Maximum Likelihood Estimation of Latent Affine Processes

David S. Bates

This article develops a direct filtration-based maximum likelihood methodology for estimating the parameters and realizations of latent affine processes. The equivalent of Bayes' rule is derived for recursively updating the joint characteristic function of latent variables and the data conditional upon past data. Likelihood functions can consequently be evaluated directly by Fourier inversion. An application to daily stock returns over 1953-96 reveals substantial divergences from EMM-based estimates: in particular, more substantial and time-varying jump risk

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Year of publication:	May 2003
Authors:	Bates, David S.
Institutions:	National Bureau of Economic Research (contributor)
Publisher:	Cambridge, Mass : National Bureau of Economic Research
Subject:	Maximum-Likelihood-Schätzung \| Maximum likelihood estimation \| Theorie \| Theory \| Kapitaleinkommen \| Capital income \| Aktienmarkt \| Stock market

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