Simulation-based Estimation of Contingent-claims Prices
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. 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 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.
Bias Reduction Bond Pricing Indirect Inference Option Pricing Simulation-based Estimation
Peter C. B. Phillips Jun Yu
Cowles Foundation for Research in Economics Yale University University of Auckland & University of Y Singapore Management University
国际会议
大连
英文
1-32
2008-07-02（万方平台首次上网日期，不代表论文的发表时间）