Blockchain Papers

Elie Bouri, Chi Keung Marco Lau, Brian M. Lucey, David Roubaud

No abstract is available for this record.

John Fry

No abstract is available for this record.

Weiyi Liu

No abstract is available for this record.

Elie Bouri, Rangan Gupta, David Roubaud

No abstract is available for this record.

Anne Haubo Dyhrberg, Sean Foley, Jiří Švec

No abstract is available for this record.

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.

Marc Juchli

For various reasons, financial institutions often make use of high-level trading strategies when buying and selling assets. Many individuals, irrespective or their level of prior trading knowledge, have recently entered the field of trading due to the increasing popularity of cryptocurrencies, which offer a low entry barrier for trading. Regardless of the intention or trading strategy of these traders, the invariable outcome is their attempt to buy or sell assets. However, in such a competitive field, experienced market participants seek to exploit any advantage over those who are less experienced, for financial gain. Therefore, this work aims to make a contribution to the important issue of how to optimize the process of buying and selling assets on exchanges, and to do so in a form that is accessible to other traders. This research concerns the optimization of limit order placement within a given time horizon of 100 seconds and how to transpose this process into an end-to-end learning pipeline in the context of reinforcement learning.<br/>Features were constructed from raw market event data that related to movements of the Bitcoin/USD trading pair on the Bittrex cryptocurrency exchange. These features were then used by deep reinforcement learning agents in order to learn a limit order placement policy. To facilitate the implementation of this process, a reinforcement learning environment that emulates a local broker was developed as part of this work. Furthermore, we defined an evaluation procedure which can determine the capabilities and limitations of the policies learned by the reinforcement learning agents and ultimately provides means to quantify the optimization achieved with our approach. Our analysis of the results of this work includes the identification of patterns in cryptocurrency trading that were formed by market participants who posted orders, and a conceptual framework to construct data features containing these patterns. We developed a fully-functioning reinforcement learning environment that emulates a local broker and, by means of this process, we identified which components are essential.<br/>With the use of this environment, we were able to train and test multiple reinforcement learning agents whose aims were to optimize the placement of buy and sell limit orders. During the evaluation, we were able to improve the parameter settings of the constructed reinforcement learning environment and therefore improve the policy learned by the agents. Ultimately, we achieved a significant improvement in limit order placement with the application of a state-of-the-art deep Q-network agent and were able to simulate purchases and sales of 1.0 BTC at a price that was up to $33.89 better than the market price. We have made use of the OpenAI Gym library and contributed our work to the community to enable further investigations to be carried out. The work done in this thesis can be used as a framework to (1) build a component that acts as an intermediary between trader and exchange and (2) to enable exchanges to provide a new order type to be used by traders.

Lanouar Charfeddine, Youcef Maouchi

This letter questions the true nature (true versus spurious) of the Long Range Dependence (LRD) behavior observed in the returns and volatility series of four Cryptocurrencies (CC). Using a robust approach, this letter shows that the LRD behavior exhibited by the returns and volatility series of Bitcoin, Litecoin, and Ripple is a true behavior, and not a statistical artifact. As for Ethereum, the results show that the true LRD is only supported for the volatility series. Our results confirm the inefficiency of all the considered markets, with the exception of Ethereum.

Elie Bouri, Syed Jawad Hussain Shahzad, David Roubaud

No abstract is available for this record.

Bundit Pinudom, Worathan Tungpisansampun, Roengchai Tansuchat, Paravee Maneejuk

This study analyses the effect of adding bitcoin into the portfolio by exploiting the Long Only investment strategy. The Portfolio consists of five assets: bitcoin, crude oil price index, stock exchange of Thailand (SET) price index, the exchange rate between Thai and USD and Thai government bond compound with treasurer bill. The model used for modelling the return of all asset is Multivariate t-copula based on GARCH and also measure the risk of the portfolio using the Value-at-risk (VaR) under the condition of minimizing the variance of return. We find that when adding more bitcoin into the portfolio, the return and risk of asset increased. If we only invest in bitcoin, we will face the risk at 16.90% and gain 6.27%. When comparing the effectiveness of portfolio by using Return-risk ratio, it found that portfolio with bitcoin shows the higher return rate than portfolios without bitcoin. Therefore, it can conclude that bitcoin could indeed increase the effectiveness of portfolio.

