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.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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