  In financial modeling problems, non-Gaussian tails exist widely in many
circumstances. Among them, the accurate estimation of risk-neutral distribution
(RND) from option prices is of great importance for researchers and
practitioners. A precise RND can provide valuable information regarding the
market's expectations, and can further help empirical asset pricing studies.
This paper presents a parsimonious parametric approach to extract RNDs of
underlying asset returns by using a generative machine learning model. The
model incorporates the asymmetric heavy tails property of returns with a clever
design. To calibrate the model, we design a Monte Carlo algorithm that has good
capability with the assistance of modern machine learning computing tools.
Numerically, the model fits Heston option prices well and captures the main
shapes of implied volatility curves. Empirically, using S\&P 500 index option
prices, we demonstrate that the model outperforms some popular parametric
density methods under mean absolute error. Furthermore, the skewness and
kurtosis of RNDs extracted by our model are consistent with intuitive
expectations. More generally, the proposed methodology is widely applicable in
data fitting and probabilistic forecasting.
