methods-coef: GARCH Coefficients Methods

Description Methods Note Author(s) Examples

Description

Coefficients methods for GARCH Modelling.

Methods

object = "ANY"

Generic function.

object = "fGARCH"

Extractor function for coefficients from a fitted GARCH model.

object = "fGARCHSPEC"

Extractor function for coefficients from a GARCH specification structure.

Note

coef is a generic function which extracts coefficients from objects returned by modeling functions.

Author(s)

Diethelm Wuertz for the Rmetrics R-port.

Examples

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## garchSpec -
   # Use default parameters beside alpha:
   spec = garchSpec(model = list(alpha = c(0.05, 0.05)))
   spec
   coef(spec)
   
## garchSim -
   # Simulate an univariate "timeSeries" series
   x = garchSim(spec, n = 200)
   x = x[,1]
  
## garchFit - 
   fit = garchFit( ~ garch(1, 1), data = x)
   
## coef - 
   coef(fit)

Example output

Loading required package: timeDate
Loading required package: timeSeries
Loading required package: fBasics


Rmetrics Package fBasics
Analysing Markets and calculating Basic Statistics
Copyright (C) 2005-2014 Rmetrics Association Zurich
Educational Software for Financial Engineering and Computational Science
Rmetrics is free software and comes with ABSOLUTELY NO WARRANTY.
https://www.rmetrics.org --- Mail to: info@rmetrics.org

Formula: 
 ~ garch(2, 1)
Model:
 omega: 1e-06
 alpha: 0.05 0.05
 beta:  0.8
Distribution: 
 norm
Presample: 
  time          z     h y
1   -1 -0.1891135 1e-05 0
2    0  0.9539806 1e-05 0
 omega alpha1 alpha2 gamma1 gamma2   beta     mu     ar     ma  delta 
 1e-06  5e-02  5e-02  0e+00  0e+00  8e-01  0e+00  0e+00  0e+00  2e+00 
attr(,"distribution")
[1] "norm"

Series Initialization:
 ARMA Model:                arma
 Formula Mean:              ~ arma(0, 0)
 GARCH Model:               garch
 Formula Variance:          ~ garch(1, 1)
 ARMA Order:                0 0
 Max ARMA Order:            0
 GARCH Order:               1 1
 Max GARCH Order:           1
 Maximum Order:             1
 Conditional Dist:          norm
 h.start:                   2
 llh.start:                 1
 Length of Series:          200
 Recursion Init:            mci
 Series Scale:              0.00269998

Parameter Initialization:
 Initial Parameters:          $params
 Limits of Transformations:   $U, $V
 Which Parameters are Fixed?  $includes
 Parameter Matrix:
                     U         V    params includes
    mu     -0.70857998   0.70858 -0.070858     TRUE
    omega   0.00000100 100.00000  0.100000     TRUE
    alpha1  0.00000001   1.00000  0.100000     TRUE
    gamma1 -0.99999999   1.00000  0.100000    FALSE
    beta1   0.00000001   1.00000  0.800000     TRUE
    delta   0.00000000   2.00000  2.000000    FALSE
    skew    0.10000000  10.00000  1.000000    FALSE
    shape   1.00000000  10.00000  4.000000    FALSE
 Index List of Parameters to be Optimized:
    mu  omega alpha1  beta1 
     1      2      3      5 
 Persistence:                  0.9 


--- START OF TRACE ---
Selected Algorithm: nlminb 

R coded nlminb Solver: 

  0:     282.38824: -0.0708580 0.100000 0.100000 0.800000
  1:     282.34888: -0.0708678 0.103307 0.100856 0.801437
  2:     282.32332: -0.0708880 0.104692 0.0982103 0.799257
  3:     282.28048: -0.0709352 0.111795 0.0962744 0.798637
  4:     282.19787: -0.0710776 0.120712 0.0886228 0.789806
  5:     282.14785: -0.0713446 0.130534 0.0937502 0.780690
  6:     282.11579: -0.0717825 0.134943 0.0980785 0.768702
  7:     282.08666: -0.0726737 0.140839 0.0929116 0.769373
  8:     282.03083: -0.0778534 0.152405 0.113158 0.741753
  9:     281.98650: -0.0835615 0.157682 0.109197 0.730925
 10:     281.91453: -0.0893262 0.162398 0.111801 0.729407
 11:     281.82330: -0.112436 0.162726 0.115049 0.725113
 12:     281.81378: -0.118987 0.166201 0.111863 0.725878
 13:     281.81375: -0.119188 0.166127 0.112549 0.725384
 14:     281.81375: -0.119141 0.166205 0.112523 0.725336
 15:     281.81375: -0.119142 0.166179 0.112520 0.725368

Final Estimate of the Negative LLH:
 LLH:  -901.0885    norm LLH:  -4.505442 
           mu         omega        alpha1         beta1 
-3.216815e-04  1.211429e-06  1.125203e-01  7.253682e-01 

R-optimhess Difference Approximated Hessian Matrix:
                  mu         omega        alpha1         beta1
mu     -3.040187e+07  1.733435e+09 -1.650372e+04  3.575789e+03
omega   1.733435e+09 -2.767849e+13 -1.568200e+08 -1.933172e+08
alpha1 -1.650372e+04 -1.568200e+08 -1.124447e+03 -1.159680e+03
beta1   3.575789e+03 -1.933172e+08 -1.159680e+03 -1.421235e+03
attr(,"time")
Time difference of 0.009485722 secs

--- END OF TRACE ---


Time to Estimate Parameters:
 Time difference of 0.1381474 secs
           mu         omega        alpha1         beta1 
-3.216815e-04  1.211429e-06  1.125203e-01  7.253682e-01 

fGarch documentation built on May 31, 2017, 4:02 a.m.

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