---
title: Portfolio Allocation with Graphical Lasso
url: https://www.ml-quant.com/papers/repec/oup-jfinec-v-22-y-2024-i-3-p-670-695/
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:oup:jfinec:v:22:y:2024:i:3:p:670-695.
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1093%2Fjjfinec%2Fnbad011%3Bh%3Drepec%3Aoup%3Ajfinec%3Av%3A22%3Ay%3A2024%3Ai%3A3%3Ap%3A670-695.
featured: 2024-07-24
citations: unknown
topic: Portfolio & Allocation
---


# Portfolio Allocation with Graphical Lasso

The Factor Graphical Lasso (FGL) framework, which combines graphical models with the factor structure, consistently estimates portfolio weights and risk exposure, and outperforms several key competitors in portfolio allocation.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1093%2Fjjfinec%2Fnbad011%3Bh%3Drepec%3Aoup%3Ajfinec%3Av%3A22%3Ay%3A2024%3Ai%3A3%3Ap%3A670-695.
- Identifier: RePEc:oup:jfinec:v:22:y:2024:i:3:p:670-695.
- Released: 2024-07-24
- First featured: Quant Letter No. 58 (2024-07-24): https://www.ml-quant.com/issues/2024-07-24/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Portfolio & Allocation

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