Universität zu Köln

Liebl, Dominik ORCID: 0000-0002-9282-4394 (2013). MODELING AND FORECASTING ELECTRICITY SPOT PRICES: A FUNCTIONAL DATA PERSPECTIVE. Ann. Appl. Stat., 7 (3). S. 1562 - 1593. CLEVELAND: INST MATHEMATICAL STATISTICS. ISSN 1932-6157

Full text not available from this repository.

Abstract

Classical time series models have serious difficulties in modeling and forecasting the enormous fluctuations of electricity spot prices. Markov regime switch models belong to the most often used models in the electricity literature. These models try to capture the fluctuations of electricity spot prices by using different regimes, each with its own mean and covariance structure. Usually one regime is dedicated to moderate prices and another is dedicated to high prices. However, these models show poor performance and there is no theoretical justification for this kind of classification. The merit order model, the most important micro-economic pricing model for electricity spot prices, however, suggests a continuum of mean levels with a functional dependence on electricity demand. We propose a new statistical perspective on modeling and forecasting electricity spot prices that accounts for the merit order model. In a first step, the functional relation between electricity spot prices and electricity demand is modeled by daily price-demand functions. In a second step, we parameterize the series of daily price-demand functions using a functional factor model. The power of this new perspective is demonstrated by a forecast study that compares our functional factor model with two established classical time series models as well as two alternative functional data models.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Liebl, Dominik UNSPECIFIED orcid.org/0000-0002-9282-4394 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-476291
DOI:	10.1214/13-AOAS652
Journal or Publication Title:	Ann. Appl. Stat.
Volume:	7
Number:	3
Page Range:	S. 1562 - 1593
Date:	2013
Publisher:	INST MATHEMATICAL STATISTICS
Place of Publication:	CLEVELAND
ISSN:	1932-6157
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language PRINCIPAL COMPONENT ANALYSIS; NONPARAMETRIC REGRESSION; PREDICTION Multiple languages Statistics & Probability Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/47629