Estimates the distributional parameters for a generalized hyperbolic Student-t distribution.
ghtFit(x, beta = 0.1, delta = 1, mu = 0, nu = 10, scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...)beta is the skewness parameter in the range (0, alpha);
delta is the scale parameter, must be zero or positive;
mu is the location parameter, by default 0.
These are the parameters in the first parameterization.
alpha takes the limit of abs(beta),
and lambda=-nu/2.
TRUE. Should the time series
be scaled by its standard deviation to achieve a more stable
optimization?
span=seq(min, max,
times = n), where, min and max are the
left and right endpoints of the range, and n gives
the number of the intermediate points.
estimate.
Either estimate is an approximate local minimum of the
function or steptol is too small;
4: iteration limit exceeded;
5: maximum step size stepmax exceeded five consecutive times.
Either the function is unbounded below, becomes asymptotic to a
finite value from above in some direction or stepmax
is too small.
The function nlm is used to minimize the "negative"
maximum log-likelihood function. nlm carries out a minimization
using a Newton-type algorithm.
## ghtFit -
# Simulate Random Variates:
set.seed(1953)
## ghtFit -
# Fit Parameters:
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