Bv_Egarch code conversion.
Posted: Wed Sep 05, 2012 2:34 pm
Hello.
i was searching for the Bivarite Egarch codes for simple time Series Data with 2 variables in this forum but i found the code OF "EGARCH OF Panel DATA" its brilliant work done . i tried my level best to understand it fully and reduce it to the Bi-variate Egarch Version but every time i got new error. can any one please Edit it so that i can estimate volatility spillover b/w Stock and foreign Exchange.
my variables are 1Stock indices 2Exchange Rate
THE AUTHOR used 17 countries in the panel and i guess two Variables
The code is given below
{%X} is used for different countries
i will be obliged for this help
i was searching for the Bivarite Egarch codes for simple time Series Data with 2 variables in this forum but i found the code OF "EGARCH OF Panel DATA" its brilliant work done . i tried my level best to understand it fully and reduce it to the Bi-variate Egarch Version but every time i got new error. can any one please Edit it so that i can estimate volatility spillover b/w Stock and foreign Exchange.
my variables are 1Stock indices 2Exchange Rate
THE AUTHOR used 17 countries in the panel and i guess two Variables
The code is given below
{%X} is used for different countries
i will be obliged for this help
Code: Select all
'change path to program path
%path=@runpath
cd %path
'load workfile containing the return series
load unstacked.wf1
smpl @all
%x ="us"
!p = 4
!q = 2
'Estimate the conditional mean using VAR
var {%x}_var.ls 1 !p e_{%x} s_{%x} @
show {%x}_var.output
'Generate residual series form var
{%x}_var.makeresids() {%x}_e {%x}_s
' set sample
' first observation of s1 need to be one or two periods after the first observation of s0
sample s0 2/5/1999 8/26/2011
sample s1 2/19/1999 8/26/2011
' initialization of parameters and starting values
smpl s0
'get starting values from univariate GARCH
equation {%x}_egarch1.ARCH(1,!q,EGARCH,H,BACKCAST=0.7,DERIV=AA) {%x}_e
equation {%x}_egarch2.ARCH(1,!q,EGARCH,H,BACKCAST=0.7,DERIV=AA) {%x}_s
' declare coef vectors to use in bi-variate Panel EGARCH model
coef(2) {%x}_alpha0
{%x}_alpha0(1)= {%x}_var.c(1,1+2*!p)
{%x}_alpha0(2)= {%x}_var.c(2,1+2*!p)
for !n=1 to !p
coef(4) {%x}_alpha!n
{%x}_alpha!n(1)= {%x}_var.c(1,!n)
{%x}_alpha!n(2)= {%x}_var.c(1,!n+!p)
{%x}_alpha!n(3)= {%x}_var.c(2,!n)
{%x}_alpha!n(4)= {%x}_var.c(2,!n+!p)
next
coef(2) {%x}_beta0
{%x}_beta0(1)= {%x}_egarch1.c(1)
{%x}_beta0(2)= {%x}_egarch2.c(1)
coef(4) {%x}_beta1
{%x}_beta1(1)={%x}_egarch1.c(2)
{%x}_beta1(2)=0
{%x}_beta1(3)=0
{%x}_beta1(4)={%x}_egarch2.c(2)
coef(2) {%x}_delta
{%x}_delta(1) = {%x}_egarch1.c(3)/{%x}_egarch1.c(2)
{%x}_delta(2) = {%x}_egarch2.c(3)/{%x}_egarch2.c(2)
for !m=1 to !q
coef(2) {%x}_gamma!m
{%x}_gamma!m(1)= {%x}_egarch1.c(!m+3)
{%x}_gamma!m(2)= {%x}_egarch2.c(!m+3)
next
coef(3) {%x}_eta
{%x}_eta(1) = 0.5
{%x}_eta(2) = 0.05
{%x}_eta(3) = 0.9
' use var-cov of sample in "s0" as starting value of variance-covariance matrix
series {%x}_var_e = @var({%x}_e)
series {%x}_var_s = @var({%x}_s)
series {%x}_z_e = {%x}_e/@sqrt(@var({%x}_e))
series {%x}_z_s = {%x}_s/@sqrt(@var({%x}_s))
series {%x}_xi = -log((1-@cor({%x}_e,{%x}_s))/(1+@cor({%x}_e,{%x}_s)))
series {%x}_cor=2/(1+@exp(-{%x}_xi))-1
series {%x}_cov={%x}_cor*@sqrt(@var({%x}_e)*@var({%x}_s))
scalar E_z=@sqrt(2/@acos(-1))
' ...........................................................