Shuai Wang, Xiaochun Ni, Yong Yuan, Fei–Yue Wang · 6 authors

Prediction markets are markets where participants trade contracts whose payoffs are tied to a future event, thereby yielding prices that can be interpreted as market aggregated forecasts. Past studies have shown that the prediction markets can provide accurate forecasts, sometimes better than sophisticated statistical tools. Due to their advantages, prediction markets have been widely used in the prediction of elections, project management, product quality, and impact of events. However, prediction markets also have some limitations, e.g., poor anonymity and limited market liquidity. In this paper, we propose to apply blockchain powered smart contracts to the prediction markets. First, we give a comprehensive overview on the prediction markets, including their theoretical basis, classification and applications. Second, we present how to design prediction markets based on smart contracts. Then, the algorithm of contracts implementation is proposed. Finally, in order to verify the effectiveness of the algorithm, an intra-enterprise prediction market is built based on a private blockchain. The experimental results show that the market can make accurate prediction for a particular event. In addition, the autonomy, self-sufficiency, and decentralization characteristics of blockchain make the prediction markets more efficient and robust.

David Spohn

This paper explores the predictive qualities of Bitcoin Miners Revenue on Bitcoin Returns. Using data on Bitcoin in the cryptocurrency market from July 1, 2010 to February 20, 2018, we reflect intervariable correlations not previously examined. We analyze those relationships with a conditional regression analysis adjusting for calendar effects. We separate the sample, and use the last 17 trading days (month) to test a strategy based on the probability of Bitcoin Returns moving higher. After a slight modification to the logistic regression analysis, we find a profitable trading strategy exists based solely on Bitcoin Miners Revenue and the probability of Bitcoin Returns moving higher.

Wei Zhang, Pengfei Wang, Xiao Li, Dehua Shen

We examine the stylized facts of eight forms of cryptocurrencies representing almost 70% of cryptocurrency market capitalization. In particular, the empirical results show that (1) there exists heavy tails for all the returns of cryptocurrencies; (2) the autocorrelations for returns decay quickly, while the autocorrelations for absolute returns decay slowly; (3) returns of cryptocurrencies display strong volatility clustering and leverage effects; (4) Hurst exponent for volatility is more volatile than that of the returns, while they all suggest the long-range dependence phenomena; and (5) there exists power-law correlation between price and volume.

Bill X. Hu, Thomas H. McInish, Jonathan Miller, Li Zeng

No abstract is available for this record.

Elie Bouri, Rangan Gupta, David Roubaud

This study examines the presence of herding in the cryptocurrency market. The latter is the outcome of mass collaboration and imitation. Results from the static model suggest no significant herding. However, the presence of structural breaks and nonlinearities in the data series suggests applying a static model is not appropriate. Accordingly, we conduct a rolling-window analysis, and those results point to significant herding behavior, which varies over time. Using a logistic regression, we find that herding tends to occur as uncertainty increases. Our findings induce useful insights related to portfolio and risk management, trading strategies, and market efficiency.

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.

Carey Caginalp

Recently, the notion of cryptocurrencies has come to the fore of public interest. These assets that exist only in electronic form, with no underlying value, offer the owners some protection from tracking or seizure by government or creditors. We model these assets from the perspective of asset flow equations developed by Caginalp and Balenovich, and investigate their stability under various parameters, as classical finance methodology is inapplicable. By utilizing the concept of liquidity price and analyzing stability of the resulting system of ordinary differential equations, we obtain conditions under which the system is linearly stable. We find that trend-based motivations and additional liquidity arising from an uptrend are destabilizing forces, while anchoring through value assumed to be fairly recent price history tends to be stabilizing.

