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Summary
This study evaluates the performance of Google's foundation model for time series, TimesFM, in estimating Value-at-Risk (VaR) for the S&P 100 index and its 91 constituents. The results show that fine-tuning the model with domain-specific data leads to significant statistical improvements, outperforming traditional econometric methods.
Highlights
- TimesFM model is used for VaR forecasting and compared with traditional econometric methods.
- Fine-tuning the model with domain-specific data leads to significant statistical improvements.
- The fine-tuned TimesFM model outperforms traditional econometric methods in VaR forecasting.
- The model's performance is evaluated using the Actual vs. Expected (AE) ratio and the Quantile Score Loss Function.
- The results show that TimesFM is a viable alternative to traditional econometric methods for VaR forecasting.
- The study highlights the potential of foundation models in financial time-series modeling.
- The adoption of foundation models can partially reduce the need for mathematical modeling in finance.
Key Insights
- The use of foundation models like TimesFM can revolutionize financial time-series modeling by providing a data-driven approach that can outperform traditional econometric methods.
- Fine-tuning the model with domain-specific data is crucial for achieving good performance in VaR forecasting, as it allows the model to adapt to the specific characteristics of the data.
- The Actual vs. Expected (AE) ratio and the Quantile Score Loss Function are effective metrics for evaluating the performance of VaR forecasting models.
- Traditional econometric methods, such as GARCH and GAS models, can be outperformed by fine-tuned foundation models in VaR forecasting.
- The adoption of foundation models in finance can have significant implications for risk management and decision-making, as they can provide more accurate and reliable forecasts.
- However, the "black-box" nature of foundation models can make it difficult to understand how they arrive at their predictions, which can be a concern in high-stakes financial settings.
- Improving the interpretability of foundation models is critical to align with regulatory expectations and support their wider use in practice.
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Citation
Goel, A., Pasricha, P., & Kanniainen, J. (2024). Time-Series Foundation Model for Value-at-Risk Forecasting (Version 5). arXiv. https://doi.org/10.48550/ARXIV.2410.11773