Time-Series Foundation Model for Value-at-Risk

• URL: http://arxiv.org/abs/2410.11773v2
• Date: Mon, 28 Oct 2024 09:18:32 GMT
• Title: Time-Series Foundation Model for Value-at-Risk
• Authors: Anubha Goel, Puneet Pasricha, Juho Kanniainen,
• Abstract summary: Foundation models, pre-trained on vast and varied datasets, can be used in a zero-shot setting with relatively minimal data. We compare the performance of Google's model, called TimesFM, against conventional parametric and non-parametric models.
• Score: 9.090616417812306
• License:
• Abstract: This study is the first to explore the application 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. We also found that fine-tuning significantly improves the results, and the model should not be used in zero-shot settings.

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