Ufuk Akoğuz, Taner Akkan

It is a very tiring process for people to watch the multiple parallel instant price changes in stock exchanges that are rapidly changing like the crypto money market. As a solution to this, a computer software that can make quick and objective decisions by constant observation can take the place of a person. In this study, an original decision algorithm that evaluates the instantaneous values of price change indicators and obtains relatively high earnings in a short period of time is examined. The Python programming language and Mathlib library have been used to construct this algorithm and to visualize the data, Moving Average Convergence Divergence (MACD) and Bollinger Bands have been used as a basic indicator. The result is an algorithm that requires less processing power and can operate continuously even on ARM-based mini-computers.

Mutlu Başaran Öztürk, Halil Arslan, Temur Kayhan, Mustafa Uysal

2017 yılında Bitcoin’in piyasa değerinde önemli bir artış yaşanmış ve 200 milyar dolar seviyesi aşılarak Bitcoin kurumsal yatırımcıların gündemine gelmeye başlamıştır. CME ve CBOE gibi dünyanın en büyük vadeli işlem borsaları Bitcoin’i listelerken Microsoft, PWC ve Overstock gibi kurumlar Bitcoin’i tanımlamaya başlamışlardır. Bitcoin’in bir yatırım aracı olarak görülebilmesi için bazı şartlar gereklidir. Verimli bir piyasada işlem görmesi, fiyatlama formasyonunun belirginleşmesi ve portföyler için bir çeşitlendirme aracı olabilmesi bunlardan bazıları olarak görülebilir. Ana akım varlık grupları ile Bitcoin arasındaki uzun vadeli ilişkiyi Johansen Eşbütünleşme testi ile inceleyen çalışma sonuçlarına göre Bitcoin’in altın haricinde diğer geleneksel finansal ve emtia varlıklarından bağımsız bir hareket gösterdiği ortaya çıkmıştır. Bitcoin’in söz konusu bağımsız hareketi Bitconomi olarak tanımlanırken bu durum mikro seviyede riskli bir varlığın makro anlamda portföylerin riskini düşürebileceği anlamına gelmektedir. Finansal sistemde çok küçük bir alanı işgal etmesi ve Bitcoin üretimindeki zorluk derecesinin klasik ekonomi ile çelişmesi korelasyonun anlamsız olmasının nedenleri arasında gösterilebilir. Kuzey Kore ve Ukrayna gerilimlerinde Bitcoin fiyatındaki artışlar ve altın ile Bitcoin arasındaki uzun vadeli pozitif ilişki yüksek varyansı nedeniyle eleştirilen Bitcoin’in gelecekte güvenli liman olabileceği gibi ilginç bir ironiye işaret etmektedir. Literatürdeki çalışmalar her geçen yıl Bitcoin’in varyansının gerilediğini göstermektedir.

Stanisław Drożdż, Robert Gȩbarowski, Ludovico Minati, Paweł Oświȩcimka · 5 authors

Based on 1-minute price changes recorded since year 2012, the fluctuation properties of the rapidly-emerging Bitcoin (BTC) market are assessed over chosen sub-periods, in terms of return distributions, volatility autocorrelation, Hurst exponents and multiscaling effects. The findings are compared to the stylized facts of mature world markets. While early trading was affected by system-specific irregularities, it is found that over the months preceding Apr 2018 all these statistical indicators approach the features hallmarking maturity. This can be taken as an indication that the Bitcoin market, and possibly other cryptocurrencies, carry concrete potential of imminently becoming a regular market, alternative to the foreign exchange (Forex). Since high-frequency price data are available since the beginning of trading, the Bitcoin offers a unique window into the statistical characteristics of a market maturation trajectory.

Wang Chun Wei

No abstract is available for this record.

Ladislav Krištoufek

No abstract is available for this record.

Aaron Brown

The same investors who invest in normal markets, using the same strategies they use in normal markets, for the same motivations they have in normal markets, can cause bubbles.

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.