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Simulated likelihood estimators for discretely observed jump-diffusions
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DOI：10.1016/j.jeconom.2019.01.015.png)
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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. (C) 2019 Elsevier B.V. All rights reserved.
Keyword:
Unbiased density estimator
Jump-diffusions
Likelihood inference
Asymptotic efficiency
Computational efficiency
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