A New Approach for American Option Pricing: The Dynamic Chebyshev Method
Author(s) -
Kathrin Glau,
Mirco Mahlstedt,
Christian Pötz
Publication year - 2019
Publication title -
siam journal on scientific computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.674
H-Index - 147
eISSN - 1095-7197
pISSN - 1064-8275
DOI - 10.1137/18m1193001
Subject(s) - mathematics , valuation of options , monte carlo method , backward induction , mathematical optimization , chebyshev polynomials , chebyshev filter , econometrics , mathematical economics , mathematical analysis , statistics , game theory
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 us to shift the model-dependent computations into an offline phase prior to the time-stepping. In the offline part a family of generalized (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 optionsu0027 delta and gamma. Moreover, the same family of (conditional) moments yield multiple outputs including the option prices for different strikes, maturities, and dif...
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