---
title: Learn the measure, estimate the moment: machine-learned drivers in dynamic conditional correlation models
url: https://www.ml-quant.com/papers/repec/cdf-wpaper-2026-12/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-10-02
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:cdf:wpaper:2026/12
source_url: https://econpapers.repec.org/RePEc:cdf:wpaper:2026/12
featured: 2026-10-02
citations: unknown
topic: Derivatives & Volatility
---


# Learn the measure, estimate the moment: machine-learned drivers in dynamic conditional correlation models

Combining machine-learned forecasts of realized measures with dynamic conditional correlation models improves correlation matrix forecasts, producing valid predictions and beating realized-driver baselines across multiple horizons.

- Source: https://econpapers.repec.org/RePEc:cdf:wpaper:2026/12
- Identifier: RePEc:cdf:wpaper:2026/12
- Released: 2026-09-30
- First featured: Quant Letter No. 133 (2026-10-02): https://www.ml-quant.com/issues/2026-10-02/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility
- Authors: Xu, Yongdeng

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