Maximum Likelihood Estimation of Latent Affine Processes

David S. Bates

No 9673, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: 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.

JEL-codes: C1 (search for similar items in EconPapers)
Date: 2003-05
New Economics Papers: this item is included in nep-ecm and nep-ets
Note: AP
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Citations: View citations in EconPapers (5)

Published as Bates, David S. "Maximum Likelihood Estimation Of Latent Affine Processes," Review of Financial Studies, 2006, v19(3,Fall), 909-965.

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