est.CKLS.KF: ML estimation for the CKLS model (Kalman filter)

In alejandralopezperez/estsde: Parametric Estimation of SDE

ML estimation for the CKLS model (Kalman filter)

Description

Parametric estimation for the CKLS model using the Kalman filter algorithm, where the discretized version of the model is obtained with the Euler-Maruyama method. The parametric form of the CKLS model used here is given by

dX_t = (α - κ X_t)dt + σ X_t^γ dW_t.

Usage

est.CKLS.KF(X, Delta = deltat(X), par = NULL, mu0 = 0, Sigma0 = 1)

Arguments

X	a numeric vector, the sample path of the SDE.
Delta	a single numeric, the time step between two consecutive observations.
par	a numeric vector with dimension four indicating initial values of the parameters. Defaults to NULL, fits a linear model using generalized least squares with AR1 correlation and a power variance heteroscedasticity structure.
mu0	a single numeric, the initial mean. Defaults to zero.
Sigma0	a single numeric, the initial variance. Defaults to one.

Value

A list containing a matrix with the estimated coefficients and the associated standard errors.

Examples

x <- rCKLS(360, 1/12, 0.09, 0.08, 0.9, 1.2, 1.5)
est.CKLS.KF(x)

alejandralopezperez/estsde documentation built on Sept. 4, 2022, 4:48 a.m.