WIAS Preprint No. 2706, (2020)

Low-dimensional approximations of high-dimensional asset price models


  • Redmann, Martin
    ORCID: 0000-0001-5182-9773
  • Bayer, Christian
    ORCID: 0000-0002-9116-0039
  • Goya, Pawan

2010 Mathematics Subject Classification

  • 91G20 91G60 93A15 60H10 65D32


  • Model order reduction, Black Scholes model, Heston model, option pricing




We consider high-dimensional asset price models that are reduced in their dimension in order to reduce the complexity of the problem or the effect of the curse of dimensionality in the context of option pricing. We apply model order reduction (MOR) to obtain a reduced system. MOR has been previously studied for asymptotically stable controlled stochastic systems with zero initial conditions. However, stochastic differential equations modeling price processes are uncontrolled, have non-zero initial states and are often unstable. Therefore, we extend MOR schemes and combine ideas of techniques known for deterministic systems. This leads to a method providing a good pathwise approximation. After explaining the reduction procedure, the error of the approximation is analyzed and the performance of the algorithm is shown conducting several numerical experiments. Within the numerics section, the benefit of the algorithm in the context of option pricing is pointed out.

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