Arbitrage-Free Multi-Maturity Risk-Neutral Marginals

2026-07-07

AI Summary

Existing methods struggle to construct risk-neutral marginal distributions from arbitrage-free option prices that simultaneously satisfy no butterfly arbitrage, no calendar spread arbitrage, exact market price recovery, efficient sampling, and full support. This work proposes an explicit construction method that exactly fits observed option prices within the range of quoted strikes via piecewise probability mass allocation, while extrapolating beyond this range using closed-form power-law tails that satisfy necessary boundary conditions. The approach uniquely achieves, within a unified framework, strict absence of static arbitrage, exact calibration to market prices, analytical expressions for both density and quantile functions, and efficient Monte Carlo sampling. Experiments on synthetic SSVI surfaces and S&P 500 market data demonstrate its computational efficiency, robustness, and practical utility, effectively bridging the gap between option pricing models and downstream applications.

Abstract

Many quantitative finance methods and applications are formulated in terms of option-implied risk-neutral marginals rather than directly in terms of option prices. Representative examples include martingale optimal transport, Bass local-volatility calibration, scenario analysis, and option-implied tail-risk measurement. The desired risk-neutral marginals should define a genuine probability law on the entire support, reproduce the input arbitrage-free option prices exactly, be free of butterfly and calendar arbitrage, and admit efficient evaluation of the density, distribution function, and quantiles, as well as Monte Carlo sampling. Existing methods typically optimize only a subset of these properties, depending on their intended purpose. This leaves a gap between upstream arbitrage-free option prices and the readily usable risk-neutral marginals required by downstream applications. We propose an explicit construction of risk-neutral marginals from discrete arbitrage-free option prices. On the observed strike range, probability mass is assigned interval by interval to exactly reproduce the input option prices. Outside the observed range, closed-form power-law tails complete the distribution by satisfying price and slope boundary conditions and allocating the remaining probability mass. Butterfly- and calendar-arbitrage-freeness are guaranteed by construction. The construction is feasible by design and computationally efficient. The resulting marginal laws admit closed-form densities, distribution functions, quantiles, and efficient Monte Carlo sampling. Numerical experiments on synthetic SSVI data and S\&P~500 market data demonstrate that the proposed construction efficiently and robustly produces marginals satisfying all of these properties in practice.

Problem

• risk-neutral marginals
• arbitrage-free
• option prices
• butterfly arbitrage
• calendar arbitrage

Innovation

• arbitrage-free
• risk-neutral marginals
• power-law tails
• Monte Carlo sampling
• option pricing