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This research aims to enhance intraday VIX derivatives hedging by utilizing a deep filtering framework for better volatility risk management.

May 22, 2026Open Access

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

Key Points

This research aims to enhance intraday VIX derivatives hedging by utilizing a deep filtering framework for better volatility risk management.
Developed a deep filtering framework for VIX hedging using neural networks as non-linear Bayesian filters.
Utilized tick-by-tick market data from 2010 to 2024 to analyze performance.
Optimized hedging portfolio variance instead of relying on price prediction accuracy.
Achieved a 42% reduction in hedging error variance compared to traditional methods.
Increased Sharpe ratios by 68%, indicating improved risk-adjusted returns.
Outperformed traditional Delta hedging approaches across various market regimes.

Abstract

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.

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