Fast, Reliable, and Error-Bounded Option Pricing with Pretrained Neural Networks: A GJR--GARCH Study

Abstract

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, implied volatilities, and Greeks follow in closed form as an arbitrage-free mixture of lognormals, with a CDF-matching loss aligned to pricing error. A distribution-free Monte Carlo noise floor, $\sqrt{1/(6N)}$, quantifies the best accuracy achievable at a given simulation budget and decomposes the out-of-sample error into four controllable terms. We demonstrate the method on GJR--GARCH, where the surrogate reaches an out-of-sample CDF error of $1.4\times 10^{-4}$, within $10\%$ of the noise floor, and prices each option in a few microseconds on a single CPU core, or under a microsecond on a GPU.

31 pages, 18 figures