The Geometry of Risk: Path-Dependent Regulation and Anticipatory Hedging via the SigSwap

TL;DR

This paper presents a novel geometry-based framework for managing complex, path-dependent financial risks using the SigSwap instrument, which decomposes risks into transparent factors and enhances regulatory compliance and real-time risk management.

Contribution

It introduces the SigSwap and Signature Expected Shortfall as innovative tools for risk decomposition and measurement, bridging physical stress-testing with risk-neutral hedging.

Findings

Enables decomposition of complex risks into linear, transparent factors.

Provides a real-time risk monitoring system with anticipatory learning.

Reduces computational complexity for high-frequency risk reporting.

Abstract

This paper introduces a transformative framework for managing path-dependent financial risk by shifting from traditional distribution-centric models to a geometry-based approach. We propose the SigSwap as a new regulatory instrument that allows market participants to decompose complex risk into terminal price law and the underlying texture of the price path. By utilising the mathematical properties of the path-signature, we demonstrate how previously unmodellable risks, such as lead-lag dynamics and flash-crash spiralling, can be converted into transparent and linear risk factors. Central to this framework is the introduction of Signature Expected Shortfall, a risk metric designed to capture toxic path geometries that traditional methods often overlook. We also present a proactive monitoring system based on the Temporal Exposure Profile, which utilises anticipatory learning to detect potential liquidity traps and geometric decoupling before they manifest as realised volatility. The proposed methodology offers a rigorous alignment with global regulatory mandates, specifically the Fundamental Review of the Trading Book (FRTB), by providing a consistent bridge between physical stress-testing and risk-neutral hedging. Finally, we show that this algebraic approach significantly reduces computational complexity, enabling real-time, high-frequency risk reporting and capital optimisation for the modern financial ecosystem.