Estimates the parameters of an univariate GARCH process.
garchFitControl(
llh = c("filter", "internal", "testing"),
nlminb.eval.max = 2000,
nlminb.iter.max = 1500,
nlminb.abs.tol = 1.0e-20,
nlminb.rel.tol = 1.0e-14,
nlminb.x.tol = 1.0e-14,
nlminb.step.min = 2.2e-14,
nlminb.scale = 1,
nlminb.fscale = FALSE,
nlminb.xscale = FALSE,
sqp.mit = 200,
sqp.mfv = 500,
sqp.met = 2,
sqp.mec = 2,
sqp.mer = 1,
sqp.mes = 4,
sqp.xmax = 1.0e3,
sqp.tolx = 1.0e-16,
sqp.tolc = 1.0e-6,
sqp.tolg = 1.0e-6,
sqp.told = 1.0e-6,
sqp.tols = 1.0e-4,
sqp.rpf = 1.0e-4,
lbfgsb.REPORT = 10,
lbfgsb.lmm = 20,
lbfgsb.pgtol = 1e-14,
lbfgsb.factr = 1,
lbfgsb.fnscale = FALSE,
lbfgsb.parscale = FALSE,
nm.ndeps = 1e-14,
nm.maxit = 10000,
nm.abstol = 1e-14,
nm.reltol = 1e-14,
nm.alpha = 1.0,
nm.beta = 0.5,
nm.gamma = 2.0,
nm.fnscale = FALSE,
nm.parscale = FALSE)
returns a list.
llh = c("filter", "internal", "testing")[1], defaults to "filter".
Maximum number of evaluations of the objective function allowed, defaults to 200.
Maximum number of iterations allowed, defaults to 150.
Absolute tolerance, defaults to 1e-20.
Relative tolerance, defaults to 1e-10.
X tolerance, defaults to 1.5e-8.
defaults to FALSE.
defaulkts to FALSE.
Minimum step size, defaults to 2.2e-14.
defaults to 1.
maximum number of iterations, defaults to 200.
maximum number of function evaluations, defaults to 500.
specifies scaling strategy:
sqp.met=1 - no scaling
sqp.met=2 - preliminary scaling in 1st iteration (default)
sqp.met=3 - controlled scaling
sqp.met=4 - interval scaling
sqp.met=5 - permanent scaling in all iterations
correction for negative curvature:
sqp.mec=1 - no correction
sqp.mec=2 - Powell correction (default)
restarts after unsuccessful variable metric updates:
sqp.mer=0 - no restarts
sqp.mer=1 - standard restart
interpolation method selection in a line search:
sqp.mes=1 - bisection
sqp.mes=2 - two point quadratic interpolation
sqp.mes=3 - three point quadratic interpolation
sqp.mes=4 - three point cubic interpolation (default)
maximum stepsize, defaults to 1.0e+3.
tolerance for the change of the coordinate vector, defaults to 1.0e-16.
tolerance for the constraint violation, defaults to 1.0e-6.
tolerance for the Lagrangian function gradient, defaults to 1.0e-6.
defaults to 1.0e-6.
defaults to 1.0e-4.
value of the penalty coefficient, default to1.0D-4. The default velue may be relatively small. Therefore, larger value, say one, can sometimes be more suitable.
The frequency of reports for the "BFGS" and "L-BFGS-B" methods if control$trace is positive. Defaults to every 10 iterations.
is an integer giving the number of BFGS updates retained in the "L-BFGS-B" method, It defaults to 5.
controls the convergence of the "L-BFGS-B" method. Convergence occurs when the reduction in the objective is within this factor of the machine tolerance. Default is 1e7, that is a tolerance of about 1.0e-8.
helps control the convergence of the "L-BFGS-B" method. It is a tolerance on the projected gradient in the current search direction. This defaults to zero, when the check is suppressed.
defaults to FALSE.
defaults to FALSE.
A vector of step sizes for the finite-difference approximation to the gradient, on par/parscale scale. Defaults to 1e-3.
The maximum number of iterations. Defaults to 100 for the derivative-based methods, and 500 for "Nelder-Mead". For "SANN" maxit gives the total number of function evaluations. There is no other stopping criterion. Defaults to 10000.
The absolute convergence tolerance. Only useful for non-negative functions, as a tolerance for reaching zero.
Relative convergence tolerance. The algorithm stops if it is
unable to reduce the value by a factor of
reltol * (abs(val) + reltol) at a step. Defaults to
sqrt(.Machine$double.eps), typically about 1e-8.
Scaling parameters for the "Nelder-Mead" method. alpha is the reflection factor (default 1.0), beta the contraction factor (0.5), and gamma the expansion factor (2.0).
An overall scaling to be applied to the value of fn and gr
during optimization. If negative, turns the problem into a
maximization problem. Optimization is performed on
fn(par) / nm.fnscale.
A vector of scaling values for the parameters. Optimization is performed on par/parscale and these should be comparable in the sense that a unit change in any element produces about a unit change in the scaled value.
Diethelm Wuertz for the Rmetrics R-port,
R Core Team for the 'optim' R-port,
Douglas Bates and Deepayan Sarkar for the 'nlminb' R-port,
Bell-Labs for the underlying PORT Library,
Ladislav Luksan for the underlying Fortran SQP Routine,
Zhu, Byrd, Lu-Chen and Nocedal for the underlying L-BFGS-B Routine.