MONTE CARLO APPROXIMATIONS OF AMERICAN OPTIONS THAT PRESERVE MONOTONICITY AND CONVEXITY

Pierre del Moral 1, 2, 3 Bruno Rémillard 4 Sylvain Rubenthaler 5
1 ALEA - Advanced Learning Evolutionary Algorithms
Inria Bordeaux - Sud-Ouest, UB - Université de Bordeaux, CNRS - Centre National de la Recherche Scientifique : UMR5251
4 Groupe d'études et de recherche en analyse des décisions (GERAD)
GERAD - Groupe d'études et de recherche en analyse des décisions (GERAD)
Abstract : It can be shown that when the payoff function is convex and decreasing (re- spectively increasing) with respect to the underlying (multidimensional) assets, then the same is true for the value of the associated American option, provided some conditions are satisfied. In such a case, all Monte Carlo methods proposed so far in the literature do not preserve the convexity or monotonicity properties. In this paper, we propose a method of approximation for American options which can preserve both convexity and monotonicity. The resulting values can then be used to define exercise times and can also be used in combination with primal-dual methods to get sharper bounds. Other application of the algorithm include finding optimal hedging strategies.
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Pierre del Moral, Bruno Rémillard, Sylvain Rubenthaler. MONTE CARLO APPROXIMATIONS OF AMERICAN OPTIONS THAT PRESERVE MONOTONICITY AND CONVEXITY. Numerical Methods in Finance, Bordeaux June 2011, Jun 2011, Bordeaux, France. pp.115-143, ⟨10.1007/978-3-642-25746-9_4⟩. ⟨hal-00755423⟩

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