A Wiener Chaos Approach to Martingale Modelling and Implied Volatility Calibration
Pere Diaz-Lozano, Thomas K. Kloster
TL;DR
The paper introduces a Wiener chaos martingale model that represents the discounted asset price under a risk-neutral measure via a truncated Wiener chaos expansion, yielding a universal and arbitrage-free framework. Calibration to the implied-volatility surface is achieved by learning the chaos coefficients, with efficient pricing via Monte Carlo or quadrature methods and variance-reduction techniques. The model demonstrates strong fit to Heston and rough Heston surfaces and shows robust out-of-sample pricing for path-dependent options, as well as good performance on real SPX data. The approach provides a flexible, time-consistent alternative to parametric models, capable of capturing a wide range of dynamics while enabling scalable calibration and hedging in practice.
Abstract
Calibration to a surface of option prices requires specifying a suitably flexible martingale model for the discounted asset price under a risk-neutral measure. Assuming Brownian noise and mean-square integrability, we construct an over-parameterized model based on the martingale representation theorem. In particular, we approximate the terminal value of the martingale via a truncated Wiener--chaos expansion and recover the intermediate dynamics by computing the corresponding conditional expectations. Using the Hermite-polynomial formulation of the Wiener chaos, we obtain easily implementable expressions that enable fast calibration to a target implied-volatility surface. We illustrate the flexibility and expressive power of the resulting model through numerical experiments on both simulated and real market data.
