Publication Type

Working Paper

Version

publishedVersion

Publication Date

12-2006

Abstract

A new methodology is proposed to estimate theortical prices of financial contingent-claims whose values are dependent on some other underlying financial assets. In the literature the preferred choice of estimator is usually maximum likelihood (ML). ML has strong asymptotic justification but is not necessarily the best method in finite samples. The present paper proposes instead a simulation-based method that improves the finite sample performance of the ML estimator while maintaining its good asymptotic properties. The methods are implemented and evaluated here in the Black-Scholes option pricing model and in the Vasicek bond pricing model, but have wider applicability. Monte Carlo studies show that the proposed procedures achieve bias reductions overML estimation in pricing contingent claims. The bias reductions are sometimes accompanied by reductions in variance, leading to significant overall gains in mean squared estimation error. Empirical applications to US treasure bills highlight the differences between the bond prices implied by the simulation-based approach and those delivered by ML and the consequences on the statistical tesing of contingent-claim pricing models.

Discipline

Econometrics | Finance

Research Areas

Econometrics

First Page

1

Last Page

30

Publisher

SMU Economics and Statistics Working Paper Series, No. 28-2006

City or Country

Singapore

Copyright Owner and License

Authors

Comments

Published in Review of Financial Studies, 2009. https://doi.org/10.1093/rfs/hhp009

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