Deep learning models reconstruct volatility surfaces with no-arbitrage constraints

Researchers have developed deep learning models to reconstruct implied volatility surfaces from limited and noisy option data, adhering to no-arbitrage constraints. The study compared various neural network architectures, including Transformers and U-Nets, against traditional SVI parameterizations using real market data. Findings indicate that Transformer and U-Net models offer superior reconstruction accuracy, especially when data is sparse, and that incorporating soft arbitrage penalties effectively reduces violations with only a minor impact on accuracy. AI

IMPACT This research could lead to more accurate financial modeling and risk assessment in quantitative finance.

RANK_REASON This is a research paper detailing a novel application of deep learning to a quantitative finance problem. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

• Pablo Rodriguez Manzi
• Transformer
• U-Net

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →