Filling in Missing FX Implied Volatilities with Uncertainties: Improving VAE-Based Volatility Imputation

TL;DR

This paper enhances VAE-based methods for imputing missing FX implied volatilities by architectural modifications and better uncertainty handling, significantly improving accuracy over prior neural network approaches.

Contribution

It introduces simple architectural improvements and a modified imputation algorithm to better handle uncertainty, outperforming previous VAE methods and classical models.

Findings

Nearly halved error in low missingness regimes

Improved uncertainty estimates for imputed data

VAE modifications outperform prior neural approaches

Abstract

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 $\beta$-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.