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Quadratic Optimal Functional Quantization of Stochastic Processes and Numerical Applications

In: Monte Carlo and Quasi-Monte Carlo Methods 2006

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  • Gilles Pagès

    (Université Paris 6, Laboratoire de Probabilités et Modèles aléatoires, UMR 7599)

Abstract

Summary 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.

Suggested Citation

  • Gilles Pagès, 2008. "Quadratic Optimal Functional Quantization of Stochastic Processes and Numerical Applications," Springer Books, in: Alexander Keller & Stefan Heinrich & Harald Niederreiter (ed.), Monte Carlo and Quasi-Monte Carlo Methods 2006, pages 101-142, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-74496-2_6
    DOI: 10.1007/978-3-540-74496-2_6
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