Convenient Multiple Directions of Stratification

TL;DR

This paper explores efficient non-orthogonal stratification directions for Monte Carlo simulations of path-dependent options, reducing computational costs while maintaining variance reduction effectiveness.

Contribution

It introduces new algorithms for generating convenient non-orthogonal stratification directions and compares their performance to existing methods and Latin Hypercube Sampling.

Findings

Non-orthogonal directions can achieve similar variance reduction as orthogonal ones.

The new algorithms reduce computational costs significantly.

Effective stratification improves pricing accuracy for complex options.

Abstract

This paper investigates the use of multiple directions of stratification as a variance reduction technique for Monte Carlo simulations of path-dependent options driven by Gaussian vectors. The precision of the method depends on the choice of the directions of stratification and the allocation rule within each strata. Several choices have been proposed but, even if they provide variance reduction, their implementation is computationally intensive and not applicable to realistic payoffs, in particular not to Asian options with barrier. Moreover, all these previously published methods employ orthogonal directions for multiple stratification. In this work we investigate the use of algorithms producing convenient directions, generally non-orthogonal, combining a lower computational cost with a comparable variance reduction. In addition, we study the accuracy of optimal allocation in terms of variance reduction compared to the Latin Hypercube Sampling. We consider the directions obtained by the Linear Transformation and the Principal Component Analysis. We introduce a new procedure based on the Linear Approximation of the explained variance of the payoff using the law of total variance. In addition, we exhibit a novel algorithm that permits to correctly generate normal vectors stratified along non-orthogonal directions. Finally, we illustrate the efficiency of these algorithms in the computation of the price of different path-dependent options with and without barriers in the Black-Scholes and in the Cox-Ingersoll-Ross markets.