arXiv:2606.04576v1 Announce Type: cross Abstract: Learning Value-at-Risk (VaR) and Expected Shortfall (ES) is important for managing financial risks effectively. Existing approaches with limited parameters are vulnerable to model misspecification in the era of big data. To address this limitation, we propose a large tail risk model, the retrieval-enhanced self-grouping autoencoder (ReSGA), which is designed with millions of parameters to exploit the rich cross-sectional dependence and long-term temporal dynamics of assets using their characteristics. Applied to monthly US equity returns from 1
The increasing availability of big data in financial markets and advancements in large AI models necessitate more sophisticated risk management tools to address the limitations of traditional methods.
This development allows financial institutions to manage tail risks more effectively, potentially preventing significant losses and improving the stability of financial systems.
The ability to use large AI models like ReSGA for Value-at-Risk and Expected Shortfall calculations means more accurate and robust financial risk assessment, moving beyond simpler, parameter-limited models.
- · Financial institutions with large datasets
- · AI/ML developers in finance
- · Quantitative analysts
- · Investors seeking more robust risk assessments
- · Traditional statistical risk modeling firms
- · Firms reliant on outdated risk management methodologies
- · Companies with limited access to financial data
More accurate financial risk modeling and potentially reduced financial volatility.
Increased adoption of large AI models in asset management and regulatory compliance, and a shift in demand for financial data infrastructure.
Enhanced systemic resilience against financial crises as institutions become better equipped to anticipate and mitigate extreme events, potentially influencing international capital flows and regulatory frameworks.
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Read at arXiv cs.LG