NFTs and Copyright: Challenges and Opportunities

Pınar Çağlayan Aksoy & Zehra Özkan Üner

2021, awaiting publication.

Abstract

URL:

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AI as Agents: Agency Law, Artificial Intelligence and Private Law: Global Perspectives

Pınar Çağlayan Aksoy

2021 (ed. Larry DiMatteo, Cristina Poncibo, Pietro Sirena, Michel Cannarsa), Cambridge University Press, awaiting publication.

Abstract

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The evolving liaisons between the transaction networks of Bitcoin and its price dynamics

Bovet A., Campajola C., Mottes F., Restocchi V., Vallarano N., Squartini T., Tessone C.J.

working paper (2019)

Abstract

Cryptocurrencies are distributed systems that allow exchanges of native tokens among participants, or the exchange of such tokens for fiat currencies in markets external to these public ledgers. The availability of their complete historical bookkeeping opens up the possibility of understanding the relationship between aggregated users’ behaviour and the cryptocurrency pricing in exchange markets. This paper analyses the properties of the transaction network of Bitcoin. We consider four different representations of it, over a period of nine years since the Bitcoin creation and involving 16 million users and 283 million transactions. By analysing these networks, we show the existence of causal relationships between Bitcoin price movements and changes of its transaction network topology. Our results reveal the interplay between structural quantities, indicative of the collective behaviour of Bitcoin users, and price movements, showing that, during price drops, the system is characterised by a larger heterogeneity of nodes activity.

URL:

arXiv

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Modelling time-varying interactions in complex systems: the Score Driven Kinetic Ising Model

Campajola C., Di Gangi D., Lillo F., Tantari D

working paper (2020)

Abstract

We introduce a generalization of the Kinetic Ising Model using the score-driven approach, which allows the efficient estimation and filtering of time-varying parameters from time series data. We show that this approach allows to overcome systematic errors in the parameter estimation, and is useful to study complex systems of interacting variables where the strength of the interactions is not constant in time: in particular we propose to quantify the amount of noise in the data and the reliability of forecasts, as well as to discriminate between periods of higher or lower endogeneity in the observed dynamics, namely when interactions are more or less relevant in determining the realization of the observations. We apply our methodology to three different financial settings to showcase some realistic applications, focusing on forecasting high-frequency volatility of stocks, measuring its endogenous component during extreme events in the market, and analysing the strategic behaviour of traders around news releases. We find interesting results on financial systems and, given
the widespread use of Ising models in multiple fields, we believe our approach can be efficiently adapted to a variety of settings, ranging from neuroscience to social sciences and machine learning

URL:

arXiv

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Arbitrage on Markets for Cryptocurrencies

Crépellière, Tommy and Zeisberger, Stefan

Abstract

Recent literature has documented substantial arbitrage opportunities in markets for cryptocurrencies. Given these rather surprising findings, we re-examine the exchange rates of four cryptocurrencies on 21 exchanges with data from October 2018 to June 2019. Our results first provide evidence that price differences exist based on our created arbitrage indices, however, at only a fraction of values reported in previous literature. We then analyze the drivers of these price differences, identifying exchange-specific factors to have the most important role. Based on this, we test actual arbitrage strategies for specific cryptocurrency and fiat currency pairs, finding, in contrast to previous literature, that arbitrage opportunities hardly exist.

URL:

SSRN

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Ethereum Gas Price Statistics

David Carl, Christian Ewerhart

University of Zurich, Department of Economics, Working Paper No. 373, 2020

Abstract

For users of the Ethereum network, the gas price is a crucial parameter that determines how swiftly the decentralized consensus protocol confirms a transaction. This paper studies the statistics of the Ethereum gas price. We start with some conceptual discussion of the gas price notion in view of the actual transaction-selection strategies used by Ethereum miners. Subsequently, we provide the descriptive statistics of what we call the threshold gas price. Finally, we identify and estimate a seasonal ARIMA (SARIMA) model for predicting the hourly median of the threshold gas price.

