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Time-Series Foundation Model for Value-at-Risk

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  • Anubha Goel
  • Puneet Pasricha
  • Juho Kanniainen

Abstract

This study is the first to explore the performance of a time-series foundation model for Value-at-Risk (VaR) estimation. Foundation models, pre-trained on vast and varied datasets, can be used in a zero-shot setting with relatively minimal data or further improved through finetuning. We compare the performance of Google's model, called TimesFM, against conventional parametric and non-parametric models, including GARCH, Generalized Autoregressive Score (GAS), and empirical quantile estimates, using daily returns from the S\&P 100 index and its constituents over 19 years. Our backtesting results indicate that in terms of the actual-over-expected ratio, the fine-tuned TimesFM model consistently outperforms traditional methods. Regarding the quantile score loss function, it achieves performance comparable to the best econometric approach, the GAS model. Overall, the foundation model is either the best or among the top performers in forecasting VaR across the 0.01, 0.025, 0.05, and 0.1 VaR levels. Fine-tuning significantly improves accuracy, indicating that zero-shot use is not optimal for VaR estimation.

Suggested Citation

  • Anubha Goel & Puneet Pasricha & Juho Kanniainen, 2024. "Time-Series Foundation Model for Value-at-Risk," Papers 2410.11773, arXiv.org, revised Dec 2024.
    • Handle: RePEc:arx:papers:2410.11773
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    References listed on IDEAS

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