Anomaly detection in blockchain networks: A comprehensive survey

MU Hassan, MH Rehmani… - … Communications Surveys & …, 2022 - ieeexplore.ieee.org
Over the past decade, blockchain technology has attracted a huge attention from both
industry and academia because it can be integrated with a large number of everyday …

Cryptocurrency volatility: A review, synthesis, and research agenda

MS Ahmed, AA El-Masry, AI Al-Maghyereh… - Research in International …, 2024 - Elsevier
This paper takes part in the ongoing debate on the newly emerging field of financial
technology by systematically reviewing 164 articles on cryptocurrency volatility during the …

CBDC, Fintech and cryptocurrency for financial inclusion and financial stability

PK Ozili - Digital Policy, Regulation and Governance, 2022 - emerald.com
CBDC, Fintech and cryptocurrency for financial inclusion and financial stability | Emerald Insight
Books and journals Case studies Expert Briefings Open Access Publish with us Advanced …

LGBM: a machine learning approach for Ethereum fraud detection

RM Aziz, MF Baluch, S Patel, AH Ganie - International Journal of …, 2022 - Springer
Ethereum is a software platform that uses the concept of blockchain and decentralizes data
by distributing copies of smart contracts to thousands of individuals worldwide. Ethereum, as …

Deep learning in predicting cryptocurrency volatility

V D'Amato, S Levantesi, G Piscopo - Physica A: Statistical Mechanics and …, 2022 - Elsevier
This paper focuses on the prediction of cryptocurrency volatility. The stock market volatility
represents a very influential aspect that affects a wide range of decisions in business and …

Modified genetic algorithm with deep learning for fraud transactions of ethereum smart contract

RM Aziz, R Mahto, K Goel, A Das, P Kumar, A Saxena - Applied Sciences, 2023 - mdpi.com
Recently, the Ethereum smart contracts have seen a surge in interest from the scientific
community and new commercial uses. However, as online trade expands, other fraudulent …

[HTML][HTML] The COVID-19 outbreak and high frequency information transmission between major cryptocurrencies: Evidence from the VAR-DCC-GARCH approach

I Yousaf, S Ali - Borsa Istanbul Review, 2020 - Elsevier
Using intraday data, this study employs the VAR-DCC-GARCH model to examine return and
volatility transmission among Bitcoin, Ethereum, and Litecoin during the pre-COVID-19 and …

Discovering interlinkages between major cryptocurrencies using high-frequency data: new evidence from COVID-19 pandemic

I Yousaf, S Ali - Financial Innovation, 2020 - Springer
Through the application of the VAR-AGARCH model to intra-day data for three
cryptocurrencies (Bitcoin, Ethereum, and Litecoin), this study examines the return and …

Does volatility in cryptocurrencies drive the interconnectedness between the cryptocurrencies market? Insights from wavelets

SK Agyei, AM Adam, A Bossman… - Cogent Economics & …, 2022 - Taylor & Francis
We present a multi-scale and time-frequency analysis of the degree of integration and the
lead-lag relationship between six cryptocurrencies (ie, Bitcoin, Bitcoincash, Ethereum …

Cue the volatility spillover in the cryptocurrency markets during the COVID-19 pandemic: evidence from DCC-GARCH and wavelet analysis

O Özdemir - Financial Innovation, 2022 - Springer
This study investigates the dynamic mechanism of financial markets on volatility spillovers
across eight major cryptocurrency returns, namely Bitcoin, Ethereum, Stellar, Ripple, Tether …