---
title: Ensemble Boosting Trees for Volatility Forecasting
url: https://www.ml-quant.com/papers/repec/eee-reveco-v-92-y-2024-i-c-p-1595-1615/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:eee:reveco:v:92:y:2024:i:c:p:1595-1615
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1059056024001643%3Bh%3Drepec%3Aeee%3Areveco%3Av%3A92%3Ay%3A2024%3Ai%3Ac%3Ap%3A1595-1615
featured: 2024-05-15
citations: unknown
topic: Derivatives & Volatility
---


# Ensemble Boosting Trees for Volatility Forecasting

The study finds ensemble boosting tree models, particularly CatBoost and LightGBM, more effective than traditional models in predicting China's crude oil futures volatility, with macroeconomic and HAR-type variables impacting forecasts differently.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1059056024001643%3Bh%3Drepec%3Aeee%3Areveco%3Av%3A92%3Ay%3A2024%3Ai%3Ac%3Ap%3A1595-1615
- Identifier: RePEc:eee:reveco:v:92:y:2024:i:c:p:1595-1615
- Released: 2024-05-15
- First featured: Quant Letter No. 49 (2024-05-15): https://www.ml-quant.com/issues/2024-05-15/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility

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