Efficiency improvements for pricing American options with a stochastic mesh

Abstract

We develop and study general-purpose techniques for improving the efficiency of the stochastic mesh method that was recently developed for pricing American options via Monte Carlo simulation.First, we develop a mesh-based, biased-low estimator.By recursively averaging the low and high estimators at each stage, we obtain a significantly more accurate point estimator at each of the mesh points.Second, we adapt the importance sampling ideas for simulation of European path-dependent options in Glasserman, Heidelberger, and Shahabuddin (1998a) to pricing of American options with a stochastic mesh.Third, we sketch generalizations of the mesh method and we discuss links with other techniques for valuing American options.Our empirical results show that the bias-reduced point estimates are much more accurate than the standard mesh-method point estimates.Importance sampling is found to increase accuracy for a smooth option-payoff functions, while variance increases are possible for non-smooth payoffs.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 39 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.