Reduced order models for pricing American options under stochastic volatility and Jump-diffusion models
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). Procedia Computer Science, 80. Elsevier BV. doi:10.1016/j.procs.2016.05.360
Published inProcedia Computer Science;80
© The Authors. Published by Elsevier B.V. This is an open access article distributed under the terms of a Creative Commons License.
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.