When Deep Learning Meets Gamma Ruin: Why AI Option Strategies Fail under Volatility Clustering

Systematic option writing strategies, primarily Covered Calls (CC) and Cash-Secured Puts (CSP), are widely employed to harvest the Volatility Risk Premium (VRP). However, these strategies face significant gamma ruin risks during periods of volatility clustering.

To manage this, the PRISM (Probabilistic Regime Identification for Systematic Management) framework was designed. PRISM integrates a DLinear decomposition layer for trend isolation with a Transformer-based attention entropy signal intended to serve as an early warning indicator.

The sandbox simulation results (2018–2024) offer a sobering perspective.

The Hard Data: PRISM Under Stress

Despite its architectural sophistication, the strategy suffered catastrophic failures under realistic sandbox conditions, yielding a mean Sharpe Ratio of -3.83 (with individual seeds as low as -6.87). This demonstrates that even advanced neural networks cannot tame the asymmetric tail risk of short-dated (0DTE) options during liquidity shocks.

Why Did the Deep Learning Model Fail?

• Attention Entropy is Reactive, Not Predictive: The Transformer measures the disorder in its attention weights. By the time the model registers spiked entropy and closes positions, the price gap has already occurred. The signal lags the market dynamics.
• Asymmetric Leverage Effect: During market sell-offs, the volatility-of-volatility increases asymmetrically, causing put values to explode and creating massive drawdowns for cash-secured puts.
• Stochastic Residuals: The remainder component from the DLinear decomposition exhibited extreme kurtosis, which standard linear mapping layers failed to model accurately.

Practical Takeaways

Advanced deep learning architectures cannot eliminate the core mathematical risks of unhedged, short-dated option writing during periods of market stress. Going short gamma means accepting execution timing risk; AI cannot bypass the fundamental constraints of market microstructure.