2026/05/20 by Pablo Rodriguez Manzi · 1 voice
Computer Science · Decision Sciences · Economics, Econometrics and Finance · #Arbitrage #Consistency (knowledge bases) #Convolutional neural network #Deep learning #Implied volatility #Regularization (linguistics) #Risk and Portfolio Optimization #Stochastic processes and financial applications #Stock Market Forecasting Methods #Volatility (finance) #cs.LG #q-fin.CP
paper · pdf · doi:10.48550/arxiv.2605.24031
openalex publication_date 2026/05/20 · arxiv published 2026/05/20 · arxiv updated 2026/05/20 · openalex created_date 2026/05/27 · openalex updated_date 2026/07/28
We study the reconstruction of implied volatility surfaces from sparse and noisy option quotes using deep learning models under no-arbitrage constraints. We compare multiple neural architectures, including multilayer perceptrons, convolutional networks, U-Nets, variational autoencoders, and Transformer-based models against classical SVI parameterizations on option market data. Results show that Transformer and U-Net architectures achieve strong reconstruction accuracy, particularly under sparse observation regimes, while soft arbitrage penalties significantly reduce arbitrage violations with moderate impact on reconstruction error. We further analyze the trade-off between accuracy and arbitrage consistency across architectures and regularization strengths.