Bridging Econometrics and AI: VaR Estimation via Reinforcement Learning and GARCH Models

Fredy Pokou (), Jules Sadefo Kamdem () and François Benhmad ()
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Fredy Pokou: CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 - Centrale Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique, INOCS - Integrated Optimization with Complex Structure - Centre Inria de l'Université de Lille - Inria - Institut National de Recherche en Informatique et en Automatique - ULB - Université libre de Bruxelles - CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 - Centrale Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique
Jules Sadefo Kamdem: MRE - Montpellier Recherche en Economie - UM - Université de Montpellier
François Benhmad: MRE - Montpellier Recherche en Economie - UM - Université de Montpellier

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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 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.

Keywords: Deep reinforcement learning; Directional prediction; Imbalanced class problem; Value-at-Risk (search for similar items in EconPapers)
Date: 2025-04-01
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