Simulation-based Estimation of Contingent-claims Prices

Peter Phillips and Jun Yu

Finance Working Papers from East Asian Bureau of Economic Research

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.

Keywords: Bias Reduction; Bond Pricing; Indirect Inference; Option Pricing; Simulation-based Estimation (search for similar items in EconPapers)
JEL-codes: C15 G12 (search for similar items in EconPapers)
Date: 2008-01
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Simulation-Based Estimation of Contingent-Claims Prices (2009)
Working Paper: Simulation-based Estimation of Contingent-claims Prices (2007)
Working Paper: Simulation-based Estimation of Contingent Claims Prices
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