Contributions published at Blockchain and Distributed Ledger Technologies (Claudio Tessone)

The aim of this paper is to discern if hype is a determinant of price spikes in cryptocurrency. Furthermore, we aim to determine if online sentiment drives the value of cryptocurrencies. Due to the youth of the cryptocurrency market, existing research regarding hype in cryptocurrency is still scarce. This thesis ads to the existing research by providing insight on the social components with respect to the abnormal movements of public interest, trade volume and community growth. To identify hype in the cryptocurrency market a literature review and a qualitative analysis are conducted. In the qualitative analysis, four determinants of hype are found. Through the abnormal movements of these, the research seeks to find the effect on the price by using the programming language R and analysing a set of n=9 cryptocurrencies. Furthermore, the underlying blockchain properties are assessed to identify if hype acts conjointly as a determinant of price. It is found that hype acts as a determinant of price and that the size of the community is irrelevant.

Cryptocurrencies, which are cryptographic virtual currencies, are centralized networks, based on blockchain technology. This means, that they are not issued by a central authority. With cryptocurrencies becoming more and more important, their exchange gets more and more significant as well. There are different ways of trading cryptocurrencies, there are centralized or decentralized options. With the decentralized option of decentralized exchanges (DEX), there is no need for a centralized market maker or a central authority since decentralized finance (DeFi) platforms use automated market maker (AMM) protocols, involving no further third party. Related to that, smart contracts, which are automated transaction protocols, enable the creation of decentralized finance elements, which are for example liquidity pools. Liquidity pools are pools of cryptocurrencies or tokens that involve many different participants. When trading in markets with low liquidity, there is a potential risk of slippage. This would be the difference between the expected trading price and the price at which the trade is finally executed. Liquidity pools are therefore a possible solution for illiquid markets. Furthermore, they are also providing speed and convenience in the decentralized finance ecosystem. Liquidity pools therefore play a very important role in decentralized finance with automated market makers, especially when it comes to decentralized exchanges. The problem to be addressed in this thesis is the functionality of liquidity pools, decentralized exchanges with automated market maker protocols. Over the past few years there are different liquidity pools that have gained large market share, like Aave, Uniswap and SushiSwap. The main contribution of the thesis would be to consolidate the sources regarding the functionality of liquidity pools in detail. The research question would address how liquidity pools work in practice and what the difference among blockchain protocols are. The method of the thesis will be theoretical/conceptual. Since sources on the topic are sparse, the main contribution would be to consolidate the sources and review the functionality of liquidity pools.

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Blockchain technology has many applications in terms of record-keeping, transparency, and cryptocurrency as well. Additionally, with one of its characteristics, manipulation or fraud can be eliminated. By decentralizing the data, the data becomes immutable, which means any traditional method of record-keeping that were susceptible to manipulation can be removed now. In this paper, a method to remove scholarship-based manipulation has been proposed. In many universities, there are scholarships based on the student’s family income. To support the poor and deserving in getting an education, these scholarships are a must. However, paper-based income certificates used to justify family income can be easily manipulated. Therefore, with the help of the Ethereum Smart Contract, a framework to store income certificates has been proposed. This method can be used to enter income data from the student’s family’s employer’s backend. Considering a fair and acceptable income for eligibility for the scholarship, the university can decide to go ahead with it without fearing the manipulation of income data.

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Blockchains based on the Proof of Stake consensus protocol have gained increasing attention in recent years. Previous research focuses on matters of energy consumption, decentralization and security of such blockchains. However, there is a lack of thorough studies concerning their fairness. Our contribution is to provide a careful investigation of fairness of the Tezos blockchain. We look at the reward distribution and apply fairness measures such as the Gini or Nakamoto index to the rewards and stakes of the Tezos blockchain participants. Our analysis indicates that certain participants of the Tezos blockchain consensus mechanism receive disproportionately more rewards than others. They do share the feature of having entered the system at a very early stage. Further, we find that the various Tezos protocol upgrades affect the reward distribution, meaning that the fairness of Tezos is impacted by its governance.

