Blockchain Papers

Jun Deng, Huifeng Pan, Shuyu Zhang, Bin Zou

No abstract is available for this record.

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.

Edina Berlinger, Barbara Dömötör, Ferenc Illés

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

Dinesh Sivagourou

Investoren und Vermögensverwalter suchen nach Finanzinstrumenten, die die erwartete Rendite ihrer Investition bei gleichzeitiger Minimierung des potenziellen Risikos erhöhen. In der Praxis diversifizieren Vermögensverwalter ihre Vermögensallokation auf verschiedene Anlageklassen allen voran Aktien, Anleihen und Rohstoffe. In den letzten Jahren gewinnt die neue Anlageklasse der Kryptowährungen immer mehr an Einfluss - Anlagekapital. Die erste unter ihnen, Bitcoin, wurde wegen ihrer Technologie sehr berühmt: dezentrale Datenhaltung, sicheres und schnelles elektronisches Tausch- und Zahlungsmittel. Diese Arbeit konzentriert sich auf die Leistung der Core-Satellite Strategie mit diesen neuen digitalen Währungen als Satellit. Die Herausforderung besteht darin, die Kryptowährungen zu finden, die Abwärtstrends kompensieren kann und dem Anleger bessere Renditen erwirtschaftet. Die Korrelationsstruktur der Kryptowährungen muss untersucht werden, sodass gegenläufige Kryptowährungen ausgewählt werden können. Dazu verwenden wir TEDAS – Tail Event Driven Asset allocation, eine aktive Investitionsstrategie zur Auswahl der Kryptowährungen. Diese Methode untersucht die Abhängigkeit von Kryptowährungen in verschiedenen Quantilen am linken Rand der Verteilung. Die Arbeit vergleicht verschiedene auf TEDAS basierenden Investitionsstrategie.

Shinji Kakinaka, Ken Umeno

The recent emergence of cryptocurrencies such as Bitcoin and Ethereum has posed possible alternatives to global payments as well as financial assets around the globe, making investors and financial regulators aware of the importance of modeling them correctly. The Lvy's stable distribution is one of the attractive distributions that well describes the fat tails and scaling phenomena in economic systems. In this paper, we show that the behaviors of price fluctuations in emerging cryptocurrency markets can be characterized by a non-Gaussian Lvy's stable distribution with ' 1:4 under certain conditions on time intervals ranging roughly from 30 min to 4 h. Our arguments are developed under quantitative valuation defined as a distance function using the Parseval's relation in addition to the theoretical background of the General Central Limit Theorem (GCLT). We also discuss the model-fitting for returns by employing the method based on likelihood ratios. Even though the cubic power-law model is a better fitting model than the Lvy's stable model in the tail part of returns, the Lvy's stable model outperforms the fit for the entire and wider range of returns. Our approach can be extended for further analysis of statistical properties and contribute to developing proper applications for financial modeling.

Chunlei Liu

Stake systems which issue stakes as well as coins are proposed. Two subadditive stake systems are studied: one is the radical stake system, the other is the logarithmic stake system. Securities of both systems are analysed.

Chunlei Liu

Stake systems which issue stakes as well as coins are proposed. Two subadditive stake systems are studied: one is the radical stake system, the other is the logarithmic stake system. Securities of both systems are analysed.

Ho-Chun Herbert Chang, Kevin Li

This study proposes a strategy to make the lookback option cheaper and more practical, and suggests the use of its properties to reduce risk exposure in cryptocurrency markets through blockchain enforced smart contracts and correct for informational inefficiencies surrounding prices and volatility. This paper generalizes partial, discretely-monitored lookback options that dilute premiums by selecting a subset of specified periods to determine payoff, which we call amnesiac lookback options. Prior literature on discretely-monitored lookback options considers the number of periods and assumes equidistant lookback periods in pricing partial lookback options. This study by contrast considers random sampling of lookback periods and compares resulting payoff of the call, put and spread options under floating and fixed strikes. Amnesiac lookbacks were priced with Monte Carlo simulations of Gaussian random walks under equidistant and random periods. Results were compared to analytic and binomial pricing models for the same derivatives. Simulations show diminishing marginal increases to the fair price as the number of selected periods is increased. The returns correspond to a Hill curve whose parameters are set by interest rate and volatility. We demonstrate over-pricing under equidistant monitoring assumptions with error increasing as the lookback periods decrease. An example of a direct implication for event trading is when shock is forecasted but its timing uncertain, equidistant sampling produces a lower error on the true maximum than random choice. We conclude that the instrument provides an ideal space for investors to balance their risk, and as a prime candidate to hedge extreme volatility. We discuss the application of the amnesiac lookback option and path-dependent options to cryptocurrencies and blockchain commodities in the context of smart contracts.

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.