Simulated Likelihood Estimators for Discretely Observed Jump-Diffusions
48 Pages Posted: 3 Nov 2014 Last revised: 29 Oct 2019
Date Written: July 31, 2018
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: Density estimator, parameter estimator, exact simulation, simulated likelihood
JEL Classification: C01, C13, C15, C51, C63
Suggested Citation: Suggested Citation