Blockchain technology, because of its widespread usage in research, and its multifarious and superior properties has also reached the education sector. While most of the work is related to the record-keeping of students' data, the work proposed in this paper is teacher-centric. In this paper, a framework for educators has been created that will facilitate their future employability. A supervisory entity has been established to provide feedback to educators as they carry out various teaching activities. The feedback and performance of educators would be uploaded to the blockchain ledger through a smart contract by the supervisor, who would create a work profile for the prospective employer to access. A token system for reward has also been introduced in the framework. Because blockchain data is immutable, secure, and distributed, this research could give educators, educational institutions, and employers an advantage in managing faculty and employee records.

In blockchain-based systems, such as Bitcoin’s Proof-of-Work (PoW) protocol, it is expected that a miner’s share of total block revenue is proportional to their share of the network’s total hashing power. However, a deviation to this behavior is the selfish mining strategy, an attack vector discovered by Eyal and Sirer in 2014. This strategy may lead to a miner earning more than their “fair share As a result, Bitcoin’s security assumption of an honest majority may not be sufficient. In this paper, in order to verify whether selfish mining is indeed a profitable strategy in PoW systems, we introduce an agent-based model to simulate the dynamics of selfish mining behavior. The model is by design minimalistic allowing us to analyze the effect of network latency, hashing power distribution, and network topology on relative revenue of selfish miners. We find that for high levels of latency, selfish mining is always a relatively more profitable strategy, and the results turn out to be very robust to changes in the network topology. In addition, we find that the hashing power distribution following power laws, as found empirically, can make it harder for selfish miners to be profitable. Our analysis confirms the main observations that selfish mining is always relatively more profitable for hashing powers representing more than one third of the total computing power. Further, it also confirms that selfish mining behavior could cause a statistically significant high probability of contiguously mined blocks.

According to the United Nations, Sustainable Development Goals are framed for improving rural health, hunger, poverty issues, environmental conditions, and illiteracy globally. With the upcoming technology, there have been many advances in the lifestyle of people all around the world. Comparatively, more emphasis has been given to the development of urban areas than rural. The sustainable development of a country depends on the growth of its rural areas. Countless technological and theoretical models, projects, and frameworks have been proposed and implemented to help overcome sundry issues and challenges faced by rural people in quotidian life. New technological methods are deemed to be the future of livability, therefore; a technologically advanced solution for sustainable rural development is called for. Blockchain Technology is the next step for innovation and development and it has far many applications in sustainable rural development that are yet to be discovered. The objective of this paper is to explicitly review research conducted in rural development to fill the undone work in the future with better research ideas, to make rural areas a livable and advanced place while also maintaining their integrity leading to sustainable development. To conduct such a review, a systematic research methodology is applied following regulations in the conduction of standardized but explorative analysis. Within the timeline of 2010–2021, 112 papers are carefully selected to perform the systematic review. This review will provide a comprehensible as well as concise research compendium for all applications proposed, implemented, and possible in the future to realize the concept of smart villages for the development of rural areas using blockchain technology.

According to the United Nations, Sustainable Development Goals are framed for improving rural health, hunger, poverty issues, environmental conditions, and illiteracy globally. With the upcoming technology, there have been many advances in the lifestyle of people all around the world. Comparatively, more emphasis has been given to the development of urban areas than rural. The sustainable development of a country depends on the growth of its rural areas. Countless technological and theoretical models, projects, and frameworks have been proposed and implemented to help overcome sundry issues and challenges faced by rural people in quotidian life. New technological methods are deemed to be the future of livability, therefore; a technologically advanced solution for sustainable rural development is called for. Blockchain Technology is the next step for innovation and development and it has far many applications in sustainable rural development that are yet to be discovered. The objective of this paper is to explicitly review research conducted in rural development to fill the undone work in the future with better research ideas, to make rural areas a livable and advanced place while also maintaining their integrity leading to sustainable development. To conduct such a review, a systematic research methodology is applied following regulations in the conduction of standardized but explorative analysis. Within the timeline of 2010–2021, 112 papers are carefully selected to perform the systematic review. This review will provide a comprehensible as well as concise research compendium for all applications proposed, implemented, and possible in the future to realize the concept of smart villages for the development of rural areas using blockchain technology.

