Combined Derivative Estimators

Paul Glasserman ()
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Paul Glasserman: Columbia Business School

A chapter in Advances in Modeling and Simulation, 2022, pp 193-210 from Springer

Abstract: Abstract We discuss combinations of simulation-based derivative estimators using infinitesimal perturbation analysis (IPA) and the likelihood ratio method (LRM). We first provide a historical perspective on combinations of IPA and LRM and then turn to connections with the generalized likelihood ratio (GLR) method. We re-derive a GLR estimator for barrier options through a combination of IPA and LRM. We then consider the behavior of a GLR estimator for a discrete-time approximation to a diffusion process as the time step shrinks. We show that an average of low-rank GLR estimators has a continuous-time limit, even though each individual estimator blows up. The limit matches an estimator previously derived through Malliavin calculus and also through a combination of IPA and LRM.

Keywords: Sensitivity analysis; Simulation; Likelihood ratio method (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-10193-9_10

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DOI: 10.1007/978-3-031-10193-9_10

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