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 non-trivial task due to data limitation and the complex arbitrage-free constraints. In this paper, we develop a more e˙ective linear programming support vector machine (SVM) 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, we apply the method to options on the S&P 500, showing it to be accurate and smooth.
| Original language | English |
|---|---|
| Pages (from-to) | 35-59 |
| Number of pages | 25 |
| Journal | Journal of Derivatives |
| Volume | 28 |
| Issue number | 3 |
| Early online date | 21 Nov 2020 |
| DOIs | |
| Publication status | Published - 1 Mar 2021 |
Keywords
- State price density, Non-parametric estimation, No-arbitrage constraints, Support vector machine regression
Fingerprint
Dive into the research topics of 'Universal Arbitrage-free Estimation of State Price Density'. Together they form a unique fingerprint.Profiles
-
David Newton
- Management - Head of Division
- Accounting, Finance & Law
- Centre for Governance, Regulation and Industrial Strategy
Person: Research & Teaching