Estimates the distrinbutional parameters for a generalized hyperbolic distribution.
ghFit(x, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2, scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...)
alpha
, beta
, delta
,
mu
, and and lambda
:
shape parameter alpha
;
skewness parameter beta
, abs(beta)
is in the
range (0, alpha);
scale parameter delta
, delta
must be zero or
positive;
location parameter mu
, by default 0;
and lambda parameter lambda
, by default -1/2.
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.
## ghFit -
# Simulate Random Variates:
set.seed(1953)
s = rgh(n = 1000, alpha = 1.5, beta = 0.3, delta = 0.5, mu = -1.0)
## ghFit -
# Fit Parameters:
ghFit(s, alpha = 1, beta = 0, delta = 1, mu = mean(s), doplot = TRUE)
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