Abstract This paper examines whether there exists a momentum effect after one-day abnormal returns in the cryptocurrency market. For this purpose, a number of hypotheses of interest are tested for the Bitcoin, Ethereum and Litecoin exchange rates vis-à-vis the US dollar over the period 01.01.2015–01.09.2019, specifically whether or not: (H1) the intraday behavior of hourly returns is different on abnormal days compared to normal days; (H2) there is a momentum effect on days with abnormal returns, and (H3) after one-day abnormal returns. The methods used for the analysis include various statistical methods as well as a trading simulation approach. The results suggest that hourly returns during the day of positive/negative abnormal returns are significantly higher/lower than those during the average positive/negative day. The presence of abnormal returns can usually be detected before the day ends by estimating specific timing parameters. Prices tend to move in the direction of the abnormal returns till the end of the day when it occurs, which implies the existence of a momentum effect on that day giving rise to exploitable profit opportunities. This effect (together with profit opportunities) is also observed on the following day. In two cases (BTCUSD positive abnormal returns and ETHUSD negative abnormal returns), a contrarian effect is detected instead.
The purpose of this paper is to examine if there is cointegration between the daily closing price of the cryptocurrency Bitcoin and five other cryptocurrencies; Ethereum, Ripple, Bitcoin Cash, EOS and Litecoin in five different time periods, all ending April 9, 2019. To test if there is a long-run relationship between Bitcoin and these mentioned cryptocurrencies, two different tests for cointegration are applied; the Engle-Granger two step approach and Johansen’s cointegration test as well as a Vector Error Correction Model (VECM). The results from both cointegration tests suggest that Bitcoin is cointegrated with Bitcoin Cash, Ethereum, Litecoin and Ripple. The Johansen test and the Engle-Granger method for cointegration demonstrate that Bitcoin and EOS do not have any cointegrating relationship. Another finding is that, based on the results from the VECM estimation, the price of Bitcoin has a statistically significant long-run impact on the prices of Bitcoin Cash, Ethereum, Litecoin and Ripple.
Sam M. Werner, Paul J. Pritz, Alexei Zamyatin, William J. Knottenbelt
Mining pools in Proof-of-Work cryptocurrencies allow miners to pool their computational resources as a means of reducing payout variance. In Ethereum, uncle blocks are valid Proof-of-Work solutions which do not become the head of the blockchain, yet yield rewards if later referenced by main chain blocks. Mining pool operators are faced with the non-trivial task of fairly distributing rewards for both block types among pool participants.Inspired by empirical observations, we formally reconstruct a Sybil attack exploiting the uncle block distribution policy in a queue-based mining pool. To ensure fairness of the queue-based payout scheme, we propose a mitigation. We examine the effectiveness of the attack strategy under the current and the proposed policy via a discrete-event simulation. Our findings show that the observed attack can indeed be obviated by altering the current reward scheme.
Federico Matteo Benčić, Pavle Skočir, Ivana Podnar Žarko
Supply chain management enhanced by the Internet of Things (IoT) solutions integrate special tags (e.g., RFID, NFC, and QR-codes) with products to create Smart Tags, in addition to storing supplemental information about a product, which is also used to track products during their lifecycle. However, a product consumer has to implicitly trust the Smart Tag creator and other stakeholders within the supply chain that they are providing authentic data within a product's tag. The DL-Tags solution steps into this environment to offer a decentralized, privacy-preserving, and verifiable management of Smart Tags during a product's lifecycle. The solution is based on distributed ledger technology (DLT) and uses the Ethereum blockchain to mediate interactions between the stakeholders during a product's exchange process. By reaching a consensus on the product's description and state logged on the blockchain, all involved stakeholders and product consumers can verify the product's authenticity without revealing their identity. The paper describes the DL-Tags solution and includes a cost analysis of all implemented transactions on the Ethereum blockchain. The proposed solution provides evidence of the product's origin and its journey across the supply chain while preventing tag duplication and manipulation. It is among the first documented practical solutions using DLT and IoT for supply chain management, which is designed to be distributed ledger agnostic.
