A backward Monte Carlo approach to exotic option pricing

TL;DR

This paper introduces a backward Monte Carlo algorithm using a multinomial tree for efficient exotic option pricing, significantly reducing variance by sampling paths backward from the terminal condition.

Contribution

It presents a novel backward sampling algorithm based on a multinomial tree, combining Recursive Marginal Quantization and finite difference methods for variance reduction in exotic option pricing.

Findings

The new method achieves substantial variance reduction compared to traditional Monte Carlo.

The approach is validated against benchmark methods, demonstrating improved efficiency.

Two alternative tree constructions are compared, showing comparable reliability.

Abstract

We propose a novel algorithm which allows to sample paths from an underlying price process in a local volatility model and to achieve a substantial variance reduction when pricing exotic options. The new algorithm relies on the construction of a discrete multinomial tree. The crucial feature of our approach is that -- in a similar spirit to the Brownian Bridge -- each random path runs backward from a terminal fixed point to the initial spot price. We characterize the tree in two alternative ways: in terms of the optimal grids originating from the Recursive Marginal Quantization algorithm and following an approach inspired by the finite difference approximation of the diffusion's infinitesimal generator. We assess the reliability of the new methodology comparing the performance of both approaches and benchmarking them with competitor Monte Carlo methods.