On the efficacy of simulated maximum likelihood for estimating the parameters of stochastic differential Equations*

TLDR

The method is feasible when the underlying stochastic differential equation is a Markov process. The study presents a method for estimating parameters of stochastic differential equations using simulated maximum likelihood. The authors employ simulated maximum likelihood to estimate SDE parameters. Abstract.

Abstract

Abstract. A method for estimating the parameters of stochastic differential equations (SDEs) by simulated maximum likelihood is presented. This method is feasible whenever the underlying SDE is a Markov process. Estimates are compared to those generated by indirect inference, discrete and exact maximum likelihood. The technique is illustrated with reference to a one‐factor model of the term structure of interest rates using 3‐month US Treasury Bill data.