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An analysis of a least squares regression method for American option pricing


Recently, various authors proposed Monte-Carlo methods for the computation of American option prices, based on least squares regression. The purpose of this paper is to analyze an algorithm due to Longstaff and Schwartz. This algorithm involves two types of approximation. Approximation one: replace the conditional expectations in the dynamic programming principle by projections on a finite set of functions. Approximation two: use Monte-Carlo simulations and least squares regression to compute the value function of approximation one. Under fairly general conditions, we prove the almost sure convergence of the complete algorithm. We also determine the rate of convergence of approximation two and prove that its normalized error is asymptotically Gaussian.

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Manuscript received: April 2001; final version received: January 2002

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Clément, E., Lamberton, D. & Protter, P. An analysis of a least squares regression method for American option pricing. Finance Stochast 6, 449–471 (2002).

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  • Key words: American options, optimal stopping, Monte-Carlo methods, least squares regression
  • JEL Classification: G10, G12, G13
  • Mathematics Subject Classification (1991): 90A09, 93E20, 60G40