[Gretl-users] GARCH for day-ahead electricity prices
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)
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Message: 1
Date: Wed, 30 Sep 2009 19:19:58 +0500
From: Saqib Ilyas <msaqib(a)gmail.com>
Subject: [Gretl-users] GARCH for day-ahead electricity prices
To: gretl-users(a)lists.wfu.edu
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<262b67200909300719k16ab8e46m5e63bd83bce25ddf(a)mail.gmail.com>
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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