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Maximum Likelihood Estimation of Latent Affine Processes

David S. Bates
The Review of Financial Studies
Vol. 19, No. 3 (Autumn, 2006), pp. 909-965
Stable URL: http://www.jstor.org/stable/3844017
Page Count: 57
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Maximum Likelihood Estimation of Latent Affine Processes
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Abstract

This article develops a direct filtration-based maximum likelihood methodology for estimating the parameters and realizations of latent affine processes. Filtration is conducted in the transform space of characteristic functions, using a version of Bayes' rule for recursively updating the joint characteristic function of latent variables and the data conditional upon past data. An application to daily stock market returns over 1953-1996 reveals substantial divergences from estimates based on the Efficient Methods of Moments (EMM) methodology; in particular, more substantial and time-varying jump risk. The implications for pricing stock index options are examined.

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