Operator Deep Smoothing for Implied Volatility
By Lukas Gonon, Antoine Jacquier, Ruben Wiedemann
Rating
1692
Battle Count: 61
Relevance
9/10
Highly relevant for options trading, market making, and risk management in quantitative finance
Implementation Complexity
7/10
Requires understanding of neural operators and graph neural networks, as well as significant computational resources for training
Reproducibility
4/5
Code is available on GitHub, detailed model configuration and training process provided
About this paper
Methodology: Operator Deep Smoothing. Problem types: Regression, Time Series Forecasting, Interpolation.
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