Filling in Missing FX Implied Volatilities with Uncertainties: Improving VAE-Based Volatility Imputation
This work addresses data imputation challenges in finance, specifically for FX options, but is incremental as it builds on existing VAE methods.
The paper tackled the problem of imputing missing implied volatilities for FX options by modifying variational autoencoder (VAE) architectures, resulting in significant performance improvements such as nearly halving the error in low missingness regimes.
Missing data is a common problem in finance and often requires methods to fill in the gaps, or in other words, imputation. In this work, we focused on the imputation of missing implied volatilities for FX options. Prior work has used variational autoencoders (VAEs), a neural network-based approach, to solve this problem; however, using stronger classical baselines such as Heston with jumps can significantly outperform their results. We show that simple modifications to the architecture of the VAE lead to significant imputation performance improvements (e.g., in low missingness regimes, nearly cutting the error by half), removing the necessity of using $β$-VAEs. Further, we modify the VAE imputation algorithm in order to better handle the uncertainty in data, as well as to obtain accurate uncertainty estimates around imputed values.