---
title: Capturing Heterogeneity: Machine Learning Approaches to Implied Volatility Forecasting
url: https://www.ml-quant.com/papers/repec/fip-fedgfe-103519/
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:fip:fedgfe:103519
source_url: https://econpapers.repec.org/RePEc:fip:fedgfe:103519
featured: 2026-09-25
citations: unknown
topic: Derivatives & Volatility
---


# Capturing Heterogeneity: Machine Learning Approaches to Implied Volatility Forecasting

Tree-based models partition the option surface by moneyness and maturity to forecast volatility, reducing one-month-ahead errors by 13 percent versus benchmark models.

- Source: https://econpapers.repec.org/RePEc:fip:fedgfe:103519
- Identifier: RePEc:fip:fedgfe:103519
- Released: 2026-09-17
- First featured: Quant Letter No. 132 (2026-09-25): https://www.ml-quant.com/issues/2026-09-25/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility
- Authors: Hyung Joo Kim, Dong Hwan Oh

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