🤖 AI Summary
This paper addresses the challenge of constructing arbitrage-free option price surfaces from noisy market quotes. We propose a robust modeling framework grounded in Chebyshev tensor bases and Hamiltonian energy-driven dynamics. Our key contribution is the introduction of a “risk-neutral density fog”—a locally adjustable deviation variable—regularized via a Hamiltonian-type energy functional to enable controlled local corrections while enforcing global no-arbitrage constraints. The method integrates spectral regularization, transport-inspired regularization, and static no-arbitrage constraints into a jointly convex optimization problem, efficiently solved using the OSQP solver. Empirical evaluation demonstrates that the approach achieves 98–99% bid-ask spread coverage under stable market conditions, with static arbitrage violation rates below 1%. Crucially, it preserves surface smoothness and economic plausibility even under market stress, substantially enhancing model robustness and practical applicability.
📝 Abstract
We study the construction of arbitrage-free option price surfaces from noisy bid-ask quotes across strike and maturity. Our starting point is a Chebyshev representation of the call price surface on a warped log-moneyness/maturity rectangle, together with linear sampling and no-arbitrage operators acting on a collocation grid. Static no-arbitrage requirements are enforced as linear inequalities, while the surface is fitted directly to prices via a coverage-seeking quadratic objective that trades off squared band misfit against spectral and transport-inspired regularisation of the Chebyshev coefficients. This yields a strictly convex quadratic program in the modal coefficients, solvable at practical scales with off-the-shelf solvers (OSQP).
On top of the global backbone, we introduce a local post-fit layer based on a discrete fog of risk-neutral densities on a three-dimensional lattice (m,t,u) and an associated Hamiltonian-type energy. On each patch of the (m,t) plane, the fog variables are coupled to a nodal price field obtained from the baseline surface, yielding a joint convex optimisation problem that reweights noisy quotes and applies noise-aware local corrections while preserving global static no-arbitrage and locality.
The method is designed such that for equity options panels, the combined procedure achieves high inside-spread coverage in stable regimes (in calm years, 98-99% of quotes are priced inside the bid-ask intervals) and low rates of static no-arbitrage violations (below 1%). In stressed periods, the fog layer provides a mechanism for controlled leakage outside the band: when local quotes are mutually inconsistent or unusually noisy, the optimiser allocates fog mass outside the bid-ask tube and justifies small out-of-band deviations of the post-fit surface, while preserving a globally arbitrage-free and well-regularised description of the option surface.