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The Forecasting Performance of a Finite Mixture Regime‐Switching Model for Daily Electricity Prices
Author(s) -
Chen Dipeng,
Bunn Derek
Publication year - 2014
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2297
Subject(s) - overfitting , markov chain , econometrics , asset (computer security) , electricity , sample (material) , computer science , nonlinear system , logistic regression , economics , mathematical optimization , artificial intelligence , mathematics , machine learning , engineering , artificial neural network , chemistry , electrical engineering , physics , computer security , chromatography , quantum mechanics
Forecasting prices in electricity markets is a crucial activity for both risk management and asset optimization. Intra‐day power prices have a fine structure and are driven by an interaction of fundamental, behavioural and stochastic factors. Furthermore, there are reasons to expect the functional forms of price formation to be nonlinear in these factors and therefore specifying forecasting models that perform well out‐of‐sample is methodologically challenging. Markov regime switching has been widely advocated to capture some aspects of the nonlinearity, but it may suffer from overfitting and unobservability in the underlying states. In this paper we compare several extensions and alternative regime‐switching formulations, including logistic specifications of the underlying states, logistic smooth transition and finite mixture regression. The finite mixture approach to regime switching performs well in an extensive, out‐of‐sample forecasting comparison. Copyright © 2014 John Wiley & Sons, Ltd.