  We propose a parameter-free model for estimating the price or valuation of
financial derivatives like options, forwards and futures using non-supervised
learning networks and Monte Carlo. Although some arbitrage-based pricing
formula performs greatly on derivatives pricing like Black-Scholes on option
pricing, generative model-based Monte Carlo estimation(GAN-MC) will be more
accurate and holds more generalizability when lack of training samples on
derivatives, underlying asset's price dynamics are unknown or the no-arbitrage
conditions can not be solved analytically. We analyze the variance reduction
feature of our model and to validate the potential value of the pricing model,
we collect real world market derivatives data and show that our model
outperforms other arbitrage-based pricing models and non-parametric machine
learning models. For comparison, we estimate the price of derivatives using
Black-Scholes model, ordinary least squares, radial basis function networks,
multilayer perception regression, projection pursuit regression and Monte Carlo
only models.
