# [R-SIG-Finance] Different results on Garch(1, 1) with regressors: Eviews vs rugarch

Eliano Marques eliano.m.marques at gmail.com
Thu Sep 10 12:56:49 CEST 2015

```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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