Mohsin Ur Rahman, Fabrizio Baiardi, Barbara Guidi, Laura Ricci
Decentralized Online Social Networks (DOSNs) have been proposed as an alternative solution to the current centralized Online Social Networks (OSNs). Online Social Networks are based on centralized architecture (e.g., Facebook, Twitter, or Google+), while DOSNs do not have a service provider that acts as central authority and users have more control over their information. Several DOSNs have been proposed during the last years. However, the decentralization of the OSN requires efficient solutions for protecting the privacy of users, and to evaluate the trust between users. Blockchain represents a disruptive technology which has been applied to several fields, among these also to Social Networks. In this paper, we propose a manageable, user-driven and auditable access control framework for DOSNs using blockchain technology. In the proposed approach, the blockchain is used as a support for the definition of privacy policies. The resource owner uses the public key of the subject to define flexible role-based access control policies, while the private key associated with the subject's Ethereum account is used to decrypt the private data once access permission is validated on the blockchain. We evaluate our solution by exploiting the Rinkeby Ethereum testnet to deploy the smart contract, and to evaluate its performance. Experimental results show the feasibility of the proposed scheme in achieving auditable and user-driven access control via smart contract deployed on the Blockchain.
In the current identity management process, the problems such as increasing differentiation of digital identities, fragmentation and centralisation of identity information have been exposed. In this paper, a framework, named EverSSDI, using smart contracts in an Ethereum-based blockchain is constructed, which provides a unique identifier to normalise differentiated user identities. A fine-grained authorisation mechanism is designed based on the Hierarchical Deterministic protocol (HD protocol) to solve the fragmentation problem of identity information. Besides, a reliable information verification mechanism is elaborated to enhance the credibility of the digital information. In addition, two novel methods are proposed for identity recovery, one of which is based on Social Networking Services (SNS) authorisation and the other Ethereum Oracles. The final implementation and discussion show that the user has self-sovereign management authority, which enables the user to become the real dominant of his/her identity rather than only a prover of digital identity.
This paper studies the forecasting ability of cryptocurrency time series. This study is about the four most capitalized cryptocurrencies: Bitcoin, Ethereum, Litecoin and Ripple. Different Bayesian models are compared, including models with constant and time-varying volatility, such as stochastic volatility and GARCH. Moreover, some crypto-predictors are included in the analysis, such as S\&P 500 and Nikkei 225. In this paper the results show that stochastic volatility is significantly outperforming the benchmark of VAR in both point and density forecasting. Using a different type of distribution, for the errors of the stochastic volatility the student-t distribution came out to be outperforming the standard normal approach.
Anthony Ngunyi, Simon Mundia, Cyprian Ondieki Omari
Cryptocurrencies have become increasingly popular in recent years attracting the attention of the media, academia, investors, speculators, regulators, and governments worldwide. This paper focuses on modelling the volatility dynamics of eight most popular cryptocurrencies in terms of their market capitalization for the period starting from 7th August 2015 to 1st August 2018. In particular, we consider the following cryptocurrencies; Bitcoin, Ethereum, Litecoin, Ripple, Moreno, Dash, Stellar and NEM. The GARCH-type models assuming different distributions for the innovations term are fitted to cryptocurrencies data and their adequacy is evaluated using diagnostic tests. The selected optimal GARCH-type models are then used to simulate out-of-sample volatility forecasts which are in turn utilized to estimate the one-day-ahead VaR forecasts. The empirical results demonstrate that the optimal in-sample GARCH-type specifications vary from the selected out-of-sample VaR forecasts models for all cryptocurrencies. Whilst the empirical results do not guarantee a straightforward preference among GARCH-type models, the asymmetric GARCH models with long memory property and heavy-tailed innovations distributions overall perform better for all cryptocurrencies.
Kentaroh Toyoda, Mojtaba Shakeri, Chi Xu, Allan N. Zhang
CIA (Confidentiality, Integrity and Availability) is getting more and more important in the cloud based manufacturing and IIoT (Industrial Internet-of-Things)/Indutry 4.0 domain. Recently, blockchain, a decentralized ledger technique, has attracted considerable attention to realize secure and robust data management systems for IIoT. Although several blockchain-based data management platforms for IIoT have been proposed, sensor data are stored outside of the blockchain, meaning that the data integrity and accessibility are not improved. In this paper, we take a different blockchain-based approach for sensor data management; any important sensor data are stored in the blockchain. For realizing this, a series of sensor data is compressed-then-stored in the blockchain by leveraging a fact that many sensor data often have a certain level of periodicity and stability. We have tested our idea against the real sensor data measured at our model factory, and evaluated several performance metrics such as compression ratio, compression/decompression time, process time required to store and retrieve sensor data from our Proof-of-Concept system with Ethereum and the expected blockchain size. From the results, it can be concluded that the proposed platform is viable for small factory cases but needs more technological advancement is required for large scale cases.
