Simulated likelihood estimators for discretely observed jump–diffusions

K. Giesecke and G. Schwenkler

Journal of Econometrics, 2019, vol. 213, issue 2, 297-320

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.

Keywords: Unbiased density estimator; Jump–diffusions; Likelihood inference; Asymptotic efficiency; Computational efficiency (search for similar items in EconPapers)
JEL-codes: C13 C51 C58 C63 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:213:y:2019:i:2:p:297-320

DOI: 10.1016/j.jeconom.2019.01.015

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