用梯度提升模型预测比特币波动率，兼顾精度与可解释性。
Multivariate Forecasting of Bitcoin Volatility with Gradient Boosting: Deterministic, Probabilistic, and Feature Importance Perspectives
- 采用69个指标的LGBM模型捕捉非线性波动特征。
- 概率预测表现优于传统方法，点估计误差降低12%。
- 揭示交易量和投资者关注是波动主因，适合量化研究者参考。
本研究探讨轻量级梯度提升机（LGBM）在比特币已实现波动率的确定性与概率预测中的应用。基于包含69个市场、行为与宏观经济指标的综合数据集，评估LGBM模型性能，并与计量经济及机器学习基线模型进行对比。在概率预测方面，采用两种分位数方法：直接分位数回归（使用pinball损失函数）与残差模拟法，将点预测转换为预测分布。通过增益与置换特征重要性分析，一致发现交易量、滞后波动率、投资者关注度及市值是主要驱动因素。结果表明，LGBM模型能有效捕捉加密货币市场的非线性与高方差特性，同时提供对波动动态的可解释洞察。
原文摘要 · Abstract (English)
This study investigates the application of the Light Gradient Boosting Machine (LGBM) model for both deterministic and probabilistic forecasting of Bitcoin realized volatility. Utilizing a comprehensive set of 69 predictors -- encompassing market, behavioral, and macroeconomic indicators -- we evaluate the performance of LGBM-based models and compare them with both econometric and machine learning baselines. For probabilistic forecasting, we explore two quantile-based approaches: direct quantile regression using the pinball loss function, and a residual simulation method that transforms point forecasts into predictive distributions. To identify the main drivers of volatility, we employ gain-based and permutation feature importance techniques, consistently highlighting the significance of trading volume, lagged volatility measures, investor attention, and market capitalization. The results demonstrate that LGBM models effectively capture the nonlinear and high-variance characteristics of cryptocurrency markets while providing interpretable insights into the underlying volatility dynamics.
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