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Found 5 papers, 3 papers with code
Quasi-Monte Carlo for Efficient Fourier Pricing of Multi-Asset Options
Nonetheless, the applicability of RQMC on the unbounded domain, $\mathbb{R}^d$, requires a domain transformation to $[0, 1]^d$, which may result in singularities of the transformed integrand at the corners of the hypercube, and deteriorate the rate of convergence of RQMC.
Optimal Damping with Hierarchical Adaptive Quadrature for Efficient Fourier Pricing of Multi-Asset Options in Lévy Models
First, we smooth the Fourier integrand via an optimized choice of the damping parameters based on a proposed optimization rule.
Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing
When approximating the expectations of a functional of a solution to a stochastic differential equation, the numerical performance of deterministic quadrature methods, such as sparse grid quadrature and quasi-Monte Carlo (QMC) methods, may critically depend on the regularity of the integrand.
Multilevel Monte Carlo with Numerical Smoothing for Robust and Efficient Computation of Probabilities and Densities
This study is motivated by the computation of probabilities of events, pricing options with a discontinuous payoff, and density estimation problems for dynamics where the discretization of the underlying stochastic processes is necessary.
Importance sampling for a robust and efficient multilevel Monte Carlo estimator for stochastic reaction networks
The multilevel Monte Carlo (MLMC) method for continuous time Markov chains, first introduced by Anderson and Higham (2012), is a highly efficient simulation technique that can be used to estimate various statistical quantities for stochastic reaction networks (SRNs), and in particular for stochastic biological systems.
Numerical Analysis Numerical Analysis Computation 60H35, 60J27, 60J75, 92C40
