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Predicting the implied volatility surface via deep learning
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This thesis explores deep learning techniques for predicting the implied volatility
surface (IVS), a critical component in options pricing and risk management. By applying
ConvLSTM and Self-Attention mechanisms, the study evaluates their ability to capture spatial
and temporal patterns across strikes and maturities. Results show that grid-based ConvLSTM
excels in short-term forecasting, while Self-Attention enhances long-term accuracy by capturing
global dependencies. The models were retrained and evaluated under volatile regimes, including
the COVID-19 crash, testing their robustness in extreme market conditions. The findings
contribute to improved IV surface predictions, benefiting strategies like volatility arbitrage and
dynamic hedging.
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Implied volatility surface ConvLSTM Stochastic volatility inspired Volatility forecasting Deep learning Self-attention mechanism Neural networks Volatility surface IVS calibration Options pricing