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Simulated Maximum Likelihood Estimation for Latent Diffusion Models

Author

Listed:
  • Tore Selland Kleppe

    (University of Bergen)

  • Jun Yu

    (Sim Kee Boon Institute for Financial Economics, School of Economics and Lee Kong Chian School of Business)

  • Hans J. Skaug

    (University of Bergen)

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.

Suggested Citation

  • Tore Selland Kleppe & Jun Yu & Hans J. Skaug, 2012. "Simulated Maximum Likelihood Estimation for Latent Diffusion Models," Working Papers 12-2012, Singapore Management University, School of Economics.
    • Handle: RePEc:siu:wpaper:12-2012
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    References listed on IDEAS

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    More about this item

    Keywords

    Closed-form approximation; Diffusion Model; Ecient importance sampler;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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