Bridging Econometrics and AI: VaR Estimation via Reinforcement Learning and GARCH Models
This paper proposes a dynamic Value-at-Risk estimation framework that combines reinforcement learning with GARCH models for optimized risk management.
What it examines
Study introduces a novel VaR estimation method that combines GARCH volatility models with deep reinforcement learning. It reformulates return forecasting as an imbalanced classification problem using DDQN to adjust risk dynamically, aiming to improve risk prediction and capital efficiency in volatile market conditions.
What it concludes
The DDQN-based VaR model outperforms traditional methods by reducing violations and capital requirements. It adapts well to volatile markets, offering applications in financial risk forecasting, capital management, and regulatory compliance. Future research could explore alternative reinforcement techniques and further optimize the model.
Evidence objects
The paper introduces a hybrid method merging traditional models like $GARCH$ with deep reinforcement learning to dynamically adjust $Value$-$at$-$Risk$, reducing violation frequencies and lowering required capital reserves during market turbulence.
key_findings bullet 1 · key_findings · validation V0
Researchers reformulated risk forecasting into an imbalanced classification task using the Double Deep Q-Network ($DDQN$), introducing $Classification$-$Adjusted$$VaR$ which adaptively sets risk thresholds based on directional predictions under fluctuating market conditions.
key_findings bullet 2 · key_findings · validation V0
Empirical analysis on Euro Stoxx 50 data shows the new model outperforms traditional and machine learning approaches in accuracy, $F1$-score, and recall, remarkably despite calibration challenges and limited interpretability overall.
key_findings bullet 3 · key_findings · validation V0
Integrating classical econometric $GARCH$ models with deep reinforcement learning, the paper introduces a novel framework for improved $VaR$ estimation in volatile markets. Addressing class imbalance in predictions, it uniquely bridges econometrics and AI, enhancing risk forecasting and capital allocation. This approach offers compelling insights and advancements in financial risk management.
key_findings bullet 4 · key_findings · validation V0
Raw abstract and provenance
Abstract: In an environment of increasingly volatile financial markets, the accurate estimation of risk remains a major challenge. Traditional econometric models, such as GARCH and its variants, are based on assumptions that are often too rigid to adapt to the complexity of the current market dynamics. To overcome these limitations, we propose a hybrid framework for Value-at-Risk (VaR) estimation, combining… ▽ More In an environment of increasingly volatile financial markets, the accurate estimation of risk remains a major challenge. Traditional econometric models, such as GARCH and its variants, are based on assumptions that are often too rigid to adapt to the complexity of the current market dynamics. To overcome these limitations, we propose a hybrid framework for Value-at-Risk (VaR) estimation, combining GARCH volatility models with deep reinforcement learning. Our approach incorporates directional market forecasting using the Double Deep Q-Network (DDQN) model, treating the task as an imbalanced classification problem. This architecture enables the dynamic adjustment of risk-level forecasts according to market conditions. Empirical validation on daily Eurostoxx 50 data covering periods of crisis and high volatility shows a significant improvement in the accuracy of VaR estimates, as well as a reduction in the number of breaches and also in capital requirements, while respecting regulatory risk thresholds. The ability of the model to adjust risk levels in real time reinforces its relevance to modern and proactive risk management. △ Less
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