Intraday VIX Hedging via Deep Filtering: A State-Space Approach to Volatility Risk Management

This paper develops a deep filtering framework for intraday VIX derivatives hedging that generalizes traditional stochastic volatility models. Volatility is mod-eled as a latent process in a partially observed state-space system, where neural networks serve as non-linear Bayesian filters to infer hidden states from high-frequency market data. The methodology directly optimizes hedging portfolio variance rather than price prediction accuracy. Using tick-by-tick data from 2010-2024, the approach demonstrates 42% reduction in hedging error variance, 68% higher Sharpe ratios, and superior performance across all market regimes compared to traditional Delta hedging methods. The framework maintains robust per-formance after accounting for transaction costs and market microstructure effects.