Publication Type

Journal Article

Version

submittedVersion

Publication Date

9-2009

Abstract

A new methodology is proposed to estimate theoretical 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. This paper proposes a simulation-based method. When it is used in connection with ML, it can improve the finite-sample performance of the ML estimator while maintaining its good asymptotic properties. The method is implemented and evaluated here in the Black-Scholes option pricing model and in the Vasicek bond and bond option pricing model. It is especially favored when the bias in ML is large due to strong persistence in the data or strong nonlinearity in pricing functions. Monte Carlo studies show that the proposed procedures achieve bias reductions over ML estimation in pricing contingent claims when ML is biased. The bias reductions are sometimes accompanied by reductions in variance. Empirical applications to U.S. Treasury bills highlight the differences between the bond prices implied by the simulation-based approach and those delivered by ML. Some consequences for the statistical testing of contingent-claim pricing models are discussed.

Keywords

Bias Reduction, Bond Pricing, Indirect Inference, Option Pricing, Simulation-based Estimation

Discipline

Econometrics | Finance

Research Areas

Econometrics

Publication

Review of Financial Studies

Volume

22

Issue

9

First Page

3669

Last Page

3705

ISSN

0893-9454

Identifier

10.1093/rfs/hhp009

Publisher

Oxford University Press

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1093/rfs/hhp009

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