Simulated nonparametric estimation of continuous time models of asset prices and returns

Filippo Altissimo and Antonio Mele

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

Keywords: nonparametric estimation; continuous time asset pricing; continuum of moments; simulations (search for similar items in EconPapers)
JEL-codes: C14 C15 C32 G12 (search for similar items in EconPapers)
Pages: 61 pages
Date: 2004-01
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http://eprints.lse.ac.uk/24674/ Open access version. (application/pdf)

Related works:
Working Paper: Simulated Nonparametric Estimation of Continuous Time Models of Asset Prices and Returns (2004)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:24674

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