Probability density models based on deep networks have achieved remarkable success in modeling complex high-dimensional datasets.
Ranked #1 on Density Estimation on UCI POWER
Atypically to standard NN applications, financial industry practitioners use such models equally to replicate market prices and to value other financial instruments.
We introduce the vine copula autoencoder (VCAE), a flexible generative model for high-dimensional distributions built in a straightforward three-step procedure.
We also include two sample tests to assess statistical similarity between the observed and simulated distributions, and we analyze the privacy tradeoffs with respect to membership inference and location-sequence attacks.
Causal inference using observational data is challenging, especially in the bivariate case.