Different results on Garch(1, 1) with regressors: Eviews vs rugarch
Discussed numerous times on this forum (i.e. differences between different software implementations). As far as I can see from your output, eviews uses: Presample variance: backcast (parameter = 0.7) whereas rugarch by default uses the whole sample for the initialization. See the ugarchspec help function on variance.targeting which allows a numeric value (instead of logical) between 0 and 1 for the backcasting. However, it could also be the case of different bound constraints. In the rugarch model, the coefficients on the external regressors in the variance equation for the sGARCH model are constrained to be positive. Feel free to play around with 'setbounds<-' and a whole host of other options, including using an alternate solver etc. Alexios
On 10/09/2015 13:56, Eliano Marques wrote:
Hi everyone,
I?m writing a thesis around the stock prices with ISEG in Lisbon.
I wrote the entire end-to-end ETL in R and I?m trying to run all the models in R. Just as a sense check, I was comparing the results between Eviews and R and realised big differences between then and I wonder if you can help me debugging this differences, i?m sure I might be doing something wrong.
Here is my R code:
RFunction_garch_estimation=function( #stock,
variance.model = list(model = "sGARCH", garchOrder = c(1, 1),
submodel = NULL, external.regressors = NULL, variance.targeting = FALSE),
mean.model = list(armaOrder = c(1, 1), include.mean = TRUE, archm = FALSE,
archpow = 1, arfima = FALSE, external.regressors = NULL, archex = FALSE),
distribution.model = "norm", start.pars = list(), fixed.pars = list(),
spec, data, out.sample = 0, solver = "solnp", solver.control = list(),
fit.control = list(stationarity = 1, fixed.se = 0, scale = 0, rec.init = 'all'),
numderiv.control = list(grad.eps=1e-4, grad.d=0.0001,
grad.zero.tol=sqrt(.Machine$double.eps/7e-7), hess.eps=1e-4, hess.d=0.1,
hess.zero.tol=sqrt(.Machine$double.eps/7e-7), r=4, v=2)
) {
library(rugarch)
mod1=ugarchspec(variance.model = variance.model,
mean.model = mean.model,
distribution.model = distribution.model)
mod1fit=ugarchfit(mod1, data,solver=solver, fit.control, out.sample, solver.control , numderiv.control )
return(mod1fit) }
#This RFunction just sets the ugarchspec and estimates at the same the garch function.
variance_model = list(model = "sGARCH", garchOrder = c(1, 1),
submodel = NULL, external.regressors = as.matrix(regressors), variance.targeting = FALSE)
mean_model = list(armaOrder = c(0,0 ), include.mean = TRUE, archm = FALSE,
archpow = 1, arfima = FALSE, external.regressors = as.matrix(regressors), archex = FALSE)
distribution_model = "norm"
#as.matrix(regressors)
model1=RFunction_garch_estimation( data=target, variance.model = variance_model, mean.model = mean_model,distribution.model = distribution_model,solver='solnp')
show(model1)
#### Results:
Robust Standard Errors:
Estimate Std. Error t value Pr(>|t|)
mu 0.000015 0.234264 0.000063 0.99995
mxreg1 0.709299 292.613915 0.002424 0.99807
mxreg2 -0.000112 0.098905 -0.001135 0.99909
mxreg3 0.000034 0.065088 0.000528 0.99958
mxreg4 -0.000003 0.075987 -0.000037 0.99997
mxreg5 -0.000009 0.012701 -0.000723 0.99942
omega 0.000000 0.000249 0.000175 0.99986
alpha1 0.020642 1.493492 0.013821 0.98897
beta1 0.973943 0.908957 1.071496 0.28395
vxreg1 0.000000 0.030732 0.000000 1.00000
vxreg2 0.000000 0.000019 0.000469 0.99963
vxreg3 0.000000 0.001606 0.000007 0.99999
vxreg4 0.000000 0.000630 0.000015 0.99999
vxreg5 0.000000 0.000634 0.000000 1.00000
LogLikelihood : 30151.719
Eviews outputs:
Dependent Variable: target
Method: ML - ARCH (Marquardt) - Normal distribution
Date: 09/10/15 Time: 11:51
Sample: 11/05/2014 09:30 8/28/2015 17:30
Included observations: 6763
Convergence achieved after 25 iterations
Bollerslev-Wooldridge robust standard errors & covariance
Presample variance: backcast (parameter = 0.7)
GARCH = C(7) + C(8)*RESID(-1)^2 + C(9)*GARCH(-1) + C(10)
*reg1 + C(11)*reg2 + C(12)
*reg3 + C(13)*reg4 + C(14)
*reg5
Variable Coefficient Std. Error z-Statistic Prob.
C -1.62E-05 4.17E-05 -0.388964 0.6973
reg1 0.723305 0.050098 14.43789 0.0000
reg2 -0.000242 0.000123 -1.972702 0.0485
reg3 0.000170 8.29E-05 2.049855 0.0404
reg4 0.000107 0.000175 0.610040 0.5418
reg5 -1.22E-05 8.26E-06 -1.482648 0.1382
Variance Equation
C 9.87E-06 3.85E-06 2.566464 0.0103
RESID(-1)^2 0.149994 0.035467 4.229165 0.0000
GARCH(-1) 0.599977 0.118194 5.076196 0.0000
reg1 -0.000362 0.002233 -0.162239 0.8711
reg2 1.35E-06 1.18E-05 0.114108 0.9092
reg3 -5.72E-07 5.44E-07 -1.050865 0.2933
reg4 -2.28E-06 9.78E-06 -0.232631 0.8160
reg5 -8.48E-08 2.61E-08 -3.251462 0.0011
Now please note that the majority of the external regressors have 0 as coefficient in the conditional variance and this isn't much different from Eviews. However when you look at the coefficients alpha and beta they significantly differ from Eviews. In addition, both methods using the robust matrix of cov-var, the p-value of a large number of coefs. differ.
Could you help me understand if I?m doing anything wrong in the R bit?
Thank you,
Eliano
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