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A new approach for American option pricing: The Dynamic Chebyshev method

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  • Kathrin Glau
  • Mirco Mahlstedt
  • Christian Potz

Abstract

We introduce a new method to price American options based on Chebyshev interpolation. In each step of a dynamic programming time-stepping we approximate the value function with Chebyshev polynomials. The key advantage of this approach is that it allows to shift the model-dependent computations into an offline phase prior to the time-stepping. In the offline part a family of generalised (conditional) moments is computed by an appropriate numerical technique such as a Monte Carlo, PDE or Fourier transform based method. Thanks to this methodological flexibility the approach applies to a large variety of models. Online, the backward induction is solved on a discrete Chebyshev grid, and no (conditional) expectations need to be computed. For each time step the method delivers a closed form approximation of the price function along with the options' delta and gamma. Moreover, the same family of (conditional) moments yield multiple outputs including the option prices for different strikes, maturities and different payoff profiles. We provide a theoretical error analysis and find conditions that imply explicit error bounds for a variety of stock price models. Numerical experiments confirm the fast convergence of prices and sensitivities. An empirical investigation of accuracy and runtime also shows an efficiency gain compared with the least-square Monte-Carlo method introduced by Longstaff and Schwartz (2001).

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  • Kathrin Glau & Mirco Mahlstedt & Christian Potz, 2018. "A new approach for American option pricing: The Dynamic Chebyshev method," Papers 1806.05579, arXiv.org.
    • Handle: RePEc:arx:papers:1806.05579
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    References listed on IDEAS

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    Cited by:

    1. Reilly Pickard & Finn Wredenhagen & Julio DeJesus & Mario Schlener & Yuri Lawryshyn, 2024. "Hedging American Put Options with Deep Reinforcement Learning," Papers 2405.06774, arXiv.org.
    2. Reilly Pickard & F. Wredenhagen & Y. Lawryshyn, 2024. "Optimizing Deep Reinforcement Learning for American Put Option Hedging," Papers 2405.08602, arXiv.org.

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