Liebl, Dominik (2013): Modeling and Forecasting Electricity Spot Prices: A Functional Data Perspective. Published in: The Annals of Applied Statistics , Vol. 7, No. 3 (September 2013): pp. 15621592.

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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 electric ity 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 or der model, the most important microeconomic 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 pricedemand functions. In a second step, we parameter ize the series of daily pricedemand 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 se ries models as well as two alternative functional data models.
Item Type:  MPRA Paper 

Original Title:  Modeling and Forecasting Electricity Spot Prices: A Functional Data Perspective 
Language:  English 
Keywords:  Functional factor model, functional data analysis, time series analysis, fundamental market model, merit order curve, European Energy Exchange, EEX 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C14  Semiparametric and Nonparametric Methods: General C  Mathematical and Quantitative Methods > C5  Econometric Modeling 
Item ID:  50881 
Depositing User:  Dominik Liebl 
Date Deposited:  23 Oct 2013 12:35 
Last Modified:  26 Sep 2019 20:07 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/50881 