Quadratic optimal functional quantization of stochastic processes and numerical applications

Abstract

In this paper, we present an overview of the recent developments of functional quantization of stochastic processes, with an emphasis on the quadratic case. Functional quantization is a way to approximate a process, viewed as a Hilbert-valued random variable, using a nearest neighbour projection on a finite codebook. A special emphasis is made on the computational aspects and the numerical applications, in particular the pricing of some path-dependent European options.

41 pages

References in corpus (3)

• Sharp asymptotics of the functional quantization problem for Gaussian processes
• Asymptotically optimal quantization schemes for Gaussian processes
• Convergence of multi-dimensional quantized $SDE$'s