' LOG LIKELIHOOD
' set up the likelihood
' 1) open a new blank likelihood object (L.O.) name bvegarch
' 2) specify the log likelihood model by append
' ...........................................................
logl {%x}_bvegarch
{%x}_bvegarch.append @logl logl
{%x}_bvegarch.append {%x}_e = E_{%x} - ({%x}_alpha0(1) + {%x}_alpha1(1)*E_{%x}(-1) + {%x}_alpha2(1)*E_{%x}(-2) + {%x}_alpha3(1)*E_{%x}(-3) + {%x}_alpha4(1)*E_{%x}(-4) + {%x}_alpha1(2)*S_{%x}(-1) + {%x}_alpha2(2)*S_{%x}(-2) + {%x}_alpha3(2)*S_{%x}(-3) + {%x}_alpha4(2)*S_{%x}(-4))
{%x}_bvegarch.append {%x}_s = S_{%x} - ({%x}_alpha0(2) + {%x}_alpha1(3)*E_{%x}(-1) + {%x}_alpha2(3)*E_{%x}(-2) + {%x}_alpha3(3)*E_{%x}(-3) + {%x}_alpha4(3)*E_{%x}(-4) + {%x}_alpha1(4)*S_{%x}(-1) + {%x}_alpha2(4)*S_{%x}(-2) + {%x}_alpha3(4)*S_{%x}(-3) + {%x}_alpha4(4)*S_{%x}(-4))
{%x}_bvegarch.append {%x}_z_e = {%x}_e/@sqrt(@var({%x}_e))
{%x}_bvegarch.append {%x}_z_s = {%x}_s/@sqrt(@var({%x}_s))
' calculate the variance and covariance series
{%x}_bvegarch.append log({%x}_var_e) = {%x}_beta0(1) + {%x}_beta1(1)*(abs({%x}_z_e(-1))-E_z+{%x}_delta(1)*{%x}_z_e(-1)) + {%x}_beta1(2)*(abs({%x}_z_s(-1))-E_z+{%x}_delta(2)*{%x}_z_s(-1)) + {%x}_gamma1(1)*log({%x}_var_e(-1)) + {%x}_gamma2(1)*log({%x}_var_e(-2))
{%x}_bvegarch.append log({%x}_var_s) = {%x}_beta0(2) + {%x}_beta1(3)*(abs({%x}_z_e(-1))-E_z+{%x}_delta(1)*{%x}_z_e(-1)) + {%x}_beta1(4)*(abs({%x}_z_s(-1))-E_z+{%x}_delta(2)*{%x}_z_s(-1)) + {%x}_gamma1(2)*log({%x}_var_s(-1)) + {%x}_gamma2(2)*log({%x}_var_s(-2))
{%x}_bvegarch.append {%x}_z_e = {%x}_e/@sqrt({%x}_var_e)
{%x}_bvegarch.append {%x}_z_s = {%x}_s/@sqrt({%x}_var_s)
{%x}_bvegarch.append {%x}_xi = {%x}_eta(1) + {%x}_eta(2)*{%x}_z_e(-1)*{%x}_z_s(-1) + {%x}_eta(3)*{%x}_xi(-1)
{%x}_bvegarch.append {%x}_cor=2/(1+@exp(-{%x}_xi))-1
{%x}_bvegarch.append {%x}_cov={%x}_cor*@sqrt({%x}_var_e*{%x}_var_s)
' determinant of the variance-covariance matrix
{%x}_bvegarch.append {%x}_deth = {%x}_var_e*{%x}_var_s - {%x}_cov^2
' log-likelihood series
{%x}_bvegarch.append logl =-0.5*(2*1*(660-!p)*log(2*@acos(-1)) + log({%x}_deth) + ({%x}_e^2*{%x}_var_s + {%x}_s^2*{%x}_var_e - 2*{%x}_cov*{%x}_e*{%x}_s)/{%x}_deth)
' estimate the model
smpl s1
{%x}_bvegarch.ml(showopts, m=500, c=1e-6)
' change below to display different output
show {%x}_bvegarch.output
graph {%x}_vare.line {%x}_var_e
graph {%x}_vars.line {%x}_var_s
graph {%x}_coves.line {%x}_cov
graph {%x}_cores.line {%x}_cor
show {%x}_vare
show {%x}_vars
show {%x}_coves
show {%x}_cores