Bitcoin is built on a blockchain, an immutable decentralized ledger that allows entities (users) to exchange Bitcoins in a pseudonymous manner. Bitcoins are associated with alpha-numeric addresses and are transferred via transactions. Each transaction is composed of a set of input addresses (associated with unspent outputs received from previous transactions) and a set of output addresses (to which Bitcoins are transferred). Despite Bitcoin was designed with anonymity in mind, different heuristic approaches exist to detect which addresses in a specific transaction belong to the same entity. By applying these heuristics, we build an Address Correspondence Network: in this representation, addresses are nodes are connected with edges if at least one heuristic detects them as belonging to the same entity. In this paper, we analyze for the first time the Address Correspondence Network and show it is characterized by a complex topology, signaled by a broad, skewed degree distribution and a power-law component size distribution. Using a large-scale dataset of addresses for which the controlling entities are known, we show that a combination of external data coupled with standard community detection algorithms can reliably identify entities. The complex nature of the Address Correspondence Network reveals that usage patterns of individual entities create statistical regularities; and that these regularities can be leveraged to more accurately identify entities and gain a deeper understanding of the Bitcoin economy as a whole.

Binary random variables are the building blocks used to describe a large variety of systems, from magnetic spins to financial time series and neuron activity. In statistical physics the kinetic Ising model has been introduced to describe the dynamics of the magnetic moments of a spin lattice, while in time series analysis discrete autoregressive processes have been designed to capture the multivariate dependence structure across binary time series. In this article we provide a rigorous proof of the equivalence between the two models in the range of a unique and invertible map unambiguously linking one model parameters set to the other. Our result finds further justification acknowledging that both models provide maximum entropy distributions of binary time series with given means, auto-correlations, and lagged cross-correlations of order one. We further show that the equivalence between the two models permits to exploit the inference methods originally developed for one model in the inference of the other.

Nestedness refers to a hierarchical organization of complex networks where a given node’s neighbors tend to form a subset of the neighborhoods of higher-degree nodes. Although nestedness has been traditionally interpreted as a macroscopic property that involves all the nodes of the network, recent works have reinterpreted it as a mesoscopic network property, by revealing that interactions in diverse empirical networks are often arranged into blocks that exhibit an internally nested structure. Inspired by the popular modularity function, these works rely on a quality function – called in-block nestedness – that assumes a partition of the nodes into blocks that exhibit an internal nested structure. A potential limitation of this approach is that the optimization of modularity (and related quality functions) notoriously suffers from resolution limits that impair the detectability of small blocks. Yet, we do not know whether the in-block nestedness function may exhibit similar resolution limits. Here, we provide numerical and analytical evidence that the in-block nestedness function lacks a resolution limit, which implies that our capacity to detect correct partitions in networks via its maximization depends solely on the accuracy of the optimization algorithms.

This study covers the evolutionary development of blockchain technologies over the last 11 years (2009–2019) and sheds lights on potential areas of innovation in heretofore unexplored sub-components. For this purpose, we collected and analyzed detailed data on 107 different blockchain technologies and studied their component-wise technological evolution. The diversity of their designs was captured by deconstructing the blockchains using the Tasca-Tessone taxonomy to build what we call the “tree of blockchain” composed of blockchain main and sub-components. With the support of information theory and phylogenetics, we found that most design explorations have been conducted within the components in the areas of consensus mechanisms and cryptographic primitives. We also show that some sub-components like Consensus Immutability and Failure Tolerance, Access and Control layer, and Access Supply Management have predictive power over other sub-components. We finally found that few dominant design models—the genetic driving clusters of Bitcoin, Ethereum, and XRP—influenced the evolutionary paths of most of the succeeding blockchains.

