Dear ALL,
Please, I found a package written by you 
and trying to use it for my thesis. However, I realised that the GARCH 
estimated parameters for \alpha is insignificant but \beta is not. I 
have been asked to try or forms of ARCH instead of EGARCH. I need 
assistance  and though you can be of help.Is there any possibility to make \alpha significant.
Below is my code and out put and hope to hearing from you soon.
sincerely yours,
Evelyn


spec3<-ugarchspec(
  variance.model = list(model = "sGARCH",
 garchOrder = c(1, 1), 
    submodel = NULL, external.regressors = NULL), 
  mean.model = list(armaOrder = c(5,0), include.mean = TRUE, 
    garchInMean = FALSE, inMeanType = 1, arfima = FALSE, 
    external.regressors = NULL), 
  distribution.model = "std", 
  start.pars = list(), fixed.pars = list())

#### Estimation
mod13<-ugarchfit(data = as.numeric(next.rtdm), spec = spec3, out.sample = 0, solver = "gosolnp", 
  solver.control = list(), 
  fit.control = list(stationarity = 1, fixed.se = 0, scale = 0))
mod13


Below is my output.*---------------------------------*
*          GARCH Model Fit        *
*---------------------------------*

Conditional Variance Dynamics   
-----------------------------------
GARCH
 Model     : eGARCH(1,1)
Mean Model      : ARFIMA(5,0,0)
Distribution    : std 

Optimal Parameters
------------------------------------
        Estimate  Std. Error  t value Pr(>|t|)
mu      0.000196    0.002418  0.08090 0.935522
ar1    -0.175643    0.036103 -4.86506 0.000001
ar2    -0.076872    0.033146 -2.31916 0.020386
ar3    -0.008402    0.028682 -0.29294 0.769570
ar4     0.009969    0.024042  0.41466 0.678391
ar5     0.031910    0.025496  1.25157 0.210725
omega  -1.039275    0.462871 -2.24528 0.024750
alpha1 -0.123399    0.060082 -2.05383
 0.039992
beta1   0.758208    0.108175  7.00909 0.000000
gamma1  0.419029    0.091741  4.56754 0.000005
shape   3.411291    0.460387  7.40961 0.000000

Robust Standard Errors:
        Estimate  Std. Error   t value Pr(>|t|)
mu      0.000196    0.002173  0.090007 0.928282
ar1    -0.175643    0.043986 -3.993178 0.000065
ar2    -0.076872    0.040139 -1.915126 0.055476
ar3    -0.008402    0.027523 -0.305277 0.760155
ar4     0.009969    0.024197  0.412005 0.680336
ar5     0.031910    0.022837  1.397295 0.162325
omega  -1.039275    0.929039 -1.118656
 0.263287
alpha1 -0.123399    0.100329 -1.229939 0.218720
beta1   0.758208    0.220150  3.444056 0.000573
gamma1  0.419029    0.122063  3.432898 0.000597
shape   3.411291    0.551153  6.189375 0.000000

LogLikelihood : 757.9011 

Information Criteria
------------------------------------
                    
Akaike       -1.7092
Bayes        -1.6491
Shibata      -1.7095
Hannan-Quinn -1.6862

Q-Statistics on Standardized Residuals
------------------------------------
      statistic   p-value
Lag10     18.21 2.694e-03
Lag15     44.09
 3.168e-06
Lag20     47.53 3.032e-05

H0 : No serial correlation

Q-Statistics on Standardized Squared Residuals
------------------------------------
      statistic  p-value
Lag10     16.68 0.005155
Lag15     29.28 0.001124
Lag20     32.97 0.004740

ARCH LM Tests
------------------------------------
             Statistic DoF P-Value
ARCH Lag[2]      3.424   2 0.18046
ARCH Lag[5]     11.627   5 0.04028
ARCH Lag[10]    14.979  10 0.13283

Nyblom stability test
------------------------------------
Joint Statistic:  4.1947
Individual Statistics:              
mu     0.07072
ar1    0.09443
ar2    0.08445
ar3    0.21113
ar4    0.08776
ar5    0.03898
omega  1.13050
alpha1 0.55509
beta1  1.30548
gamma1 0.32253
shape  0.49571

Asymptotic Critical Values (10% 5% 1%)
Joint Statistic:         2.49 2.75 3.27
Individual Statistic:    0.35 0.47 0.75

Sign Bias Test
------------------------------------
                   t-value   prob sig
Sign Bias           0.4858 0.6272    
Negative Sign Bias  0.2361 0.8134    
Positive Sign Bias  0.3620 0.7175    
Joint
 Effect        0.7142 0.8699    


Adjusted Pearson Goodness-of-Fit Test:
------------------------------------
  group statistic p-value(g-1)
1    20     18.45       0.4927
2    30     32.45       0.3003
3    40     43.44       0.2879
4    50     56.21       0.2231


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