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Abstract
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, options
Key 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.
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Don’t have access? Click here to request a demo
Alternatively, Call a member of the team to discuss membership options
US and Overseas: +1 646-931-9045
UK: 0207 139 1600