Reduced order models for pricing American options under stochastic volatility and Jump-diffusion models

Abstract
American options can be priced by solving linear complementary problems (LCPs) with parabolic partial(-integro) differential operators under stochastic volatility and jump-diffusion models like Heston, Merton, and Bates models. These operators are discretized using finite difference methods leading to a so-called full order model (FOM). Here reduced order models (ROMs) are derived employing proper orthogonal decomposition (POD) and non negative matrix factorization (NNMF) in order to make pricing much faster within a given model parameter variation range. The numerical experiments demonstrate orders of magnitude faster pricing with ROMs.
Main Authors
Format
Conferences Conference paper
Published
2016
Series
Subjects
Publication in research information system
Publisher
Elsevier BV
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-201608173811Use this for linking
Review status
Peer reviewed
ISSN
1877-0509
DOI
https://doi.org/10.1016/j.procs.2016.05.360
Conference
International Conference on Computational Science
Language
English
Published in
Procedia Computer Science
Is part of publication
ICCS 2016 : International Conference on Computational Science 2016, 6-8 June 2016, San Diego, California, USA
Citation
  • Balajewicz, M., & Toivanen, J. (2016). Reduced order models for pricing American options under stochastic volatility and Jump-diffusion models. In ICCS 2016 : International Conference on Computational Science 2016, 6-8 June 2016, San Diego, California, USA (pp. 734-743). Elsevier BV. Procedia Computer Science, 80. https://doi.org/10.1016/j.procs.2016.05.360
License
CC BY-NC-ND 4.0Open Access
Copyright© The Authors. Published by Elsevier B.V. This is an open access article distributed under the terms of a Creative Commons License.

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