Cryptocurrency mining is a process of calculation of some hard-computational task. Ethereum cryptocurrency mining sets a task of determining a special block header value which is corresponds to requirements of cryptocurrency system. While doing computational jobs there are software computational errors which will lead to wrong mining task solution. To ensure mining correctness, the mining results should be validated. This paper describes reliable mining result validation methodology, which is not affecting mining performance.
With the growing interest in cryptocurrency and its algorithm, studies on cryptocurrency price predations have been extensively conducted in various academic disciplines. Since the cryptocurrency is generated and consumed by the Blockchain system, it has been considered that Blockchain-specific information would be the main components in predicting cryptocurrency prices. Specifically, this point of view has been largely employed in the studies of Bitcoin price predictions. However, this study recognizes that Ethereum, a popular and leading cryptocurrency in the market, has distinct Blockchain information as compared to that of Bitcoin. We attempt to investigate the relationships between inherent Ethereum Blockchain information and Ethereum prices. Furthermore, the research examines how Blockchain information of other coins in the market is associated with Ethereum prices. The results of data analysis show that Ethereum Blockchain information and Blockchain information of other coins have strong correlations with the final Ethereum prices.
This study attempts to create a cryptocurrency classification metod and tool for reliability assessment from the point of view of investors and traders. The definition of the reliability of cryptocurrency and classification criteria is formulated. The following emphasizes how, using the support vector method, based on the available open data, it is possible to determine the reliability of Ethereum-based cryptocurrencies. As a result of testing, 15 reliable cryptocurrencies were identified and software was developed that allows you to collect and classify cryptocurrencies.
Distributed ledgers allow us to replicate databases of records across mutually untrusted parties. The best known example of distributed ledger is perhaps the Bitcoin blockchain, which maintains a consistent history of financial transactions organized as a hashed chain of blocks. Distributed ledgers can be public, i.e., accessible by everyone, or private, i.e., accessible only by a given consortium of parties. In this paper, we explore the technological possibilities of applying Identity-Based Encryption and Attribute-Based Encryption to distributed ledgers. We introduce the novel concept of Virtual Private Ledger. A Virtual Private Ledger is a private distributed ledger embedded in a public cryptocurrency ledger by means of cryptography. A Virtual Private Ledger provides for the same confidentiality and integrity of a private distributed ledger, but without its high operational costs. In particular, nodes that maintain the ledger do not have to be always online to trust the order and the integrity of the records. We analytically show that Virtual Private Ledgers can be implemented over many existing cryptocurrency ledgers like Ethereum, EOS.IO, IOTA, XRP. Different cryptocurrencies lead to different trade-offs between the Virtual Private Ledger max record size, cost, validation time, and max consortium members.
In Chapter 2 , I introduced EOS.IO when I covered bitcoin, altcoins, and different consensus mechanisms. Specifically, I covered how EOS.IO is an example of altcoins that turn into tokens; you created an EOS block producer and were able to create a full node capable of mining EOS tokens. Ethereum was the beginning of your blockchain smart contract development, and you learned to use the Solidity language to write smart contracts and dapps. EOS.IO has created a more robust architecture than Ethereum for smart contract and dapp development.
Ravi Chandra Koirala, Keshav Dahal, Santiago Matalonga
The concept of openness and decentralization is one which people have desired since years. Everyone wants transactional data to be transparent. When sensitive data are on the hand of third-parties, it may be susceptible to frauds and misuse. The advent of blockchain, a decentralized technology in cryptocurrency, has revealed an appropriate solution to address such issues. Blockchain maintains the integrity of a transaction. It not only secures from tempering and fraud but also ensures transactions are verifiable without involvement of an intermediate. Therefore, blockchain is applied to different decentralized domains that require trusted computing. Supply chain is one of these domains, that can benefit from trustworthy decentralized transactions initiated by multiple stakeholders. This paper presents a general model for a blockchain enabled supply chain. We have implemented this model using three smart contracts on an Ethereum platform. We have provided evidence of our verification and validation efforts. Our results convey the feasibility of the approach, which can streamline the administrative processes, and automatize the transactions making the system more efficient and transparent.