---
title: Predicting VIX Trends
url: https://www.ml-quant.com/papers/repec/taf-quantf-v-24-y-2024-i-12-p-1857-1873/
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:taf:quantf:v:24:y:2024:i:12:p:1857-1873
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1080%2F14697688.2024.2439458%3Bh%3Drepec%3Ataf%3Aquantf%3Av%3A24%3Ay%3A2024%3Ai%3A12%3Ap%3A1857-1873
featured: 2025-02-19
citations: unknown
topic: Derivatives & Volatility
---


# Predicting VIX Trends

The study uses machine learning to predict the CBOE Volatility Index, finding that weekly jobless claim data significantly impacts market volatility and improves trading strategies' resilience.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1080%2F14697688.2024.2439458%3Bh%3Drepec%3Ataf%3Aquantf%3Av%3A24%3Ay%3A2024%3Ai%3A12%3Ap%3A1857-1873
- Identifier: RePEc:taf:quantf:v:24:y:2024:i:12:p:1857-1873
- Released: 2024-03-23
- First featured: Quant Letter No. 85 (2025-02-19): https://www.ml-quant.com/issues/2025-02-19/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility

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