Curved Greeks: A Geometric Layer for Option P&L Adjustments
Pedro Pablo P\'erez Velasco, Mengjue Lu, Daniel Arrieta

TL;DR
This paper introduces a geometric, model-agnostic framework for option P&L adjustments that ensures quadratic risk measures are invariant under different parameterizations, improving hedge accuracy and liquidity management.
Contribution
It proposes a covariant Hessian using an affine connection to make quadratic P&L predictions coordinate-invariant, with practical calibration and application to FX case studies.
Findings
Invariant quadratic P&L predictor demonstrated in FX case studies
Calibration via small linear systems is efficient and well-posed
Framework enables local liquidity-aware sensitivities and rebalancing strategies
Abstract
Short-horizon option book management relies on P&L expansions in a small set of risk factors. In practice, the quadratic term and common desk adjustments (smile corrections, execution cost add-ons) depend on the chosen factor coordinates, so predicted second-order P&L can change when moving between spot, forward, and log-forward parameterizations. We propose a local, model-agnostic framework that makes the quadratic term coordinate invariant. The usual Hessian is replaced by a covariant Hessian defined by an affine connection, yielding an invariant quadratic predictor. The connection is calibrated to match a desk target for quadratic P&L (Vanna-Volga for smile effects or, in principle, a local fit to realized P&L) while leaving first-order hedge Greeks unchanged. Execution frictions enter through a quadratic cost model for hedge trades. Combined with hedge ratios, this induces an…
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Taxonomy
TopicsStochastic processes and financial applications · Financial Markets and Investment Strategies · Risk and Portfolio Optimization
