garchFitControl            package:fGarch            R Documentation

_G_A_R_C_H _F_i_t_t_i_n_g _A_l_g_o_r_i_t_h_m_s _a_n_d _C_o_n_t_r_o_l

_D_e_s_c_r_i_p_t_i_o_n:

     Estimates the parameters of an univariate GARCH process.

_U_s_a_g_e:

         
     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)

_A_r_g_u_m_e_n_t_s:

     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.  

_V_a_l_u_e:

     returns a list.

_A_u_t_h_o_r(_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.

_E_x_a_m_p_l_e_s:

       
     ## 

