Regression-Based Complexity Reduction of the Nested Monte Carlo Methods

DOI10.1137/17M114577XzbMath1415.91314arXiv1611.06344MaRDI QIDQ4579837

Denis Belomestny, Stefan Häfner, Mikhail A. Urusov

Publication date: 10 August 2018

Published in: SIAM Journal on Financial Mathematics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1611.06344

zbMATH Keywords

Monte Carlo methodscontrol variatesBermudan optionsregression methodsnested simulations

Mathematics Subject Classification ID

Numerical methods (including Monte Carlo methods) (91G60) Monte Carlo methods (65C05) Stopping times; optimal stopping problems; gambling theory (60G40) Derivative securities (option pricing, hedging, etc.) (91G20)

Related Items

Variance reduction for risk measures with importance sampling in nested simulation, A general continuous time Markov chain approximation for multi-asset option pricing with systems of correlated diffusions, Optimal liquidation through a limit order book: a neural network and simulation approach, Truncated control variates for weak approximation schemes, A New Approach for American Option Pricing: The Dynamic Chebyshev Method

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