Accelerated Option Pricing in Multiple Scenarios

TL;DR

This paper introduces a fast Monte Carlo-based method for pricing financial derivatives across multiple future scenarios by using a smoothing technique to reduce computational effort.

Contribution

It proposes a novel approach that employs representative nested simulations and smoothing methods to accelerate multi-scenario option pricing.

Findings

Significantly reduces computational time for multi-scenario pricing

Maintains accuracy with fewer nested simulations

Applicable in risk management for rapid scenario analysis

Abstract

This paper covers a massive acceleration of Monte-Carlo based pricing method for financial products and financial derivatives. The method is applicable in risk management settings, where a financial product has to be priced under a number of potential future scenarios. Instead of starting a separate nested Monte Carlo simulation for each scenario under consideration, the new method covers the utilization of very few representative nested simulations and estimating the product prices at each scenario by a smoothing method based on the state-space. This smoothing technique can be e.g. non-parametric regression or kernel smoothing.