Convex Regularization of Local Volatility Estimation in a Discrete Setting

TL;DR

This paper develops convex regularization methods for calibrating local volatility surfaces using discrete, noisy market data, achieving convergence rates similar to continuous models and improving calibration accuracy.

Contribution

It introduces convex regularization techniques tailored for discrete and noisy data in local volatility calibration, with proven convergence and practical validation.

Findings

Achieves convergence rates comparable to idealized continuous settings.

Separately accounts for uncertainties from noise and discretization.

Validated with real market data and simulated examples.

Abstract

We apply convex regularization techniques to the problem of calibrating the local volatility surface model of Dupire taking into account the practical requirement of discrete grids and noisy data. Such requirements are the consequence of bid and ask spreads, quantization of the quoted prices and lack of liquidity of option prices for strikes far way from the at the money level. We obtain convergence rates and results comparable to those obtained in the idealized continuous setting. Our results allow us to take into account separately the uncertainties due to the price noise and those due to discretization errors. Thus allowing better discretization levels both in the domain and in the image of the parameter to solution operator. We illustrate the results with simulated as well as real market data. We also validate the results by comparing the implied volatility prices of market data with the computed prices of the calibrated model. {10pt} \noindent {\bf Keywords:} Convex regularization, local volatility surfaces, regularization convergence rates, numerical methods for volatility calibration.