Accelerating FHS Option Pricing Under Linear GARCH

We propose an analytical approximation technique to accelerate the filtered historical simulation (FHS) option pricing method. The analytical approximation technique has at least two advantages over the FHS method: first, it does not suffer from random sampling error as it needs no simulation; second, it is fast in calculating option price as it is analytical. Simulation results indicate our technique approximates the FHS method quite well, and empirical results show that our technique has very good option pricing performance.