Forest of Stochastic Trees: A Method for Valuing Multiple Exercise Options

R. Mark Reesor and T. James Marshall
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R. Mark Reesor: Department of Mathematics, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada
T. James Marshall: Bank of Montreal, Toronto, ON M5X 1A1, Canada

JRFM, 2020, vol. 13, issue 5, 1-31

Abstract: We present the Forest of Stochastic Trees (FOST) method for pricing multiple exercise options by simulation. The proposed method uses stochastic trees in place of binomial trees in the Forest of Trees algorithm originally proposed to value swing options, hence extending that method to allow for a multi-dimensional underlying process. The FOST can also be viewed as extending the stochastic tree method for valuing (single exercise) American-style options to multiple exercise options. The proposed valuation method results in positively- and negatively-biased estimators for the true option value. We prove the sign of the estimator bias and show that these estimators are consistent for the true option value. This method is of particular use in cases where there is potentially a large number of assets underlying the contract and/or the underlying price process depends on multiple risk factors. Numerical results are presented to illustrate the method.

Keywords: Monte Carlo; multiple exercise options; dynamic programming; stochastic optimal control (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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