Publicação

Predicting the implied volatility surface via deep learning

Ver documento

Detalhes bibliográficos
Resumo: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.
Autores principais:Lee, Nicolas Yong Joon
Assunto:Implied volatility surface ConvLSTM Stochastic volatility inspired Volatility forecasting Deep learning Self-attention mechanism Neural networks Volatility surface IVS calibration Options pricing
Ano:2025
País:Portugal
Tipo de documento:dissertação de mestrado
Tipo de acesso:acesso aberto
Instituição associada:Universidade Nova de Lisboa
Idioma:inglês
Origem:Repositório Institucional da UNL

Registos relacionados

Registos relacionados