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Simulated likelihood estimators for discretely observed jump–diffusions

Author

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  • Giesecke, K.
  • Schwenkler, G.

Abstract

This paper develops an unbiased Monte Carlo approximation to the transition density of a jump–diffusion process with state-dependent drift, volatility, jump intensity, and jump magnitude. The approximation is used to construct a likelihood estimator of the parameters of a jump–diffusion observed at fixed time intervals that need not be short. The estimator is asymptotically unbiased for any sample size. It has the same large-sample asymptotic properties as the true but uncomputable likelihood estimator. Numerical results illustrate its properties.

Suggested Citation

  • Giesecke, K. & Schwenkler, G., 2019. "Simulated likelihood estimators for discretely observed jump–diffusions," Journal of Econometrics, Elsevier, vol. 213(2), pages 297-320.
    • Handle: RePEc:eee:econom:v:213:y:2019:i:2:p:297-320
    DOI: 10.1016/j.jeconom.2019.01.015
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    References listed on IDEAS

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    Cited by:

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    More about this item

    Keywords

    Unbiased density estimator; Jump–diffusions; Likelihood inference; Asymptotic efficiency; Computational efficiency;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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