---
title: ML for Financial Risk Measurement
url: https://www.ml-quant.com/papers/repec/aza-rmfi00-y-2023-v-17-i-1-p-43-52/
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:aza:rmfi00:y:2023:v:17:i:1:p:43-52
source_url: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fhstalks.com%2Farticle%2F8214%2Fdownload%2F%3Bh%3Drepec%3Aaza%3Armfi00%3Ay%3A2023%3Av%3A17%3Ai%3A1%3Ap%3A43-52
featured: 2024-01-23
citations: unknown
topic: Econometrics & Forecasting
---


# ML for Financial Risk Measurement

A new sequential learning algorithm based on Kalman filtering has proven to be more effective than traditional methods in measuring financial market risk.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fhstalks.com%2Farticle%2F8214%2Fdownload%2F%3Bh%3Drepec%3Aaza%3Armfi00%3Ay%3A2023%3Av%3A17%3Ai%3A1%3Ap%3A43-52
- Identifier: RePEc:aza:rmfi00:y:2023:v:17:i:1:p:43-52
- Released: 2023-09-19
- First featured: Quant Letter No. 34 (2024-01-23): https://www.ml-quant.com/issues/2024-01-23/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Econometrics & Forecasting

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