Time discretization and quantization methods for optimal multiple switching problem

TL;DR

This paper develops probabilistic numerical methods using optimal quantization for solving optimal multiple switching problems, analyzing convergence rates and proposing space discretization schemes with numerical validation.

Contribution

It introduces quantization-based numerical schemes for optimal switching problems, providing convergence analysis and extending methods for uncontrolled state processes.

Findings

Error rate of order (h log(1/h))^{1/2} for discrete approximation

Error rate of order h^{1/2} when switching costs are state-independent

Numerical tests demonstrate the effectiveness of the proposed methods

Abstract

In this paper, we study probabilistic numerical methods based on optimal quantization algorithms for computing the solution to optimal multiple switching problems with regime-dependent state process. We first consider a discrete-time approximation of the optimal switching problem, and analyze its rate of convergence. Given a time step $h$, the error is in general of order $(h \log(1/h))^{1/2}$, and of order $h^{1/2}$ when the switching costs do not depend on the state process. We next propose quantization numerical schemes for the space discretization of the discrete-time Euler state process. A Markovian quantization approach relying on the optimal quantization of the normal distribution arising in the Euler scheme is analyzed. In the particular case of uncontrolled state process, we describe an alternative marginal quantization method, which extends the recursive algorithm for optimal stopping problems as in Bally-Pag\`es (2003). A priori $L^p$-error estimates are stated in terms of quantization errors. Finally, some numerical tests are performed for an optimal switching problem with two regimes.