# Stacked Monte Carlo for option pricing

## TL;DR

This paper presents a stacking Monte Carlo method for option pricing that learns control variates through function approximation, improving efficiency in evaluating European and Asian options under various volatility models.

## Contribution

It introduces a novel stacking Monte Carlo algorithm for option pricing, adapting machine learning ideas to learn control variates for enhanced accuracy.

## Key findings

- Efficient evaluation of European and Asian Call options.
- Applicable to constant and stochastic volatility models.
- Demonstrates improved Monte Carlo performance.

## Abstract

We introduce a stacking version of the Monte Carlo algorithm in the context of option pricing. Introduced recently for aeronautic computations, this simple technique, in the spirit of current machine learning ideas, learns control variates by approximating Monte Carlo draws with some specified function. We describe the method from first principles and suggest appropriate fits, and show its efficiency to evaluate European and Asian Call options in constant and stochastic volatility models.