---
title: TimesNet for Realized Volatility Prediction
url: https://www.ml-quant.com/papers/ssrn/4660025/
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 4660025
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4660025
featured: 2023-12-13
citations: 3
topic: Derivatives & Volatility
---


# TimesNet for Realized Volatility Prediction

The study shows that the TimesNet model is effective in predicting stock volatility, particularly during extreme market movements, making it a strong neural network benchmark in volatility research.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4660025
- Identifier: SSRN 4660025
- Released: 2023-12-10
- First featured: Quant Letter No. 29 (2023-12-13): https://www.ml-quant.com/issues/2023-12-13/
- Citations (Semantic Scholar): 3
- Published in: not yet
- Topic: Derivatives & Volatility

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