# Multivariate Forecasting of Bitcoin Volatility with Gradient Boosting: Deterministic, Probabilistic, and Feature Importance Perspectives

arXiv: https://arxiv.org/abs/2511.20105 · HTML: https://mlsanity.com/paper/2511.20105/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 25 Nov 2025.

## Builds on (most cited first, 6 of 6)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Forecasting Bitcoin volatility spikes from whale transactions and CryptoQuant data using Synthesizer Transformer models](https://mlsanity.com/paper/2211.08281/) (2022)
- [Beyond Trading Data: The Hidden Influence of Public Awareness and Interest on Cryptocurrency Volatility](https://mlsanity.com/paper/2202.08967/) (2022)
- [Stacking for Probabilistic Short-term Load Forecasting](https://mlsanity.com/paper/2406.10718/) (2024)
- [Probabilistic Forecasting Cryptocurrencies Volatility: From Point to Quantile Forecasts](https://mlsanity.com/paper/2508.15922/) (2025)

## Abstract (arXiv)

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.