Variance reduction for discretised diffusions via regression

DOI10.1016/j.jmaa.2017.09.002zbMath1373.93303arXiv1510.03141OpenAlexW2565620316MaRDI QIDQ1674395

Tigran Nagapetyan, Stefan Häfner, Denis Belomestny, Mikhail A. Urusov

Publication date: 2 November 2017

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

Full work available at URL: https://arxiv.org/abs/1510.03141

zbMATH Keywords

stochastic differential equationsMonte Carlo methodscontrol variatesregression methodsweak schemes

Mathematics Subject Classification ID

Monte Carlo methods (65C05) Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) Stochastic systems in control theory (general) (93E03)

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Higher-order deep solver of non-linear PDEs implied by a non-linear discrete Clark-Ocone formula, Regression-Based Complexity Reduction of the Nested Monte Carlo Methods, Unbiased Deep Solvers for Linear Parametric PDEs, Approximation of Stochastic Volterra Equations with kernels of completely monotone type, Truncated control variates for weak approximation schemes, Variance reduction in Monte Carlo estimators via empirical variance minimization, Higher-order error estimates of the discrete-time Clark-Ocone formula, Variance reduction for additive functionals of Markov chains via martingale representations

Cites Work

• Unnamed Item
• On the complexity of computing quadrature formulas for marginal distributions of SDEs
• Evaluation of conditional Wiener integrals by numerical integration of stochastic differential equations
• A new higher-order weak approximation scheme for stochastic differential equations and the Runge-Kutta method
• A distribution-free theory of nonparametric regression
• An operator approach for Markov chain weak approximations with an application to infinite activity Lévy driven SDEs
• Cubature on Wiener space
• Multilevel Monte Carlo Path Simulation
• Weak Approximation of Stochastic Differential Equations and Application to Derivative Pricing
• Practical Variance Reduction via Regression for Simulating Diffusions
• A Method of Second-Order Accuracy Integration of Stochastic Differential Equations
• Variance Reduction for Simulated Diffusions
• Discrete chaotic calculus and covariance identities
• Approximation of expectation of diffusion processes based on Lie algebra and Malliavin calculus