Semiparametric Pricing of Multivariate Contingent Claims

27 Pages Posted: 7 Nov 2008

Date Written: August 1999

Abstract

This paper develops and implements a methodology for pricing multivariate contingent claims (MVCC s) based on semiparametric estimation of the multivariate risk-neutral density function.This methodology generates MVCC prices which are consistent with current market prices of univariate contingent claims.This method allows for completely general marginal risk-neutral densities and is compatible with all univariate risk-neutral density estimation techniques. The univariate risk-neutral densities are related by their risk-neutral correlation, which is estimated using time-series data on asset returns and an empirical pricing kernel (Rosenberg and Engle, 1999). This permits the multivariate risk-neutral density to be identified without requiring observation of multivariate contingent claims prices. The semiparametric MVCC pricing technique is used for valuation of one-month options on the better of two equity index returns. Option contracts with payoffs dependent on are four equity indexpairs are considered: S&P500 - CAC40, S&P500 - NK225, S&P500 - FTSE100, and S&P500 - DAX30. Five marginal risk-neutral densities (S&P500, CAC40, NK225, FTSE100, and DAX30) are estimated semiparametrically using a cross-section of contemporaneously measured equity index option prices in each market. A bivariate risk-neutral Plackett (1965) density is constructed using the given marginals and risk-neutral correlation derived using an empirical pricing kernel and the historical joint density of the index returns. Price differences from a lognormal pricing formulausing historical and risk-neutral return moments are found to be significant.

Suggested Citation

Rosenberg, Joshua V., Semiparametric Pricing of Multivariate Contingent Claims (August 1999). NYU Working Paper No. FIN-99-028, Available at SSRN: https://ssrn.com/abstract=1297104

Joshua V. Rosenberg (Contact Author)

Independent ( email )

United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
133
Abstract Views
5,105
Rank
396,655
PlumX Metrics