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Maximum Likelihood Estimation of Latent Affine Processes

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  • David S. Bates

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. Copyright 2006, Oxford University Press.

Suggested Citation

  • David S. Bates, 2006. "Maximum Likelihood Estimation of Latent Affine Processes," Review of Financial Studies, Society for Financial Studies, vol. 19(3), pages 909-965.
  • Handle: RePEc:oup:rfinst:v:19:y:2006:i:3:p:909-965
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    File URL: http://hdl.handle.net/10.1093/rfs/hhj022
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