To better understand your model, could you give us an idea of the
independent variable you are using to predict NEPOOL prices? Typical
independent variables include day of week/holiday dummy variables,
natural gas price, heating oil price, weather variables, etc..

Thanks


- Mike

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Today's Topics:

  1. GARCH for day-ahead electricity prices (Saqib Ilyas)


----------------------------------------------------------------------

Message: 1
Date: Wed, 30 Sep 2009 19:19:58 +0500
From: Saqib Ilyas <msaqib@gmail.com>
Subject: [Gretl-users] GARCH for day-ahead electricity prices
To: gretl-users@lists.wfu.edu
Message-ID:
       <262b67200909300719k16ab8e46m5e63bd83bce25ddf@mail.gmail.com>
Content-Type: text/plain; charset="iso-8859-1"

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
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