Product Markovian quantization of an R^d -valued Euler scheme of a diffusion process with applications to finance

TL;DR

This paper presents a novel Markovian product quantization method for efficiently discretizing high-dimensional Euler schemes of diffusion processes, with applications in financial option pricing and stochastic differential equations.

Contribution

It introduces a fast online quantization technique based on componentwise product quantization, enabling efficient computation in high dimensions with explicit formulas.

Findings

Efficient quantization with explicit transition probabilities.

Accurate pricing of basket and European options.

Effective approximation of backward stochastic differential equations.

Abstract

We introduce a new approach to quantize the Euler scheme of an $\mathbb{R}^d$-valued diffusion process. This method is based on a Markovian and componentwise product quantization and allows us, from a numerical point of view, to speak of {\em fast online quantization} in dimension greater than one since the product quantization of the Euler scheme of the diffusion process and its companion weights and transition probabilities may be computed quite instantaneously. We show that the resulting quantization process is a Markov chain, then, we compute the associated companion weights and transition probabilities from (semi-) closed formulas. From the analytical point of view, we show that the induced quantization errors at the $k$-th discretization step $t_k$ is a cumulative of the marginal quantization error up to time $t_k$. Numerical experiments are performed for the pricing of a Basket call option, for the pricing of a European call option in a Heston model and for the approximation of the solution of backward stochastic differential equations to show the performances of the method.