Simulation-based Estimation of Contingent Claims Prices

Peter Phillips and Jun Yu

Working Papers from Singapore Management University, Sim Kee Boon Institute for Financial Economics

Abstract: A new methodology is proposed to estimate theoretical prices of financial contin- gent 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 imple- mented 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 bi- ased. The bias reductions are sometimes accompanied by reductions in variance. Empirical applications to U.S. Treasury bills highlight the di?erences 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 and Bond Option Pricing; Indirect Inference; Option Pricing; Simulation-based Estimation. (search for similar items in EconPapers)
JEL-codes: C11 C15 G12 (search for similar items in EconPapers)
Pages: 62 Pages
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Published in SMU-SKBI CoFie Working Paper

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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 (2008)
Working Paper: Simulation-based Estimation of Contingent-claims Prices (2007)
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