---
title: Volatility Forecasting: Linear vs. Nonlinear
url: https://www.ml-quant.com/papers/repec/eee-empfin-v-78-y-2024-i-c-s0927539824000598/
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:empfin:v:78:y:2024:i:c:s0927539824000598
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0927539824000598%3Bh%3Drepec%3Aeee%3Aempfin%3Av%3A78%3Ay%3A2024%3Ai%3Ac%3As0927539824000598
featured: 2024-11-13
citations: unknown
topic: Derivatives & Volatility
---


# Volatility Forecasting: Linear vs. Nonlinear

Linear vs. Nonlinear: Machine learning models were found to be effective in forecasting global stock market volatility, with simpler models performing better for volatility-timing portfolios.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0927539824000598%3Bh%3Drepec%3Aeee%3Aempfin%3Av%3A78%3Ay%3A2024%3Ai%3Ac%3As0927539824000598
- Identifier: RePEc:eee:empfin:v:78:y:2024:i:c:s0927539824000598
- Released: 2024-11-13
- First featured: Quant Letter No. 74 (2024-11-13): https://www.ml-quant.com/issues/2024-11-13/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility

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