This paper is concerned with the Bayesian estimation of non-linear stochastic differential equations when only discrete observations are available. The estimation is carried out using a tuned MCMC method, in particular a blocked Metropolis-Hastings algorithm, by introducing auxiliary points and...

This paper is concerned with the estimation of stochastic differential equations when only discrete observations are available. It primarily focuses on deriving a closed form solution for the one-step ahead conditional transition density using the Milstein scheme. This higher order Taylor...

Simulation estimators, such as indirect inference or simulated maximum likelihood, are successfully employed for estimating stochastic differential equations. They adjust for the bias (inconsistency) caused by discretization of the underlying stochastic process, which is in continuous time. The...

Threshold models have been found useful in modeling nonlinearities in many financial time series. In this framework, the financial variable of interest evolves according to different dynamics, which is solely determined by the threshold regimes that the observed indicator variable falls into....

Threshold models have been found useful in modeling nonlinearities in many financial time series. In this framework, the financial variable of interest evolves according to different dynamics, which is solely determined by the threshold regimes that the observed indicator variable falls into....

This paper estimates stochastic differential equation models for the interest rate dynamics of the United Kingdom bond market using Gaussian estimation econometric methods and monthly data over the period 1970–2010 using a range of maturities. Gaussian estimates of single and two equation...