analytically the optimal portfolio weights and relate our result to several other strategies commonly used by practitioners, including the static double-threshold strategy. Finally, we apply our model to a bitcoin portfolio and conduct an out-of-sample test with historical data from three exchanges, with two cointegrating relations.

Spyros Giannelos, Ioannis Konstantelos, Goran Štrbac

Under the current EU legislation, Distribution Network Operators (DNOs) are expected to provide firm connections to new DG, whose penetration is set to increase worldwide creating the need for significant investments to enhance network capacity. However, the uncertainty around the magnitude, location and timing of future DG capacity renders planners unable to accurately determine in advance where network violations may occur. Hence, conventional network reinforcements run the risk of asset stranding, leading to increased integration costs. A novel stochastic planning model is proposed that includes generalized formulations for investment in conventional and smart grid assets such as Demand-Side Response (DSR), Coordinated Voltage Control (CVC) and Soft Open Point (SOP) allowing the quantification of their option value. We also show that deterministic planning approaches may underestimate or completely ignore smart technologies.

Josef Kokeš, Michal Bejček

The paper deals with cryptocurrencies and trading. Main goal of this article is to introduce strategy for automated trading on cryptocurrency exchange market. For this purpose we will use algorithm based of Floyd-Warshall algorithm. Article is introductory and can this method can be developed in the future. First, a general introduction to cryptocurrencies is given from the programmer's point of view, some statistics data and figure representing volatility of exchange. Then the article describes some basic strategies for automated trading. Also explained is the algorithm Floyd-Warshall and its modifications for automation arbitrage. An illustrative example is given and a trading algorithm is listed.

Matteo Ortisi

In this paper we propose to use the Grand Canonical Minority Game (GCMG, a highly simplified financial market model) as a model of bitcoin market to show how the lack of an income for “miners”, similar to yield earned by bond holders, could be a structural reason for high volatility of bitcoin price in a reference currency. Coherently with present analysis, the introduction of future contracts on bitcoin would have the effect of reducing the overall market volatility.

Joerg Osterrieder

Cryptocurrencies became popular with the emergence of Bitcoin and have shown an unprecedented growth over the last few years. As of November 2016, more than 720 cryptocurrencies exist, with Bitcoin still being the most popular one. We show the statistical properties of the most important cryptocurrencies. We characterize their exchange rates versus the US Dollar by fitting parametric distributions to them, including the Student t distribution, the generalized hyperbolic distribution as well as the asymmetric normal inverse Gaussian and the asymmetric variance gamma distribution. Our findings show that cryptocurrencies exhibit strong non-normal characteristics, with standard heavy-tailed distributions such as the Student t distribution giving good descriptions of the data. This is the first study that looks at the parametric distribution of cryptocurreny returns. The results are important for investment and risk management purposes.

Ludvig Backlund

This thesis examines how Distributed Ledger Technologies (DLTs) could be utilized in capital markets in general and in the Nordic capital market in particular. DLTs were introduced with the so called cryptocurrency Bitcoin in 2009 and has in the last few years been of interest to various financial institutions as a means to streamline financial processes. By combining computer scientific concepts such as public-key cryptography and consensus algorithms DLTs make it possible to keep shared databases with limited trust among the participators and without the use of a trusted third party. In this thesis various actors on the Nordic capital market were interviewed and their stance on DLTs were summarized. In addition to this a Proof of Concept of a permissioned DLT application for ownership registration of securities was constructed. It was found that all the interviewees were generally optimistic about DLTs potential to increase the efficiency of capital markets. The technology needs to be adopted to handle the capital markets demand for privacy and large transaction volumes, but there is a general agreement among the interviewees that these issues will be solved. The biggest challenge for an adoption of DLTs seem to lie in that of finding a common industry-wide standard.