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Functional quantization for pricing derivatives
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.
https://hal.inria.fr/inria-00070611
Contributor : Rapport de Recherche Inria <>
Submitted on : Friday, May 19, 2006 - 9:00:49 PM
Last modification on : Tuesday, December 17, 2019 - 2:14:55 AM
Long-term archiving on: : Sunday, April 4, 2010 - 9:34:36 PM
Contributor : Rapport de Recherche Inria <>
Submitted on : Friday, May 19, 2006 - 9:00:49 PM
Last modification on : Tuesday, December 17, 2019 - 2:14:55 AM
Long-term archiving on: : Sunday, April 4, 2010 - 9:34:36 PM