In this study, we analyse the aggregated transaction networks of Ether (the native cryptocurrency in Ethereum) and the three most market-capitalised ERC-20 tokens in this platform at the time of writing: Binance, USDT, and Chainlink. We analyse a comprehensive dataset from 2015 to 2020 (encompassing 87,780,546 nodes and 856,207,725 transactions) to understand the mechanism that drives their growth. In a seminal analysis, Kondor et al. (PLoS ONE, 2014, 9: e86197) showed that during its first year, the aggregated Bitcoin transaction network grew following linear preferential attachment. For the Ethereum-based cryptoassets, we find that they present in general super-linear preferential attachment, i.e., the probability for a node to receive a new incoming link is proportional to kα, where k is the node’s degree. Specifically, we find an exponent α = 1.2 for Binance and Chainlink, for Ether α = 1.1, and for USDT α = 1.05. These results reveal that few nodes become hubs rapidly. We then analyse wealth and degree correlation between tokens since many nodes are active simultaneously in different networks. We conclude that, similarly to what happens in Bitcoin, “the rich indeed get richer” in Ethereum and related tokens as well, with wealth much more concentrated than in-degree and out-degree.

Cryptocurrencies are distributed systems that allow exchanges of native (and non-) tokens between participants. The availability of the complete historical bookkeeping opens up an unprecedented possibility: that of understanding the evolution of a cryptocurrency's network structure while gaining useful insights into the relationships between users' behavior and cryptocurrency pricing in exchange markets. In this article we review some recent results concerning the structural properties of the Bitcoin Transaction Networks, a generic name referring to a set of three different constructs: the Bitcoin Address Network, the Bitcoin User Network, and the Bitcoin Lightning Network. The picture that emerges is of a system growing over time, which becomes increasingly sparse and whose mesoscopic structural organization is characterized by the presence of an increasingly significant core-periphery structure. Such a peculiar topology is accompanied by a highly uneven distribution of bitcoins, a result suggesting that Bitcoin is becoming an increasingly centralized system at different levels.

Identifying risk spillovers in financial markets is of great importance for assessing systemic risk and portfolio management. Granger causality in tail (or in risk) tests whether past extreme events of a time series help predicting future extreme events of another time series. The topology and connectedness of networks built with Granger causality in tail can be used to measure systemic risk and to identify risk transmitters. Here we introduce a novel test of Granger causality in tail which adopts the likelihood ratio statistic and is based on the multivariate generalization of a discrete autoregressive process for binary time series describing the sequence of extreme events of the underlying price dynamics. The proposed test has very good size and power in finite samples, especially for large sample size, allows inferring the correct time scale at which the causal interaction takes place, and it is flexible enough for multivariate extension when more than two time series are considered in order to decrease false detections as spurious effect of neglected variables. An extensive simulation study shows the performances of the proposed method with a large variety of data generating processes and it introduces also the comparison with the test of Granger causality in tail by Hong et al. (2009). We report both advantages and drawbacks of the different approaches, pointing out some crucial aspects related to the false detections of Granger causality for tail events. An empirical application to high frequency data of a portfolio of US stocks highlights the merits of our novel approach.

Many real networks feature the property of nestedness, i.e. the neighbours of nodes with a few connections are hierarchically nested within the neighbours of nodes with more connections. Despite the abstract simplicity of this notion, various mathematical definitions of nestedness have been proposed, sometimes giving contrasting results. Moreover, there is an ongoing debate on the statistical significance of nestedness, since random networks where the number of connections (degree) of each node is fixed to its empirical value are typically as nested as real ones. By using only ergodic and unbiased null models, we propose a clarification that exploits the recent finding that random networks where the degrees are enforced as hard constraints (microcanonical ensembles) are thermodynamically different from random networks where the degrees are enforced as soft constraints (canonical ensembles). Indeed, alternative definitions of nestedness can be negatively correlated in the microcanonical one, while being positively correlated in the canonical one. This result disentangles distinct notions of nestedness captured by different metrics and highlights the importance of making a principled choice between hard and soft constraints in null models of ecological networks.

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