Simulated Maximum Likelihood Estimation for Latent Diffusion Models

Tore Kleppe (), Jun Yu and Hans J. Skaug Author Email:
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Hans J. Skaug Author Email:: University of Bergen

Working Papers from Singapore Management University, Sim Kee Boon Institute for Financial Economics

Abstract: In this paper a method is developed and implemented to provide the simulated maximum likelihood estimation of latent diffusions based on discrete data. The method is applicable to diffusions that either have latent elements in the state vector or are only observed at discrete time with a noise. Latent diffusions are very important in practical applications in financial economics. The proposed approach synthesizes the closed form method of Aït-Sahalia (2008) and the efficient importance sampler of Richard and Zhang (2007). It does not require any infill observations to be introduced and hence is computationally tractable. The Monte Carlo study shows that the method works well in finite sample. The empirical applications illustrate usefulness of the method and find no evidence of infinite variance in the importance sampler.

Keywords: Closed-form approximation; Diffusion Model; Efficient importance sampler (search for similar items in EconPapers)
JEL-codes: C11 C15 G12 (search for similar items in EconPapers)
Pages: 29 Pages
Date: 2011-07
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Published in SMU-SKBI CoFie Working Paper

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Related works:
Working Paper: Simulated Maximum Likelihood Estimation for Latent Diffusion Models (2012)
Working Paper: Simulated Maximum Likelihood Estimation for Latent Diffusion Models (2011)
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