---
title: Deep Learning and GARCH Models for Financial Volatility
url: https://www.ml-quant.com/papers/ssrn/4589950/
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: SSRN 4589950
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4589950
featured: 2023-10-04
citations: unknown
topic: Derivatives & Volatility
---


# Deep Learning and GARCH Models for Financial Volatility

A hybrid approach combining GARCH time series models with deep learning neural networks is proposed for forecasting financial volatility and risk, tested on S&P 500, gold, and Bitcoin prices.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4589950
- Identifier: SSRN 4589950
- Released: 2023-10-02
- First featured: Quant Letter No. 18 (2023-10-04): https://www.ml-quant.com/issues/2023-10-04/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility

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