---
title: Bagging vs combination for oil futures volatility
url: https://www.ml-quant.com/papers/repec/eee-reveco-v-87-y-2023-i-c-p-457-467/
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:87:y:2023:i:c:p:457-467
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1059056023001594%3Bh%3Drepec%3Aeee%3Areveco%3Av%3A87%3Ay%3A2023%3Ai%3Ac%3Ap%3A457-467
featured: 2023-07-12
citations: unknown
topic: Derivatives & Volatility
---


# Bagging vs combination for oil futures volatility

The bagging method in machine learning is more effective than traditional models in predicting oil futures volatility, especially during the COVID-19 pandemic, with economic policy uncertainty indices being more useful than macroeconomic variables.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1059056023001594%3Bh%3Drepec%3Aeee%3Areveco%3Av%3A87%3Ay%3A2023%3Ai%3Ac%3Ap%3A457-467
- Identifier: RePEc:eee:reveco:v:87:y:2023:i:c:p:457-467
- Released: 2023-07-12
- First featured: Quant Letter No. 7 (2023-07-12): https://www.ml-quant.com/issues/2023-07-12/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility

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