Detecting and repairing arbitrage in traded option prices

TL;DR

This paper introduces a linear programming approach to minimally repair arbitrage in traded option prices, improving data quality for financial modeling and calibration.

Contribution

It presents a novel LP-based method for arbitrage repair that minimizes data changes and enhances calibration robustness.

Findings

The method produces sparse data perturbations.

It is computationally efficient for large datasets.

Arbitrage removal improves model calibration accuracy.

Abstract

Option price data are used as inputs for model calibration, risk-neutral density estimation and many other financial applications. The presence of arbitrage in option price data can lead to poor performance or even failure of these tasks, making pre-processing of the data to eliminate arbitrage necessary. Most attention in the relevant literature has been devoted to arbitrage-free smoothing and filtering (i.e. removing) of data. In contrast to smoothing, which typically changes nearly all data, or filtering, which truncates data, we propose to repair data by only necessary and minimal changes. We formulate the data repair as a linear programming (LP) problem, where the no-arbitrage relations are constraints, and the objective is to minimise prices' changes within their bid and ask price bounds. Through empirical studies, we show that the proposed arbitrage repair method gives sparse perturbations on data, and is fast when applied to real world large-scale problems due to the LP formulation. In addition, we show that removing arbitrage from prices data by our repair method can improve model calibration with enhanced robustness and reduced calibration error.