garchFitControl {fGarch}R Documentation

GARCH Fitting Algorithms and Control

Description

Estimates the parameters of an univariate GARCH process.

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 allowed, defaults to 200.
nlminb.iter.max Maximum number of iterations allowed, 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$trace is positive. Defaults to every 10 iterations.
lbfgsb.lmm is an integer giving the number of BFGS updates retained in the "L-BFGS-B" method, It defaults to 5.
lbfgsb.factr controls the convergence of the "L-BFGS-B" method. Convergence occurs when the reduction in the objective is within this factor of the machine tolerance. Default is 1e7, that is a tolerance of about 1.0e-8.
lbfgsb.pgtol helps control the convergence of the "L-BFGS-B" method. It is a tolerance on the projected gradient in the current search direction. This defaults to zero, when the check is suppressed.
lbfgsb.fnscale defaults to FALSE.
lbfgsb.parscale defaults to FALSE.
nm.ndeps A vector of step sizes for the finite-difference approximation to the gradient, on par/parscale scale. Defaults to 1e-3.
nm.maxit The maximum number of iterations. Defaults to 100 for the derivative-based methods, and 500 for "Nelder-Mead". For "SANN" maxit gives the total number of function evaluations. There is no other stopping criterion. Defaults to 10000.
nm.abstol The absolute convergence tolerance. Only useful for non-negative functions, as a tolerance for reaching zero.
nm.reltol Relative convergence tolerance. The algorithm stops if it is unable to reduce the value by a factor of reltol * (abs(val) + reltol) at a step. Defaults to sqrt(.Machine$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)/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

returns 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.

Examples

  
##
[Package fGarch version 2110.80 Index]