A computationally efficient state-space partitioning approach to pricing high-dimensional American options via dimension reduction

DOI10.1016/j.ejor.2013.05.035zbMath1317.91068OpenAlexW1997190995MaRDI QIDQ2356102

Xun Li, Xing Jin, Zhenyu Wu, Hwee Huat Tan

Publication date: 28 July 2015

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2013.05.035

zbMATH Keywords

dimension reductionstochastic dynamic programminghigh dimensional American-style option

Mathematics Subject Classification ID

Applications of statistics to actuarial sciences and financial mathematics (62P05) Stochastic programming (90C15) Dynamic programming (90C39) Stopping times; optimal stopping problems; gambling theory (60G40) Financial applications of other theories (91G80) Derivative securities (option pricing, hedging, etc.) (91G20) Software, source code, etc. for problems pertaining to game theory, economics, and finance (91-04)

Related Items (8)

Pricing exotic derivatives exploiting structure ⋮ The implication of missing the optimal-exercise time of an American option ⋮ Spitzer identity, Wiener-Hopf factorization and pricing of discretely monitored exotic options ⋮ An auto-realignment method in quasi-Monte Carlo for pricing financial derivatives with jump structures ⋮ An operator splitting method for multi-asset options with the Feynman-Kac formula ⋮ American step options ⋮ VIX derivatives, hedging and vol-of-vol risk ⋮ Generic improvements to least squares Monte Carlo methods with applications to optimal stopping problems

Cites Work

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