6:02 p.m.
I am currently using Gretl to run GARCH model for volatility of daily
return of IBM stock estimation.
However, I can't match the result from gretl and the GARCH formula.
What I know is: ht=ω＋α（ｒ−ｍ）^2＋βσ^2
But, what I got is:
Variable Coefficient
const 0.000553614
alpha(0) 9.90527e-06
alpha(1) 0.108505
beta(1) 0.879647
Mean of dependent variable = 9.79994e-005
Standard deviation of dep. var. = 0.0244575
Unconditional error variance = 0.000836019
Log-likelihood = 2387.036
AIC = -4764.073
BIC = -4739.534
I think α(0) is ω.
But I don't know which is ht and what does const mean.
Thank you for your help.
Have a good weekend.
Manabu Ohinata
manabu0120(a)hotmail.com
6:14 p.m.
On Fri, February 17, 2006 19:02, ohinata manabu wrote:
I am currently using Gretl to run GARCH model for volatility of
daily
return of IBM stock estimation.
However, I can't match the result from gretl and the GARCH formula.
What I know is: ht=$B&X!\&A!J#r!]#m!K(B^2$B!\&B&R(B^2
But, what I got is:
Variable Coefficient
const 0.000553614
alpha(0) 9.90527e-06
alpha(1) 0.108505
beta(1) 0.879647
Mean of dependent variable = 9.79994e-005
Standard deviation of dep. var. = 0.0244575
Unconditional error variance = 0.000836019
Log-likelihood = 2387.036
AIC = -4764.073
BIC = -4739.534
I think $B&A(B(0) is $B&X(B.
But I don't know which is ht and what does const mean.
Thank you for your help.
Have a good weekend.
By default gretl estimates a model where the conditional mean is constant, so
"const" is the estimate of m in the following pair of equations:
y_t = m + e_t
V(e_t) = h_t = alpha_0 + alpha_1 * e_{t-1}^2 + \beta_1 h_{t-1}
note that V(e_t) should be read as the *conditional* variance of e_t.
In your example, the estimate for m is 0.000553614 and so on.
--
Riccardo "Jack" Lucchetti
Dipartimento di Economia
Facoltà di Economia "G. Fuà"
Ancona
7:03 p.m.
Thank you for your answer, and I want to know more.
I am sorry for my detail question,
I want to forecast the volatility for the next day.
So the equation shoud be the following:
h_{t+1} = alpha_0 + alpha_1 * e_t^2 + beta_1 * h_t
My question is how I can get h_t, even though I don't know e_{t-1} and
h_{t-1} for the h_t equation.
And I also want to know how to get e_t, then, and what is y_t? Is it the
actual volatility?
Equation you gave me.
y_t = m + e_t
V(e_t) = h_t = alpha_0 + alpha_1 * e_{t-1}^2 + beta_1 h_{t-1}
Thank you very much for your support.
Manabu Ohinata
****************************************************************************
From: "Riccardo Jack" Lucchetti""
<r.lucchetti(a)univpm.it>
Reply-To: r.lucchetti(a)univpm.it, Gretl list
<gretl-users(a)ricardo.ecn.wfu.edu>
To: "Gretl list" <gretl-users(a)ricardo.ecn.wfu.edu>
Subject: Re: [Gretl-users] Question about GARCH
Date: Fri, 17 Feb 2006 19:14:23 +0100 (CET)
On Fri, February 17, 2006 19:02, ohinata manabu wrote:
> I am currently using Gretl to run GARCH model for volatility of daily
> return of IBM stock estimation.
> However, I can't match the result from gretl and the GARCH formula.
>
> What I know is: ht=ω＋α（ｒ−ｍ）^2＋βσ^2
>
> But, what I got is:
>
> Variable Coefficient
> const 0.000553614
> alpha(0) 9.90527e-06
> alpha(1) 0.108505
> beta(1) 0.879647
> Mean of dependent variable = 9.79994e-005
> Standard deviation of dep. var. = 0.0244575
> Unconditional error variance = 0.000836019
> Log-likelihood = 2387.036
> AIC = -4764.073
> BIC = -4739.534
>
> I think α(0) is ω.
> But I don't know which is ht and what does const mean.
>
> Thank you for your help.
> Have a good weekend.
By default gretl estimates a model where the conditional mean is constant,
so
"const" is the estimate of m in the following pair of
equations:
y_t = m + e_t
V(e_t) = h_t = alpha_0 + alpha_1 * e_{t-1}^2 + \beta_1 h_{t-1}
note that V(e_t) should be read as the *conditional* variance of e_t.
In your example, the estimate for m is 0.000553614 and so on.
--
Riccardo "Jack" Lucchetti
Dipartimento di Economia
Facolt・di Economia "G. Fu・quot;
Ancona
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7:19 p.m.
On Sat, 18 Feb 2006, ohinata manabu wrote:
I want to forecast the volatility for the next day.
So the equation shoud be the following:
h_{t+1} = alpha_0 + alpha_1 * e_t^2 + beta_1 * h_t
My question is how I can get h_t, even though I don't know e_{t-1} and
h_{t-1} for the h_t equation.
You can retrieve the residuals and the conditional variance from the
GARCH model like this:
genr et = $uhat
genr ht = $h
Or if you estimate the model using the graphical interface, you can
add these variables to the dataset from the "Model data" menu in the
model window, under the item "Add to data set".
And I also want to know how to get e_t, then, and what is y_t? Is
it the actual volatility?
No, y_t is the level of the time-series in question.
Allin Cottrell
7:39 p.m.
Wow! Thank you very much!!!!
I can finish my research paper now!!!
Thank you very much.
Manabu Ohinata
7200
days inactive
7200
days old
4 comments
3 participants
participants (3)
-
Allin Cottrell -
ohinata manabu -
Riccardo Jack Lucchetti