est.CKLS.HP: ML estimation for the CKLS model (Hermite polynomial...

In alejandralopezperez/estsde: Parametric Estimation of SDE

ML estimation for the CKLS model (Hermite polynomial expansion)

Description

Parametric estimation for the CKLS model using maximum likelihood and the discretized version of the model, obtained with the Ait-Sahalia Hermite polynomial expansion 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.HP(X, Delta = deltat(X), par = NULL)

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.

Value

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

References

Ait-Sahalia, Y. (2002). Maximum likelihood estimation of discretely sampled diffusions: A closed-form approximation approach. Econometrica, 70(1):223–262.

Examples

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

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