Résultats de recherche pour « Bitcoin »

The Bitcoin protocol allows to save arbitrary data on the blockchain through a special instruction of the scripting language, called OP_RETURN. A growing number of protocols exploit this feature to extend the range of applications of the Bitcoin blockchain beyond transfer of currency. A point of debate in the Bitcoin community is whether loading data through OP_RETURN can negatively affect the performance of the Bitcoin network with respect to its primary goal. This paper is an empirical study of the usage of OP_RETURN over the years. We identify several protocols based on OP_RETURN, which we classify by their application domain. We measure the evolution in time of the usage of each protocol, the distribution of OP_RETURN transactions by application domain, and their space consumption.

Bitcoin's enormous success has inspired the development of alternative blockchains, such as consortium chains. Several cross-chain protocols have been proposed as ways of connecting these universes of individual blockchains in a distributed and secure manner. In this paper, we present Niji, a new cross-chain protocol that allows parties to perform virtual Bitcoin payment securely on a consortium chain, without any trusted third-party or mediators. Our work focuses on the issue that it is difficult for a consortium chain's token to hold a stable market value, and Niji makes it possible for smart contract services to acquire means of payment in the consortium chain. With the Bitcoin payment channel built on the consortium chain, the process from payment to service provision runs autonomously without any interaction between parties. Niji introduces the concept of a transaction template to validate Bitcoin payments efficiently on different blockchains, and it allows a service provider to delegate all of its tasks for verifying state updates to a smart contract on the consortium chain. We also propose a novel bi-directional payment channel adapted for design of the Niji protocol, which can update payments non-interactively between parties. We implemented a prototype of the Niji protocol and conducted an experiment measuring the computational cost and latency that demonstrates the protocol's feasibility on practical platforms.

This paper analyses the relationship between BitCoin price and supply-demand fundamentals of BitCoin, global macro-financial indicators and BitCoin attractiveness for investors. Using daily data for the period 2009-2014 and applying time-series analytical mechanisms, we find that BitCoin market fundamentals and BitCoin attractiveness for investors have a significant impact on BitCoin price. Our estimates do not support previous findings that the macro-financial developments are driving BitCoin price.

This is the first paper that estimates the price determinants of BitCoin in a Generalised Autoregressive Conditional Heteroscedasticity framework using high frequency data. Derived from a theoretical model, we estimate BitCoin transaction demand and speculative demand equations in a GARCH framework using hourly data for the period 2013-2018. In line with the theoretical model, our empirical results confirm that both the BitCoin transaction demand and speculative demand have a statistically significant impact on the BitCoin price formation. The BitCoin price responds negatively to the BitCoin velocity, whereas positive shocks to the BitCoin stock, interest rate and the size of the BitCoin economy exercise an upward pressure on the BitCoin price.

This paper proposes a classification model for predicting the main activity of bitcoin addresses based on their balances. Since the balances are functions of time, we apply methods from functional data analysis; more specifically, the features of the proposed classification model are the functional principal components of the data. Classifying bitcoin addresses is a relevant problem for two main reasons: to understand the composition of the bitcoin market, and to identify addresses used for illicit activities. Although other bitcoin classifiers have been proposed, they focus primarily on network analysis rather than curve behavior. Our approach, on the other hand, does not require any network information for prediction. Furthermore, functional features have the advantage of being straightforward to build, unlike expert-built features. Results show improvement when combining functional features with scalar features, and similar accuracy for the models using those features separately, which points to the functional model being a good alternative when domain-specific knowledge is not available.