Malliavin calculus for signatures with applications to finance

📅 2026-04-24
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🤖 AI Summary
This study addresses the challenge of computing sensitivities (Greeks) for complex path-dependent derivatives within Malliavin calculus, where explicit and tractable expressions are typically unavailable. Focusing on random variables given by finite linear combinations of signatures of time-augmented Brownian motion, the paper establishes, for the first time, purely algebraic formulations of the Malliavin derivative, Clark–Ocone representation, Ornstein–Uhlenbeck semigroup and its generator, and integration-by-parts formulas. By synergistically combining Malliavin variational methods, stochastic analysis, and the algebraic structure of path signatures, this approach overcomes traditional computational bottlenecks and enables efficient Greek computation under signature-based volatility models. Numerical experiments further demonstrate the performance differences among various Malliavin weights, validating the efficacy of the proposed framework.

Technology Category

Reasoning under Uncertainty: Stochastic OptimizationMachine Learning: Other Foundations of Machine LearningSearch and Optimization: Sampling/Simulation-based Search

Application Category

Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphsSemantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semanticsEconomics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
📝 Abstract
Malliavin calculus is a powerful and general framework for the analysis of square-integrable random variables, but it often suffers from a lack of tractability and explicit representations. To address this limitation, we focus on a subclass of random variables given by finite linear combinations of time-extended Brownian motion signatures. The class remains rich due to the universal approximation properties of signatures. Leveraging the algebraic structure of signatures, we first derive explicit formulas for the Malliavin derivative of signatures of continuous It\^o processes. As a consequence, we obtain closed-form expressions for the Clark--Ocone representation, the Ornstein--Uhlenbeck semigroup and its generator, as well as the integration-by-parts formula within the class of Brownian signature variables. These results provide purely algebraic formulations of the classical operators of Malliavin calculus. As an application, we compute Greeks for general path-dependent options under signature volatility models, and numerically compare different choices of Malliavin weights.
Problem

Research questions and friction points this paper is trying to address.

Malliavin calculus
signatures
tractability
path-dependent options
Greeks
Innovation

Methods, ideas, or system contributions that make the work stand out.

Malliavin calculus
signatures
Itô processes
Clark–Ocone formula
path-dependent options
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