# A priori error estimates for reduced order models in finance

Ekkehard W. Sachs; Matthias Schu

- Volume: 47, Issue: 2, page 449-469
- ISSN: 0764-583X

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topSachs, Ekkehard W., and Schu, Matthias. "A priori error estimates for reduced order models in finance." ESAIM: Mathematical Modelling and Numerical Analysis - Modélisation Mathématique et Analyse Numérique 47.2 (2013): 449-469. <http://eudml.org/doc/273315>.

@article{Sachs2013,

abstract = {Mathematical models for option pricing often result in partial differential equations. Recent enhancements are models driven by Lévy processes, which lead to a partial differential equation with an additional integral term. In the context of model calibration, these partial integro differential equations need to be solved quite frequently. To reduce the computational cost the implementation of a reduced order model has shown to be very successful numerically. In this paper we give a priori error estimates for the use of the proper orthogonal decomposition technique in the context of option pricing models.},

author = {Sachs, Ekkehard W., Schu, Matthias},

journal = {ESAIM: Mathematical Modelling and Numerical Analysis - Modélisation Mathématique et Analyse Numérique},

keywords = {option pricing models; proper orthogonal decomposition; a priori error estimate; error estimates; integro-partial differential equations; reduced-order model},

language = {eng},

number = {2},

pages = {449-469},

publisher = {EDP-Sciences},

title = {A priori error estimates for reduced order models in finance},

url = {http://eudml.org/doc/273315},

volume = {47},

year = {2013},

}

TY - JOUR

AU - Sachs, Ekkehard W.

AU - Schu, Matthias

TI - A priori error estimates for reduced order models in finance

JO - ESAIM: Mathematical Modelling and Numerical Analysis - Modélisation Mathématique et Analyse Numérique

PY - 2013

PB - EDP-Sciences

VL - 47

IS - 2

SP - 449

EP - 469

AB - Mathematical models for option pricing often result in partial differential equations. Recent enhancements are models driven by Lévy processes, which lead to a partial differential equation with an additional integral term. In the context of model calibration, these partial integro differential equations need to be solved quite frequently. To reduce the computational cost the implementation of a reduced order model has shown to be very successful numerically. In this paper we give a priori error estimates for the use of the proper orthogonal decomposition technique in the context of option pricing models.

LA - eng

KW - option pricing models; proper orthogonal decomposition; a priori error estimate; error estimates; integro-partial differential equations; reduced-order model

UR - http://eudml.org/doc/273315

ER -

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