Transaction activity and bitcoin realized volatility

DOI10.1016/j.orl.2021.06.016OpenAlexW3185009217MaRDI QIDQ2060362

Konstantinos Gkillas, Manolis Tzagarakis, Maria Tantoula

Publication date: 13 December 2021

Published in: Operations Research Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.orl.2021.06.016

zbMATH Keywords

random forestsrealized volatilitybitcointransaction activity

Mathematics Subject Classification ID

Operations research, mathematical programming (90-XX)

Cites Work

• Unnamed Item
• Approximately normal tests for equal predictive accuracy in nested models
• Bitcoin price forecasting with neuro-fuzzy techniques
• Threshold bipower variation and the impact of jumps on volatility forecasting
• Volatility forecasting accuracy for Bitcoin
• Volatility and return jumps in Bitcoin
• Deep learning with long short-term memory networks for financial market predictions
• Deep neural networks, gradient-boosted trees, random forests: statistical arbitrage on the S\&P 500
• An application of extreme value theory to cryptocurrencies
• Empirical evidence on the importance of aggregation, asymmetry, and jumps for volatility prediction
• A machine learning efficient frontier
• Transform Analysis and Asset Pricing for Affine Jump-diffusions
• Modeling and Forecasting Realized Volatility
• Random forests

This page was built for publication: Transaction activity and bitcoin realized volatility