PT  - JOURNAL ARTICLE
AU  - Qi Hu
AU  - David Newton
TI  - Universal Arbitrage-Free Estimation of State Price Density
AID  - 10.3905/jod.2020.1.123
DP  - 2021 Feb 28
TA  - The Journal of Derivatives
PG  - 35--59
VI  - 28
IP  - 3
4099  - https://pm-research.com/content/28/3/35.short
4100  - https://pm-research.com/content/28/3/35.full
AB  - 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.