A Derivative-Free Milstein Type Approximation Method for SPDEs covering the Non-Commutative Noise case
Claudine von Hallern, Andreas R\"o{\ss}ler

TL;DR
This paper introduces a derivative-free Milstein scheme for SPDEs with non-commutative noise, achieving higher effective convergence rates and reducing computational costs compared to traditional methods.
Contribution
The authors develop a new derivative-free Milstein scheme for SPDEs that do not require commutative noise, improving efficiency and convergence over existing methods.
Findings
The scheme attains the same order as the original Milstein scheme.
It outperforms the original Milstein scheme when considering computational cost.
Numerical simulations confirm the theoretical convergence improvements.
Abstract
Higher order schemes for stochastic partial differential equations that do not possess commutative noise require the simulation of iterated stochastic integrals. In this work, we propose a derivative-free Milstein type scheme to approximate the mild solution of stochastic partial differential equations that need not to fulfill a commutativity condition for the noise term and which can flexibly be combined with some approximation method for the involved iterated integrals. Recently, the authors introduced two algorithms to simulate such iterated stochastic integrals; these clear the way for the implementation of the proposed higher order scheme. We prove the mean-square convergence of the introduced derivative-free Milstein type scheme which attains the same order as the original Milstein scheme. The original scheme, however, is definitely outperformed when the computational cost is…
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Taxonomy
TopicsStochastic processes and financial applications · Financial Risk and Volatility Modeling · Probability and Risk Models
