Fast, Reliable, and Error-Bounded Option Pricing with Pretrained Neural Networks: A GJR--GARCH Study (opens in new tab)

Many models in quantitative finance have no closed-form option prices and rely on slow, noisy Monte Carlo simulation; neural surrogates restore speed but offer no error guarantees. We present a general recipe for surrogates that are fast, with bounded and verifiable error, applicable to any simulation-based density model. A Mixture Density Network maps parameters and maturity to the terminal return density as a Gaussian mixture, so prices, impli...