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Simulation-based Estimation of Contingent-claims Prices

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  • Peter C. B. Phillips

    (SMU)

  • Jun Yu

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.

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Bibliographic Info

Paper provided by East Asian Bureau of Economic Research in its series Finance Working Papers with number 22473.

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Date of creation: Jan 2008
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Handle: RePEc:eab:financ:22473

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Keywords: Bias Reduction; Bond Pricing; Indirect Inference; Option Pricing; Simulation-based Estimation;

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Citations

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Cited by:
  1. Laurini, Márcio Poletti & Hotta, Luiz Koodi, 2013. "Indirect Inference in fractional short-term interest rate diffusions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 109-126.
  2. Christian Gourieroux & Peter C. B. Phillips & Jun Yu, 2006. "Indirect Inference for Dynamic Panel Models," Cowles Foundation Discussion Papers 1550, Cowles Foundation for Research in Economics, Yale University.
  3. Huang, Shirley J. & Yu, Jun, 2010. "Bayesian analysis of structural credit risk models with microstructure noises," Journal of Economic Dynamics and Control, Elsevier, vol. 34(11), pages 2259-2272, November.
  4. Tore Selland Kleppe & Jun Yu & Hans J. Skaug, 2012. "Simulated Maximum Likelihood Estimation for Latent Diffusion Models," Working Papers 12-2012, Singapore Management University, School of Economics.
  5. Wang, Xiaohu & Phillips, Peter C.B. & Yu, Jun, 2011. "Bias in estimating multivariate and univariate diffusions," Journal of Econometrics, Elsevier, vol. 161(2), pages 228-245, April.
  6. Yu, Jun, 2012. "Bias in the estimation of the mean reversion parameter in continuous time models," Journal of Econometrics, Elsevier, vol. 169(1), pages 114-122.
  7. Xiao, Wei-Lin & Zhang, Wei-Guo & Yao, Zheng & Wang, Xiao-Hui, 2013. "The impact of issuing warrant and debt on behavior of the firm's stock," Economic Modelling, Elsevier, vol. 31(C), pages 635-641.
  8. Mario Cerrato & Chia Chun Lo & Konstantinos Skindilias, 2011. "Adaptive continuous time Markov chain approximation model to general jump-diffusions," Working Papers 2011_16, Business School - Economics, University of Glasgow.

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