2:19 p.m.
Hi all
I am using the 2006 till 2009 data for the New England pool of day-ahead
weighted average prices. I am not very fluent with econometrics, but I read
in some papers that GARCH has been used successfully to forecast electricity
wholesale prices. When I train a GARCH model on one year worth of data, and
forecast for the last 3 days of training data, I get a mean absolute error
of 3.6642%. The error increases to 27.826% when I use two years worth of
data, and decreases to 17.123% when using three years worth of past data. Is
this expected?
Also, when I plot the actual and fitted data against time, the GARCH model
seems to have done a really bad job, compared to a default ARIMA model. I'm
guessing this might be because people are actually using ARIMA models with
(added) GARCH errors, so a simple GARCH model-based forecast isn't doing
exactly what they have done. Am I right? Why would the ARIMA be a better fit
than GARCH?
One author mentioned that they took a log of the prices (in their case it
was hourly prices) before fitting a GARCH model. In your opinion, is that an
important factor in the kind of errors I am getting?
Thanks and best regards
--
Muhammad Saqib Ilyas
PhD Student, Computer Science and Engineering
Lahore University of Management Sciences
5:16 a.m.
Hi all,
I tried sending this email last night, but I don't think it has gotten
through. Perhaps my membership hadn't been processed as yet.
I am using the 2006 till 2009 data for the New England pool of day-ahead
weighted average prices. I am not very fluent with econometrics, but I read
in some papers that GARCH has been used successfully to forecast electricity
wholesale prices. When I train a GARCH model on one year worth of data, and
forecast for the last 3 days of training data, I get a mean absolute error
of 3.6642%. The error increases to 27.826% when I use two years worth of
data, and decreases to 17.123% when using three years worth of past data. Is
this expected?
Also, when I plot the actual and fitted data against time, the GARCH model
seems to have done a really bad job, compared to a default ARIMA model. I'm
guessing this might be because people are actually using ARIMA models with
(added) GARCH errors, so a simple GARCH model-based forecast isn't doing
exactly what they have done. Am I right? Why would the ARIMA be a better fit
than GARCH?
One author mentioned that they took a log of the prices (in their case it
was hourly prices) before fitting a GARCH model. In your opinion, is that an
important factor in the kind of errors I am getting?
Thanks and best regards
--
Muhammad Saqib Ilyas
PhD Student, Computer Science and Engineering
Lahore University of Management Sciences
11:49 a.m.
An ARIMA model is basically a model of the level of the process. A
GARCH model is a model of the volatility of the process. There can of
course be interactions between the volatility and the level (e.g.
Garch in mean process). A complete answer to your questions would
take an email equivalent to several chapters in a book. One uses the
log of a variable when one sees the shock to the process as being
multiplicative. I would think that the shock process in
multiplicative for most price processes.
Might I recommend a text such as Enders or similar as an introduction
to time series analysis.
Best Regards
John
2009/10/1 Saqib Ilyas <msaqib(a)gmail.com>:
Hi all,
I tried sending this email last night, but I don't think it has gotten
through. Perhaps my membership hadn't been processed as yet.
I am using the 2006 till 2009 data for the New England pool of day-ahead
weighted average prices. I am not very fluent with econometrics, but I read
in some papers that GARCH has been used successfully to forecast electricity
wholesale prices. When I train a GARCH model on one year worth of data, and
forecast for the last 3 days of training data, I get a mean absolute error
of 3.6642%. The error increases to 27.826% when I use two years worth of
data, and decreases to 17.123% when using three years worth of past data. Is
this expected?
Also, when I plot the actual and fitted data against time, the GARCH model
seems to have done a really bad job, compared to a default ARIMA model. I'm
guessing this might be because people are actually using ARIMA models with
(added) GARCH errors, so a simple GARCH model-based forecast isn't doing
exactly what they have done. Am I right? Why would the ARIMA be a better fit
than GARCH?
One author mentioned that they took a log of the prices (in their case it
was hourly prices) before fitting a GARCH model. In your opinion, is that an
important factor in the kind of errors I am getting?
Thanks and best regards
--
Muhammad Saqib Ilyas
PhD Student, Computer Science and Engineering
Lahore University of Management Sciences
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Gretl-users(a)lists.wfu.edu
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--
John C Frain
Economics Department
Trinity College Dublin
Dublin 2
Ireland
www.tcd.ie/Economics/staff/frainj/home.html
mailto:frainj@tcd.ie
mailto:frainj@gmail.com
12:20 p.m.
Awesome! Thanks. Is that the reason why I seem to have seen people ask about
adding an ARMA process and a GARCH process?
I'll surely look at that text.
On Thu, Oct 1, 2009 at 4:49 PM, John C Frain <frainj(a)gmail.com> wrote:
An ARIMA model is basically a model of the level of the process. A
GARCH model is a model of the volatility of the process. There can of
course be interactions between the volatility and the level (e.g.
Garch in mean process). A complete answer to your questions would
take an email equivalent to several chapters in a book. One uses the
log of a variable when one sees the shock to the process as being
multiplicative. I would think that the shock process in
multiplicative for most price processes.
Might I recommend a text such as Enders or similar as an introduction
to time series analysis.
Best Regards
John
2009/10/1 Saqib Ilyas <msaqib(a)gmail.com>:
> Hi all,
> I tried sending this email last night, but I don't think it has gotten
> through. Perhaps my membership hadn't been processed as yet.
>
> I am using the 2006 till 2009 data for the New England pool of day-ahead
> weighted average prices. I am not very fluent with econometrics, but I
read
> in some papers that GARCH has been used successfully to forecast
electricity
> wholesale prices. When I train a GARCH model on one year worth of data,
and
> forecast for the last 3 days of training data, I get a mean absolute
error
> of 3.6642%. The error increases to 27.826% when I use two years worth of
> data, and decreases to 17.123% when using three years worth of past data.
Is
> this expected?
>
> Also, when I plot the actual and fitted data against time, the GARCH
model
> seems to have done a really bad job, compared to a default ARIMA model.
I'm
> guessing this might be because people are actually using ARIMA models
with
> (added) GARCH errors, so a simple GARCH model-based forecast isn't doing
> exactly what they have done. Am I right? Why would the ARIMA be a better
fit
> than GARCH?
>
> One author mentioned that they took a log of the prices (in their case it
> was hourly prices) before fitting a GARCH model. In your opinion, is that
an
> important factor in the kind of errors I am getting?
> Thanks and best regards
> --
> Muhammad Saqib Ilyas
> PhD Student, Computer Science and Engineering
> Lahore University of Management Sciences
> _______________________________________________
> Gretl-users mailing list
> Gretl-users(a)lists.wfu.edu
> http://lists.wfu.edu/mailman/listinfo/gretl-users
>
--
John C Frain
Economics Department
Trinity College Dublin
Dublin 2
Ireland
www.tcd.ie/Economics/staff/frainj/home.html
mailto:frainj@tcd.ie
mailto:frainj@gmail.com
_______________________________________________
Gretl-users mailing list
Gretl-users(a)lists.wfu.edu
http://lists.wfu.edu/mailman/listinfo/gretl-users
--
Muhammad Saqib Ilyas
PhD Student, Computer Science and Engineering
Lahore University of Management Sciences
5314
days inactive
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days old
3 comments
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