---
title: Forecasting Global Stock Market Volatility with GNN Model
url: https://www.ml-quant.com/papers/repec/wly-jforec-v-42-y-2023-i-7-p-1539-1559/
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:wly:jforec:v:42:y:2023:i:7:p:1539-1559
source_url: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fdoi.org%2F10.1002%2Ffor.2975%3Bh%3Drepec%3Awly%3Ajforec%3Av%3A42%3Ay%3A2023%3Ai%3A7%3Ap%3A1539-1559
featured: 2023-10-12
citations: unknown
topic: Derivatives & Volatility
---


# Forecasting Global Stock Market Volatility with GNN Model

The article discusses a study that introduces a new volatility forecasting model for global market indices. This model uses a spatial-temporal graph neural network (GNN) and performs better than existing models in short- and mid-term forecasting, potentially leading to economic benefits for investors.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fdoi.org%2F10.1002%2Ffor.2975%3Bh%3Drepec%3Awly%3Ajforec%3Av%3A42%3Ay%3A2023%3Ai%3A7%3Ap%3A1539-1559
- Identifier: RePEc:wly:jforec:v:42:y:2023:i:7:p:1539-1559
- Released: 2023-10-12
- First featured: Quant Letter No. 20 (2023-10-12): https://www.ml-quant.com/issues/2023-10-12/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility

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