garchFitControl: Control GARCH fitting algorithms
In fGarch: Rmetrics - Autoregressive Conditional Heteroskedastic Modelling

garchFitControl

R Documentation

Control GARCH fitting algorithms

Description

Control parameters for the GARCH fitting algorithms.

Usage

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)

Arguments

`llh`	`llh = c("filter", "internal", "testing")[1]`, defaults to `"filter"`.
`nlminb.eval.max`	maximum number of evaluations of the objective function, defaults to 200.
`nlminb.iter.max`	maximum number of iterations, defaults to 150.
`nlminb.abs.tol`	absolute tolerance, defaults to 1e-20.
`nlminb.rel.tol`	relative tolerance, defaults to 1e-10.
`nlminb.x.tol`	X tolerance, defaults to 1.5e-8.
`nlminb.fscale`	defaults to FALSE.
`nlminb.xscale`	defaulkts to FALSE.
`nlminb.step.min`	minimum step size, defaults to 2.2e-14.
`nlminb.scale`	defaults to 1.
`sqp.mit`	maximum number of iterations, defaults to 200.
`sqp.mfv`	maximum number of function evaluations, defaults to 500.
`sqp.met`	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.
`sqp.mec`	correction for negative curvature: sqp.mec=1 - no correction, sqp.mec=2 - Powell correction (default).
`sqp.mer`	restarts after unsuccessful variable metric updates: sqp.mer=0 - no restarts, sqp.mer=1 - standard restart.
`sqp.mes`	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).
`sqp.xmax`	maximum stepsize, defaults to 1.0e+3.
`sqp.tolx`	tolerance for the change of the coordinate vector, defaults to 1.0e-16.
`sqp.tolc`	tolerance for the constraint violation, defaults to 1.0e-6.
`sqp.tolg`	tolerance for the Lagrangian function gradient, defaults to 1.0e-6.
`sqp.told`	defaults to 1.0e-6.
`sqp.tols`	defaults to 1.0e-4.
`sqp.rpf`	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.
`lbfgsb.REPORT`	the frequency of reports for the `"BFGS"` and `"L-BFGS-B"` methods if `control$trace` is positive. Defaults to every 10 iterations.
`lbfgsb.lmm`	an integer giving the number of BFGS updates retained in the `"L-BFGS-B"` method, It defaults to 5.
`lbfgsb.factr`	controls the convergence of the `"L-BFGS-B"` method. Convergence occurs when the reduction in the objective is within this factor of the machine tolerance. Default is 1e7, that is a tolerance of about 1.0e-8.
`lbfgsb.pgtol`	helps control the convergence of the `"L-BFGS-B"` method. It is a tolerance on the projected gradient in the current search direction. This defaults to zero, when the check is suppressed.
`lbfgsb.fnscale`	defaults to FALSE.
`lbfgsb.parscale`	defaults to FALSE.
`nm.ndeps`	a vector of step sizes for the finite-difference approximation to the gradient, on par/parscale scale. Defaults to 1e-3.
`nm.maxit`	the maximum number of iterations. Defaults to 100 for the derivative-based methods, and 500 for `"Nelder-Mead"`. For `"SANN"` maxit gives the total number of function evaluations. There is no other stopping criterion. Defaults to 10000.
`nm.abstol`	the absolute convergence tolerance. Only useful for non-negative functions, as a tolerance for reaching zero.
`nm.reltol`	relative convergence tolerance. The algorithm stops if it is unable to reduce the value by a factor of `reltol * (abs(val) + reltol)` at a step. Defaults to `sqrt(.Machine$double.eps)`, typically about 1e-8.
`nm.alpha`, `nm.beta`, `nm.gamma`	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).
`nm.fnscale`	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`.
`nm.parscale`	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.

Value

a list

Author(s)

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.