Time-Series Foundation AI Model for Value-at-Risk Forecasting (2410.11773v7)

Abstract: 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.

• The paper demonstrates that fine-tuning the TimesFM model significantly improves VaR estimation compared to zero-shot and traditional econometric approaches.
• It employs robust backtesting techniques, including quantile scores and Dynamic Quantile tests, to evaluate performance across multiple VaR levels.
• The study highlights the potential of AI-driven foundation models to reshape financial risk analytics despite challenges in model interpretability and computational scalability.

Time-Series Foundation Model for Value-at-Risk: An Expert Overview

This paper explores the application of a foundation model, TimesFM, for estimating Value-at-Risk (VaR), marking an initial foray into using such models for this financial risk metric. The research juxtaposes TimesFM's performance against traditional econometric models like GARCH and GAS, employing daily return data from the S&P 100 index over 19 years—creating a rich dataset primed for empirical analysis.

Methodological Insights

Foundation models, distinguished by their pre-training on expansive datasets, present a dual opportunity: they can operate in a zero-shot context or be fine-tuned to specific tasks. In this paper, both strategies are assessed. The TimesFM model, developed by Google, is subjected to rigorous backtesting, producing performance metrics across various VaR levels: 0.01, 0.025, 0.05, and 0.10.

The traditional econometric benchmarks include both parametric models such as GARCH, with normal and Student's t-distributed errors, and non-parametric methods like rolling window quantile estimates. These methods are complemented by the GAS model, known for its robust performance in dynamic quantile estimation.

Key Findings

From the analysis, several compelling conclusions arise:

1. Superior Performance with Fine-Tuning: The fine-tuned TimesFM consistently outperforms both its pre-trained configuration and traditional econometric models in terms of the Actual-over-Expected (AE) violation ratios, particularly at lower quantile levels. This emphasizes the value of domain-specific adjustments to foundation models.
2. Quantile Score Metrics: TimesFM achieves comparable performance to the GAS model when evaluated using the quantile score, a critical metric for assessing VaR accuracy.
3. Evaluation Metrics and Backtesting: The research employs an array of backtesting techniques, such as unconditional and conditional coverage tests along with Dynamic Quantile (DQ) tests, affirming TimesFM’s statistical adequacy and dynamic performance.
4. Computational Practicality: Despite its robust prediction capabilities, the TimesFM model showcases a marked improvement over the zero-shot approach, illustrating the necessity of fine-tuning for complex financial datasets. However, the pre-trained model was not effective enough in zero-shot settings, revealing limitations in directly applying foundation models without adaptation.

Theoretical and Practical Implications

The implications are multifold. Theoretically, this paper champions the integration of AI-driven foundation models into financial analytics, which could herald a paradigm shift in risk management practices. Practically, this provides a template for deploying such models in real-world financial settings, potentially offering more responsive and less parametrically constrained predictive capabilities.

Foundation models, like TimesFM, bring flexibility and efficiency to VaR estimation, yet they also introduce challenges, notably in transparency and regulatory compliance. Understanding their internal mechanics remains complex. Future research could focus on enhancing model interpretability and developing methodologies that blend model sophistication with financial intuition. Furthermore, the computational demands of fine-tuning remain a barrier for smaller institutions, suggesting a need for scalable solutions that democratize access to high-performance forecasting.

In conclusion, this investigation illuminates the potential of foundation models in financial risk estimation, setting a promising precedent for their broader utilization. The transition from conventional econometric techniques to foundation models could redefine best practices in financial prediction, though this will necessitate careful handling of interpretability and computational cost issues.