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Functional quantization for pricing derivatives

Gilles Pagès Jacques Printems 1
1 MATHFI - Financial mathematics
Inria Paris-Rocquencourt, ENPC - École des Ponts ParisTech, UPEC UP12 - Université Paris-Est Créteil Val-de-Marne - Paris 12
Abstract : We investigate in this paper the numerical performances of quadratic functional quantization and their applications to Finance. We emphasize the rôle played by the so-called product quantizers and the Karhunen-Loève expansion of Gaussian processes. Numerical experiments are carried out on two classical pricing problems: Asian options in a Black-Scholes model and vanilla options in a stochastic volatility Heston model. Pricing based on ``crude" functional quantization is very fast and produce accurate deterministic results. When combined with a Romberg $\log$-extrapolation, it always outperforms Monte Carlo simulation for usual accuracy levels.
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https://hal.inria.fr/inria-00070611
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Submitted on : Friday, May 19, 2006 - 9:00:49 PM
Last modification on : Friday, February 4, 2022 - 3:07:47 AM
Long-term archiving on: : Sunday, April 4, 2010 - 9:34:36 PM

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  • HAL Id : inria-00070611, version 1

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Gilles Pagès, Jacques Printems. Functional quantization for pricing derivatives. [Research Report] RR-5392, INRIA. 2004, pp.53. ⟨inria-00070611⟩

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