Develops basis-expansion reductions for stochastic hedge ratios with residual-minimization and projected-moment (Galerkin/Petrov-Galerkin) coefficient criteria to accelerate pathwise sensitivity-to-hedge conversion in Monte Carlo engines.
Adjoints and Automatic (Algorithmic) Differentiation in Computational Finance
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Two of the most important areas in computational finance: Greeks and, respectively, calibration, are based on efficient and accurate computation of a large number of sensitivities. This paper gives an overview of adjoint and automatic differentiation (AD), also known as algorithmic differentiation, techniques to calculate these sensitivities. When compared to finite difference approximation, this approach can potentially reduce the computational cost by several orders of magnitude, with sensitivities accurate up to machine precision. Examples and a literature survey are also provided.
fields
q-fin.RM 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Faster Forward Sensitivities: Reduced stochastic hedge ratios from pathwise algorithmic differentiation
Develops basis-expansion reductions for stochastic hedge ratios with residual-minimization and projected-moment (Galerkin/Petrov-Galerkin) coefficient criteria to accelerate pathwise sensitivity-to-hedge conversion in Monte Carlo engines.