I am estimating a GARCH(1,1) model and it has an additional variance regressor. I want to obtain the likelihood ratio value when there are no ARCH or GARCH terms - so I only have the implied volatility appearing in the variance regressor.
This is my first attempt at programming ever and any guidance/pointers are much appreciated. I have not completely understood everything yet and am still reading. I have been using the samples provided as well.
NOTE: I am NOT asking someone to write the code for me - just practical help.
I am using Eviews 7.
Code: Select all
' GARCH-M with constant and implied volatility in the conditional variance equation
' change path to program path
%path = @runpath
cd %path
' load workfile
load new thesis approach
series y = logsp
' set sample (1/02/1990-12/31/1996)
sample s0 1/02/1990 12/31/1996
' get starting values from Gaussian GARCH-M
equation eq1
eq1.arch(0,0,garch,archm=var, logvix) y c
show eq1.output
' declare and initialize parameters
coef(1) lambda0 = eq1.c(1)
coef(1) lambda1 = eq1.c(2)
coef(1) mu = eq1.c(3)
' set presample values of expressions in logl
smpl s0
series sig2 = omega(1)
series res = 0
' set up GARCH likelihood
logl ll1
ll1.append @logl logl
ll1.append res = y-mu(1)
ll1.append sig2 = omega(1)+alpha(1)*res(-1)^2 +beta(1)*sig2(-1)
ll1.append z = res^2/sig2/(tdf(1)-2) + 1
ll1.append logl = @gammalog((tdf(1) + 1)/2) - @gammalog(tdf(1)/2) - log(!pi)/2 - log(tdf(1) - 2)/2 - log(sig2)/2 - (tdf(1)+1)*log(z)/2
' estimate and display output
smpl s1
ll1.ml(showopts, m=1000, c=1e-5)