TY - JOUR T1 - Universal Arbitrage-Free Estimation of State Price Density JF - The Journal of Derivatives SP - 35 LP - 59 DO - 10.3905/jod.2020.1.123 VL - 28 IS - 3 AU - Qi Hu AU - David Newton Y1 - 2021/02/28 UR - https://pm-research.com/content/28/3/35.abstract N2 - Given the valuable information content of Arrow–Debreu prices, the recovery of a well-behaved state price density is of considerable importance. However, this is a nontrivial task because of data limitation and the complex arbitrage-free constraints. In this article, the authors develop a more effective linear programming support vector machine estimator for state price density, which incorporates no-arbitrage restrictions and bid–ask spread. This method does not depend on a particular approximation function and framework and is, therefore, universally applicable. In a parallel empirical study, they apply the method to options on the S&P 500, showing it to be accurate and smooth.TOPICS: Derivatives, optionsKey Findings▪ Recovery of a well-behaved state price density is an important but nontrivial task because of data limitation and the complex arbitrage-free constraints.▪ The authors develop a universally applicable linear programming support vector machine estimator for state price density that incorporates no-arbitrage restrictions and bid–ask spread.▪ They apply the method empirically to options on the S&P 500, showing it to be accurate and smooth. ER -