Importance Sampling for Backward SDEs

DOI10.1080/07362990903546405zbMath1188.65004OpenAlexW2115474447MaRDI QIDQ5305278

Christian Bender, Thilo Moseler

Publication date: 19 March 2010

Published in: Stochastic Analysis and Applications (Search for Journal in Brave)

Full work available at URL: http://nbn-resolving.de/urn:nbn:de:bsz:352-opus-65227

zbMATH Keywords

Monte Carlo simulationvariance reductionnumericsBSDE

Mathematics Subject Classification ID

Numerical methods (including Monte Carlo methods) (91G60) Monte Carlo methods (65C05) Derivative securities (option pricing, hedging, etc.) (91G20) Numerical solutions to stochastic differential and integral equations (65C30)

Related Items (11)

Least-Squares Monte Carlo for Backward SDEs ⋮ Overcoming the timescale barrier in molecular dynamics: Transfer operators, variational principles and machine learning ⋮ Numerical methods for backward stochastic differential equations: a survey ⋮ Stability of backward stochastic differential equations: the general Lipschitz case ⋮ Stochastic optimal control via forward and backward stochastic differential equations and importance sampling ⋮ Adaptive importance sampling in least-squares Monte Carlo algorithms for backward stochastic differential equations ⋮ Linear regression MDP scheme for discrete backward stochastic differential equations under general conditions ⋮ The Forward-Backward Stochastic Heat Equation: Numerical Analysis and Simulation ⋮ Variational approach to rare event simulation using least-squares regression ⋮ A fully backward representation of semilinear PDEs applied to the control of thermostatic loads in power systems ⋮ Solving BSDEs based on novel multi-step schemes and multilevel Monte Carlo

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