Lightning Network: a second path towards centralisation of the Bitcoin economy

Jian-Hong Lin, Kevin Primicerio, Tiziano Squartini, Christian Decker, Claudio J.Tessone

arXiv:2002.02819

Abstract

The Bitcoin Lightning Network (BLN), a so-called “second layer” payment protocol, was launched in 2018 to scale up the number of transactions between Bitcoin owners. In this paper, we analyse the structure of the BLN over a period of 18 months, ranging from 14th January 2018 to 13th July 2019, at the end of which the network has reached 8.216 users, 122.517 active channels and 2.732,5 transacted bitcoins. Here, we consider three representations of the BLN: the daily snapshot one, the weekly snapshot one and the daily-block snapshot one. By studying the topological properties of the binary and weighted versions of the three representations above, we find that the total volume of transacted bitcoins approximately grows as the square of the network size; however, despite the huge activity characterising the BLN, the bitcoins distribution is very unequal: the average Gini coefficient of the node strengths is approximately 0.88 causing 10% (50%) the of the nodes to hold the 80% (99%) of the bitcoins at stake in the BLN (on average, across the entire period). Like for other economic systems, we hypothesise that local properties of nodes, like the degree, ultimately determine part of its characteristics. Therefore, we have tested the goodness of the Undirected Binary Configuration Model (UBCM) in reproducing the structural features of the BLN: the UBCM recovers the disassortative and the hierarchical character of the BLN but underestimates the centrality of nodes; this suggests that the BLN is becoming an increasingly centralised network, more and more compatible with a core-periphery structure. Further inspection of the resilience of the BLN shows that removing hubs leads to the collapse of the network into many components, an evidence suggesting that this network may be a target for the so-called split attacks.

Bitcoin Transaction Networks: an overview of recent results

Nicolò Vallarano, Claudio Tessone, Tiziano Squartini

arXiv:2005.00114

Abstract

Cryptocurrencies are distributed systems that allow exchanges of native (and non-) tokens among participants. The complete historical bookkeeping and its wide availability opens up an unprecedented possibility, i.e., that of understanding the evolution of their network structure while gaining useful insight on the relationships between user behaviour and cryptocurrency pricing in exchange markets. In this contribution we review some of the most recent results concerning the structural properties of Bitcoin Transaction Networks, a generic name referring to a set of different constructs: the Bitcoin Address Network, the Bitcoin User Network and the Bitcoin Lightning Network. A common picture that emerges out of analysing them all is that of a system growing over time, which becomes increasingly sparse, and whose structural organization at the mesoscopic level is characterised by the presence of a statistically-significant core-periphery structure. Such a peculiar topology is matched by a highly unequal distribution of bitcoins, a result suggesting that Bitcoin is becoming an increasingly centralised system at different levels.

Network-based indicators of Bitcoin bubbles

Alexandre Bovet, Carlo Campajola, Jorge F. Lazo, Francesco Mottes, Iacopo Pozzana, Valerio Restocchi, Pietro Saggese, Nicoló Vallarano, Tiziano Squartini, Claudio J. Tessone

arXiv:1805.04460

Abstract

The functioning of the cryptocurrency Bitcoin relies on the open availability of the entire history of its transactions. This makes it a particularly interesting socio-economic system to analyse from the point of view of network science. Here we analyse the evolution of the network of Bitcoin transactions between users. We achieve this by using the complete transaction history from December 5th 2011 to December 23rd 2013. This period includes three bubbles experienced by the Bitcoin price. In particular, we focus on the global and local structural properties of the user network and their variation in relation to the different period of price surge and decline. By analysing the temporal variation of the heterogeneity of the connectivity patterns we gain insights on the different mechanisms that take place during bubbles, and find that hubs (i.e., the most connected nodes) had a fundamental role in triggering the burst of the second bubble. Finally, we examine the local topological structures of interactions between users, we discover that the relative frequency of triadic interactions experiences a strong change before, during and after a bubble, and suggest that the importance of the hubs grows during the bubble. These results provide further evidence that the behaviour of the hubs during bubbles significantly increases the systemic risk of the Bitcoin network, and discuss the implications on public policy interventions.