Temporal mixture ensemble models for probabilistic forecasting of intraday cryptocurrency volume

DOI10.1007/s10203-021-00344-9zbMath1480.91329OpenAlexW3189911589MaRDI QIDQ2064616

Fabrizio Lillo, Tian Guo, Nino Antulov-Fantulin

Publication date: 6 January 2022

Published in: Decisions in Economics and Finance (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10203-021-00344-9

zbMATH Keywords

machine learningeconometricscryptocurrency marketstemporal mixture ensemble

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Actuarial science and mathematical finance (91G99)

Uses Software

• Scikit
• Rugarch
• XGBoost
• Adam

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Greedy function approximation: A gradient boosting machine.
• Speculative bubbles in bitcoin markets? An empirical investigation into the fundamental value of bitcoin
• Volatility estimation for Bitcoin: a comparison of GARCH models
• Generalized autoregressive conditional heteroscedasticity
• Limit order books
• Estimation in the Three-Parameter Lognormal Distribution
• The role of volume in order book dynamics: a multivariate Hawkes process analysis
• Evolution of high-frequency systematic trading: a performance-driven gradient boosting model
• Direct multiperiod forecasting for algorithmic trading
• Randomized Gradient Boosting Machine
• OPTIMAL EXECUTION OF A VWAP ORDER: A STOCHASTIC CONTROL APPROACH