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

Maciej Balajewicz, Jari Toivanen

Research output: Contribution to journalConference article

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.

Original languageEnglish (US)
Pages (from-to)734-743
Number of pages10
JournalProcedia Computer Science
Volume80
DOIs
StatePublished - Jan 1 2016
EventInternational Conference on Computational Science, ICCS 2016 - San Diego, United States
Duration: Jun 6 2016Jun 8 2016

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Costs
Factorization
Finite difference method
Mathematical operators
Decomposition
Experiments

Keywords

  • American option
  • Linear complementary problem
  • Option pricing
  • Reduced order model

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Reduced order models for pricing American options under stochastic volatility and Jump-diffusion models. / Balajewicz, Maciej; Toivanen, Jari.

In: Procedia Computer Science, Vol. 80, 01.01.2016, p. 734-743.

Research output: Contribution to journalConference article

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