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Simulated Nonparametric Estimation of Continuous Time Models of Asset Prices and Returns

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Author Info
Antonio Mele ()
Filippo Altissimo

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Abstract

 This paper introduces a new parameter estimator of dynamic models in which the state is a multidimensional, continuous-time, partially observed Markov process. The estimator minimizes appropriate distances between nonparametric joint (and/or conditional) densities of sample data and nonparametric joint (and/or conditional) densities estimated from data simulated out of the model of interest. Sample data and model-simulated data are smoothed with the same kernel. This makes the estimator: 1) consistent independently of the amount of smoothing; and 2) asymptotically root-T normal when the smoothing parameter goes to zero at a reasonably mild rate. When the underlying state is observable, the estimator displays the same asymptotic efficiency properties as the maximum-likelihood estimator. In the partially observed case, we derive conditions under which efficient estimators can be implemented with the help of auxiliary prediction functions suggested by standard asset pricing theories. The method is flexible, fast to implement and possesses finite sample properties that are well approximated by the asymptotic theory. 

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Paper provided by Financial Markets Group in its series FMG Discussion Papers with number dp476.

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Date of creation: Jan 2004
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Handle: RePEc:fmg:fmgdps:dp476

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  1. Valentina Corradi & Norman Swanson & Geetesh Bhardwaj, 2006. "A Simulation Based Specification Test for Diffusion Processes," Departmental Working Papers 200614, Rutgers University, Department of Economics. [Downloadable!]
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This page was last updated on 2009-11-16.

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