Results 81 to 90 of about 30,192 (248)
An empirical analysis of smart contracts: platforms, applications, and design patterns
, 2017 Smart contracts are computer programs that can be consistently executed by a network of mutually distrusting nodes, without the arbitration of a trusted authority. Because of their resilience to tampering, smart contracts are appealing in many scenarios,
B Marino +10 more
core +1 more source
The impact of cryptocurrency heists on Bitcoin's market efficiency
International Journal of Finance &Economics, EarlyView.Abstract Within the adaptive market hypothesis (AMH) framework, this study explores the dynamic impact of cryptocurrency heists on Bitcoin's market efficiency. By analysing Bitcoin's one‐minute price data, we calculate permutation entropy to assess market disorder and employ the complexity‐entropy causality plane to quantify structural changes in the ...
Mingnan Li, Viktor Manahov, John Ashton
wiley +1 more source
, 2021 Ethereum platform operates with rich spectrum of data structures and hashing and coding functions. The main source describing them is the Yellow paper, complemented by a lot of informal blogs. These sources are somehow limited. In particular, the Yellow paper does not ideally balance brevity and detail, in some parts it is very detail, while too ...
openaire +2 more sources
International Journal of Finance &Economics, EarlyView.ABSTRACT This research aims to explore and understand the dynamic nature of volatility connectedness between BRICS stock markets and various asset price implied volatility indices through a TVP‐VAR broadened connectedness approach. Results display nontrivial dynamic connectedness in the BRICS stock markets and uncertainties in different markets during ...
Halilibrahim Gökgöz +3 more
wiley +1 more source
Forecasting Ethereum’s volatility: an expansive approach using HAR models and structural breaks
Cogent Economics & FinanceCryptocurrencies have become a popular investment option and the Ethereum has become a mainstream cryptocurrency because of the additional functionality that can be accomplished with the backing of the powerful Ethereum network compared to Bitcoin.
Ruijie Chen
doaj +1 more source
Forecasting Digital Asset Return: An Application of Machine Learning Model
International Journal of Finance &Economics, EarlyView.ABSTRACT In this study, we aim to identify the machine learning model that can overcome the limitations of traditional statistical modelling techniques in forecasting Bitcoin prices. Also, we outline the necessary conditions that make the model suitable.
Vito Ciciretti +4 more
wiley +1 more source
The Operational Cost of Ethereum Airdrops [PDF]
, 2019 Efficient transfers to many recipients present a host of issues on Ethereum. First, accounts are identified by long and incompressible constants. Second, these constants have to be stored and communicated for each payment. Third, the standard interface for token transfers does not support lists of recipients, adding repeated communication to the ...
Michael Fröwis, Rainer Böhme
openaire +2 more sources
DISTRIBUTION OF ETHEREUM BLOCKCHAIN ADDRESSES
Scientific Journal of Astana IT University, 2020 In the contemporary world, Ethereum is a very reliable financial saving among cryptocurrencies. It is also well known as a blockchain platform for creating and launching its own cryptocurrency.
A. Seitenov, G. Smagulova
doaj
Mutual-Excitation of Cryptocurrency Market Returns and Social Media Topics [PDF]
, 2018 Cryptocurrencies have recently experienced a new wave of price volatility and interest; activity within social media communities relating to cryptocurrencies has increased significantly.
Gorse, Denise, Phillips, Ross C.
core +1 more source
Major Conundrums and Possible Solutions in DeFi Insurance
International Journal of Finance &Economics, EarlyView.ABSTRACT This paper empirically explores the early development of insurance projects in the decentralised finance (DeFi) industry, which is based on disruptive technologies like blockchain and smart contracts. A brief history of DeFi is narrated, stressing four risks of DeFi (volatility risk, cyberattack risk, liquidity risk, and regulation risk) and ...
Peng Zhou, Ying Zhang
wiley +1 more source