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American Monte Carlo in high dimension

Zhen Wei (Stanford University)


This talk is aimed at resolving a big challenge for the American Monte Carlo method in pricing/hedging products with complex payoffs and high-dimensionality, where the traditional LSMC fails to maintain the pricing profile. We introduce a two-step regression procedure, which automatically selects the most relevant information in the high-dimensional space and produces reasonable pricing profiles for high dimensional products. Furthermore, unlike LSMC, the two-step method performs better after expanding the information space (whereas LSMC performs worse) and is flexible in specifying the basis functions. We successfully apply the proposed method to high dimensional equity basket products like Asian Rainbow options and Basket Average options with knockouts.

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