Can I read Interpolation of Missing Swaption Volatility Data using Gibbs Sampling on Variational Autoencoders on EtoBox?

Interpolation of Missing Swaption Volatility Data using Gibbs Sampling on Variational Autoencoders by Richert, Ivo; Buch, Robert is a scholarly article available to read on EtoBox.

What is Interpolation of Missing Swaption Volatility Data using Gibbs Sampling on Variational Autoencoders about?

Albeit of crucial interest for both financial practitioners and researchers, market-implied volatility data of European swaptions often exhibit large portions of missing quotes due to illiquidity of the various underlying swaption instruments. In this case, standard stochastic interpolation tools like the common SABR model often cannot be calibrated to observed implied volatility smiles, due to data being only available for the at-the-money quote of the respective underlying swaption. Here, we propose to infer the geometry of the full unknown implied volatility cube by learning stochastic latent representations of implied volatility cubes via variational autoencoders, enabling inference about the missing volatility data conditional on the observed data by an approximate Gibbs sampling approach. Imputed estimates of missing quotes can afterwards be used to fit a standard stochastic volatility model. Since training data for the employed variational autoencoder model is usually sparsely available, we test the robustness of the approach for a model trained on synthetic data on real market quotes and we show that SABR interpolated volatilites calibrated to reconstructed volatility cubes

Author

Richert, Ivo; Buch, Robert

Published

2022

Language

EN