Journal of Computational Finance

Complexity reduction for calibration to American options

Olena Burkovska, Kathrin Glau, Mirco Mahlstedt and Barbara Wohlmuth

  • We propose and investigate a new method for the calibration to American option price data
  • Method: Complexity reduction by reduced basis, designed to efficiently solve the parametric PDE.
  • Comparative numerical study shows its competitiveness to the existing de-Americanization method.

American put options are among the most frequently traded single stock options, and their calibration is computationally challenging since no closed-form expression is available. Due to their higher flexibility compared with European options, the mathematical model involves additional constraints, and a variational inequality is obtained. We use the Heston stochastic volatility model to describe the price of a single stock option. In order to speed up the calibration process, we apply two model-reduction strategies. First, we introduce a reduced basis method. We thereby reduce the computational complexity of solving the parametric partial differential equation drastically, compared with a classical finite-element method, which makes applications of standard minimization algorithms for the calibration significantly faster.

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