# Time-Series Foundation AI Model for Value-at-Risk Forecasting

arXiv: https://arxiv.org/abs/2410.11773 · HTML: https://mlsanity.com/paper/2410.11773/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 38 references that are in the map.
- First posted on arXiv 15 Oct 2024.

## Builds on (most cited first, 13 of 13)

- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [A decoder-only foundation model for time-series forecasting](https://mlsanity.com/paper/2310.10688/) (2023)
- [TimeGPT-1](https://mlsanity.com/paper/2310.03589/) (2023)
- [Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis](https://mlsanity.com/paper/1712.00975/) (2017)
- [Deep Learning in Asset Pricing](https://mlsanity.com/paper/1904.00745/) (2019)
- [Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations](https://mlsanity.com/paper/1909.02210/) (2019)
- [Of Quantiles and Expectiles: Consistent Scoring Functions, Choquet Representations, and Forecast Rankings](https://mlsanity.com/paper/1503.08195/) (2015)
- [Hallucination Detection in Foundation Models for Decision-Making: A Flexible Definition and Review of the State of the Art](https://mlsanity.com/paper/2403.16527/) (2024)
- [Deep Learning Statistical Arbitrage](https://mlsanity.com/paper/2106.04028/) (2021)
- [Deep Generative Modeling for Financial Time Series with Application in VaR: A Comparative Review](https://mlsanity.com/paper/2401.10370/) (2024)
- [Data-driven Neural Architecture Learning For Financial Time-series Forecasting](https://mlsanity.com/paper/1903.06751/) (2019)
- [Multivariate time-series modeling with generative neural networks](https://mlsanity.com/paper/2002.10645/) (2020)
- [Generative Causal Inference](https://mlsanity.com/paper/2306.16096/) (2023)

## Built on by (most cited first, 2 of 2)

- [time2time: Causal Intervention in Hidden States to Simulate Rare Events in Time Series Foundation Models](https://mlsanity.com/paper/2509.05801/) (2025)
- [Generalisation Bounds of Zero-Shot Economic Forecasting using Time Series Foundation Models](https://mlsanity.com/paper/2506.15705/) (2025)

## Abstract (arXiv)

This study is the first to analyze the performance of a time-series foundation AI model for Value-at-Risk (VaR), which essentially forecasts the left-tail quantiles of returns. Foundation models, pre-trained on diverse datasets, can be applied in a zero-shot setting with minimal data or further improved through finetuning. We compare Google's TimesFM model to conventional parametric and non-parametric models, including GARCH and Generalized Autoregressive Score (GAS), using 19 years of daily returns from the SP 100 index and its constituents. Backtesting with over 8.5 years of out-of-sample data shows that the fine-tuned foundation model consistently outperforms traditional methods in actual-over-expected ratios. For the quantile score loss function, it performs comparably to the best econometric model, GAS. Overall, the foundation model ranks as the best or among the top performers across the 0.01, 0.025, 0.05, and 0.1 quantile forecasting. Fine-tuning significantly improves accuracy, showing that zero-shot use is not optimal for VaR.