Low-rank tensor approximation for Chebyshev interpolation in parametric option pricing

Understand

Treating high dimensionality is one of the main challenges in the development of computational methods for solving problems arising in finance, where tasks such as pricing, calibration, and risk assessment need to be performed accurately and in real-time.

• Among the growing literature addressing this problem, Gass et al.
• [14] propose a complexity reduction technique for parametric option pricing based on Chebyshev interpolation.
• As the number of parameters increases, however, this method is affected by the curse of dimensionality.