---
title: LLMVaR Risk Forecasting
url: https://www.ml-quant.com/papers/ssrn/5104383/
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 5104383
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5104383
featured: 2025-01-23
citations: unknown
topic: Econometrics & Forecasting
---


# LLMVaR Risk Forecasting

The research introduces new methods for forecasting financial risk using large language models, finding these models effective for short-term but traditional models superior for long-term financial risk management.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5104383
- Identifier: SSRN 5104383
- Released: 2025-01-20
- First featured: Quant Letter No. 83 (2025-01-23): https://www.ml-quant.com/issues/2025-01-23/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Econometrics & Forecasting

## Related

- [Soft Measures for Extracting Causal Collective Intelligence](https://www.ml-quant.com/papers/arxiv/2409.18911/): A study uses large language models to extract fuzzy cognitive maps from text, but emphasizes the need for specific soft similarity measures for this process.
- [FinCARE: Financial Causal Analysis with Reasoning and Evidence](https://www.ml-quant.com/papers/arxiv/2510.20221/): KG+LLM for Financial Causal Discovery: Combines SEC knowledge graphs, LLM reasoning, and causal discovery to build more accurate finance‑grounded causal models.
- [TKAN: Temporal Kolmogorov-Arnold Networks](https://www.ml-quant.com/papers/ssrn/4825654/): The article presents Temporal Kolomogorov-Arnold Networks (TKANs), a new neural network design that merges the benefits of Recurrent Neural Networks and Long Short-Term Memory for improved multistep time series forecasting.
- [Robust agents learn causal world models](https://www.ml-quant.com/papers/arxiv/2402.10877/): The research suggests that intelligent agents must learn an approximate causal model to generalize to new domains, impacting fields like transfer learning and causal inference.
- [The Evolution of Unobserved Skill Returns in the U.S.: A New Approach Using Panel Data](https://www.ml-quant.com/papers/arxiv/2501.09917/): The study disputes the common view that wage inequality in the US is due to unobserved skills, instead attributing it to increasing skill volatility.
- [Exploring the heterogeneous impacts of Indonesia’s conditional cash transfer scheme (PKH) on maternal health care utilisation using instrumental causal forests](https://www.ml-quant.com/papers/arxiv/2501.12803/): The research uses machine learning to study the effects of Indonesia's conditional cash transfer scheme on maternal health care, finding significant variations based on supply-side factors and poverty indicators.
