A weak approximation for Bismut’s formula: An algorithmic differentiation method
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DOI: 10.1016/j.matcom.2023.09.003
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- Syoiti Ninomiya & Nicolas Victoir, 2008. "Weak Approximation of Stochastic Differential Equations and Application to Derivative Pricing," Applied Mathematical Finance, Taylor & Francis Journals, vol. 15(2), pages 107-121.
- Mariko Ninomiya & Syoiti Ninomiya, 2009. "A new higher-order weak approximation scheme for stochastic differential equations and the Runge–Kutta method," Finance and Stochastics, Springer, vol. 13(3), pages 415-443, September.
- Martin I. Reiman & Alan Weiss, 1989. "Sensitivity Analysis for Simulations via Likelihood Ratios," Operations Research, INFORMS, vol. 37(5), pages 830-844, October.
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Keywords
Algorithmic differentiation; Stochastic differential equation; Weak approximation; Bismut formula; Gaussian kusuoka-approximation; Enlarged semigroup;All these keywords.
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