Blockchain Papers

Dean Fantazzini, Stephan Zimin

No abstract is available for this record.

Paolo Pagnottoni

Despite the current growing interest in Bitcoins-and cryptocurrencies in general-financial instruments, as well as studies related to them, are quite underdeveloped. Therefore, this article aims to provide a suitable pricing model for options written on this peculiar underlying. This is done through an artificial neural network approach, where classical pricing models-namely the trinomial tree, Monte Carlo simulation, and explicit finite difference method-are used as input layers. Results show that options written on Bitcoin turn out to be systematically overpriced when considering classical methods, whereas a noticeable improvement in price predictions is achieved by means of the proposed neural network model.

Julien Chevallier, Stéphane Goutte, Khaled Guesmi, Samir Saadi

This study contributes to the existing literature on the empirical characteristics of virtual currency allowing for a dynamic transition between different economic regimes and considering various crashes and rallies over the business cycle, that is captured by jumps. We combine Markov-switching models with Levy jump-diffusion offer a new model that captures the different sub-period of crises over the business cycle, that is captured by jumps. This method also enables to test the relevance of dynamic measures of regime switching concerning the independent pure-jump process, which are not frequently used in the literature. Bitcoin offers something different than a traditional currency; there is potential value of having a network that helps as a secure repository for the common knowledge of all transactions. Besides, the value of Bitcoin fluctuates so wildly that it may be too risky to serve as a credible store of value.

Tetsuya Takaishi

Recent studies have found that the log-volatility of asset returns exhibit roughness. This study investigates roughness or the anti-persistence of Bitcoin volatility. Using the multifractal detrended fluctuation analysis, we obtain the generalized Hurst exponent of the log-volatility increments and find that the generalized Hurst exponent is less than $1/2$, which indicates log-volatility increments that are rough. Furthermore, we find that the generalized Hurst exponent is not constant. This observation indicates that the log-volatility has multifractal property. Using shuffled time series of the log-volatility increments, we infer that the source of multifractality partly comes from the distributional property.

Dorje C. Brody, Lane P. Hughston, Bernhard K. Meister

A term structure model in which the short rate is zero is developed as a candidate for a theory of cryptocurrency interest rates. The price processes of crypto discount bonds are worked out, along with expressions for the instantaneous forward rates and the prices of interest-rate derivatives. The model admits functional degrees of freedom that can be calibrated to the initial yield curve and other market data. Our analysis suggests that strict local martingales can be used for modelling the pricing kernels associated with virtual currencies based on distributed ledger technologies.

Nae-Young Kang, Jungmu Kim

Given that there are both continuous and discontinuous components in the movement of asset prices, existing asset pricing models that assume only continuous price movements should be revised. In this paper, we explore the features of jumps, which are discontinuous movements, by examining Bitcoin pricing. First, we identify jumps in the Bitcoin price on a daily basis, applying a non-parametric methodology and then break down the Bitcoin total rate of return into a jump rate of return and a continuous rate of return. In our empirical analysis, price jumps turn out to be independent of volatility. Moreover, the jumps in the Bitcoin price do not appear at regular intervals; rather, they tend to be concentrated in clusters during special periods, implying that once an economic crisis occurs, the crisis will last for a long time due to contagion effects and the economy will take a considerable amount of time to recover fully. Further, the contribution of the jump rate of return to the total rate of return of the Bitcoin price is lower than the contribution of the continuous return, implying that the pursuit of sustainable returns rather than large but temporary returns will improve the total rate of return over the long term. Finally, more jumps are observed when trading volume is lower, implying that market illiquidity drives discontinuous movement in asset prices. Overall, the features of jump risk are like two sides of the same coin and jump risks are expected to have a significant effect on asset pricing, suggesting that consideration of jumps is essential for risk management as well as asset pricing.

Stjepan Begušić, Zvonko Kostanjčar

The goal of this paper is to explore the relationship between momentum effects and liquidity in cryptocurrency markets. Portfolios based on momentum-liquidity bivariate sorts are formed and rebalanced on a varying number of cryptocurrencies through time. We find a strong momentum effect in the most liquid cryptocurrencies, which supports the theories of investor herding behavior. Moreover, we propose two profitable long-only strategies: the illiquid losers and liquid winners, which exhibit improved risk adjusted performance over the market capitalization weighted portfolio.

Dilip B. Madan, Sofie Reyners, Wim Schoutens

No abstract is available for this record.

Matúš Chládek

This thesis investigates diversification benefits of Bitcoin and Ethereum. Technological innovation that made them possible, is interesting but for investors hard to grasp. The more important question is whether they should buy the digital currency or avoid it. We analyze Bitcoin and Ethereum from point of view of an investor within compatible with mean-variance (and non-mean-variance respectively) framework. Both cryptoassets are alternately added to base portfolio consisting of global indices representing American, European and Asian markets. Statistically rigorous tests suggest that Bitcoin yields ad- ded value to investors with utility function consistent with mean-variance setting. Same holds for for investors with preferences described by exponential and power utility func- tion. Ethereum shows similar results with exception of exponential utility. Performance benefits of both assets are preserved in the out-of-sample setting as size of test window reaches 28 weeks and increases. In the case of shorter test window, base assets show similar or slightly superior performance. Optimal allocation in out-of-sample framework is found by direct utility maximization with gradient based method. Keywords Bitcoin, Ethereum, digital currency, investment portfolio, diversification

Gianna Figà‐Talamanca, Sergio M. Focardi, Marco Patacca

No abstract is available for this record.

Yu-Min Lian, Chi-Hung Cheng, Shih-Hsun Lin, Jui-Hsuan Lin

In this study, we make use of both the specific method of Monte Carlo simulation and the spot-futures parity with the cost of carry to establish a dynamic price model of Bitcoin futures and to conduct the appraisals and numerical analyses. More specifically, the electricity fees and equipment costs are taken into account and the proposed model is thereby built. Numerical results show that various cost factors have significant effects on the Bitcoin futures price. We employ Monte Carlo simulation to approximate the Bitcoin futures price and we use Python to program the computations.

David Cerezo Sánchez

The Holy Grail of a decentralised stablecoin is achieved on rigorous mathematical frameworks, obtaining multiple advantageous proofs: stability, convergence, truthfulness, faithfulness, and malicious-security. These properties could only be attained by the novel and interdisciplinary combination of previously unrelated fields: model predictive control, deep learning, alternating direction method of multipliers (consensus-ADMM), mechanism design, secure multi-party computation, and zero-knowledge proofs. For the first time, this paper proves: - the feasibility of decentralising the central bank while securely preserving its independence in a decentralised computation setting - the benefits for price stability of combining mechanism design, provable security, and control theory, unlike the heuristics of previous stablecoins - the implementation of complex monetary policies on a stablecoin, equivalent to the ones used by central banks and beyond the current fixed rules of cryptocurrencies that hinder their price stability - methods to circumvent the impossibilities of Guaranteed Output Delivery (G.O.D.) and fairness: standing on truthfulness and faithfulness, we reach G.O.D. and fairness under the assumption of rational parties As a corollary, a decentralised artificial intelligence is able to conduct the monetary policy of a stablecoin, minimising human intervention.

Tobias Burggraf

No abstract is available for this record.

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.

Andrew Phillip, Jennifer Chan, Shelton Peiris

No abstract is available for this record.

Edina Berlinger, Barbara Dömötör, Ferenc Illés

No abstract is available for this record.

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

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.